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Title: Relating First-person and Third-person Vision.
Creator: Ardeshir Behrostaghi, Shervin, Borji, Ali, Shah, Mubarak, Hu, Haiyan, Atia, George, University of Central Florida
Abstract / Description: Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person (egocentric) perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In many computer vision tasks (e.g. identification, action recognition, face recognition, pose estimation, etc.),...
Show moreThanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person (egocentric) perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In many computer vision tasks (e.g. identification, action recognition, face recognition, pose estimation, etc.), the human actors are the main focus. Hence, detecting, localizing, and recognizing the human actor is often incorporated as a vital component. In an egocentric video however, the person behind the camera is often the person of interest. This would change the nature of the task at hand, given that the camera holder is usually not visible in the content of his/her egocentric video. In other words, our knowledge about the visual appearance, pose, etc. on the egocentric camera holder is very limited, suggesting reliance on other cues in first person videos. First and third person videos have been separately studied in the past in the computer vision community. However, the relationship between first and third person vision has yet to be fully explored. Relating these two views systematically could potentially benefit many computer vision tasks and applications. This thesis studies this relationship in several aspects. We explore supervised and unsupervised approaches for relating these two views seeking different objectives such as identification, temporal alignment, and action classification. We believe that this exploration could lead to a better understanding the relationship of these two drastically different sources of information.
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Date Issued: 2018
Identifier: CFE0007151, ucf:52322
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007151

Title: Practical Issues in GPRAM Development.
Creator: Li, Yin, Wei, Lei, Wu, Xinzhang, Mikhael, Wasfy, University of Central Florida
Abstract / Description: In this thesis, two parts of practical issues in the GPRAM system design are included. The first part is the coding part. The sum-product decoding algorithm of LDPC codes has been refined to fit for the GPRAM hardware implementation. As we all know, communication channel has noise. The noise in telecom system is different from that in GPRAM systems. So the noise should be handled well in the GPRAM design. A noise look-up table was created for FPGA and those noises in the table are quantized....
Show moreIn this thesis, two parts of practical issues in the GPRAM system design are included. The first part is the coding part. The sum-product decoding algorithm of LDPC codes has been refined to fit for the GPRAM hardware implementation. As we all know, communication channel has noise. The noise in telecom system is different from that in GPRAM systems. So the noise should be handled well in the GPRAM design. A noise look-up table was created for FPGA and those noises in the table are quantized. The second part of the thesis is to convert perfect images in video stream to those similar to the coarse images in human vision. GPRAM is an animal like robot in which coarse images are needed more than the fine images in order for us to understand how to GPRAM progresses those images to generate as clear image as we experienced. We use three steps, Point Spread function, inserting Poisson Noise, and introducing Eye fixation movements to mimic the coarse images seen merely from our eyes at the retinal photo-receptor level, i.e., without any brain processing.
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Date Issued: 2014
Identifier: CFE0005200, ucf:50632
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005200

Title: OBJECT ASSOCIATION ACROSS MULTIPLE MOVING CAMERAS IN PLANAR SCENES.
Creator: Sheikh, Yaser, Shah, Mubarak, University of Central Florida
Abstract / Description: In this dissertation, we address the problem of object detection and object association across multiple cameras over large areas that are well modeled by planes. We present a unifying probabilistic framework that captures the underlying geometry of planar scenes, and present algorithms to estimate geometric relationships between different cameras, which are subsequently used for co-operative association of objects. We first present a local1 object detection scheme that has three fundamental...
Show moreIn this dissertation, we address the problem of object detection and object association across multiple cameras over large areas that are well modeled by planes. We present a unifying probabilistic framework that captures the underlying geometry of planar scenes, and present algorithms to estimate geometric relationships between different cameras, which are subsequently used for co-operative association of objects. We first present a local1 object detection scheme that has three fundamental innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic scene behavior, nominal misalignments and motion due to parallax. By using a non-parametric density estimation method over a joint domain-range representation of image pixels, complex dependencies between the domain (location) and range (color) are directly modeled. We present a model of the background as a single probability density. Second, temporal persistence is introduced as a detection criterion. Unlike previous approaches to object detection that detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking), since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the method is performed and presented on a diverse set of data. We then address the problem of associating objects across multiple cameras in planar scenes. Since cameras may be moving, there is a possibility of both spatial and temporal non-overlap in the fields of view of the camera. We first address the case where spatial and temporal overlap can be assumed. Since the cameras are moving and often widely separated, direct appearance-based or proximity-based constraints cannot be used. Instead, we exploit geometric constraints on the relationship between the motion of each object across cameras, to test multiple correspondence hypotheses, without assuming any prior calibration information. Here, there are three contributions. First, we present a statistically and geometrically meaningful means of evaluating a hypothesized correspondence between multiple objects in multiple cameras. Second, since multiple cameras exist, ensuring coherency in association, i.e. transitive closure is maintained between more than two cameras, is an essential requirement. To ensure such coherency we pose the problem of object associating across cameras as a k-dimensional matching and use an approximation to find the association. We show that, under appropriate conditions, re-entering objects can also be re-associated to their original labels. Third, we show that as a result of associating objects across the cameras, a concurrent visualization of multiple aerial video streams is possible. Results are shown on a number of real and controlled scenarios with multiple objects observed by multiple cameras, validating our qualitative models. Finally, we present a unifying framework for object association across multiple cameras and for estimating inter-camera homographies between (spatially and temporally) overlapping and non-overlapping cameras, whether they are moving or non-moving. By making use of explicit polynomial models for the kinematics of objects, we present algorithms to estimate inter-frame homographies. Under an appropriate measurement noise model, an EM algorithm is applied for the maximum likelihood estimation of the inter-camera homographies and kinematic parameters. Rather than fit curves locally (in each camera) and match them across views, we present an approach that simultaneously refines the estimates of inter-camera homographies and curve coefficients globally. We demonstrate the efficacy of the approach on a number of real sequences taken from aerial cameras, and report quantitative performance during simulations.
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Date Issued: 2006
Identifier: CFE0001045, ucf:46797
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001045

Title: DETECTING CURVED OBJECTS AGAINST CLUTTERED BACKGROUNDS.
Creator: Prokaj, Jan, Lobo, Niels, University of Central Florida
Abstract / Description: Detecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an...
Show moreDetecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-level features into weak classifiers, which then produce a strong classifier using AdaBoost. The resulting strong classifier is then tested on the problem of detecting heads with shoulders. On a database of over 500 images of people, cropped to contain head and shoulders, and with a diverse set of backgrounds, the detection rate is 90% while the false positive rate on a database of 500 negative images is less than 2%.
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Date Issued: 2008
Identifier: CFE0002102, ucf:47535
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002102

Title: Oculomotor Mechanisms Underlying Attentional Costs In Distracted Visual Search.
Creator: Lewis, Joanna, Neider, Mark, Szalma, James, Schmidt, Joseph, Joseph, Dana, University of Central Florida
Abstract / Description: Performance consequences have been long established when humans multitask. This research concerns the impact of distraction on the attentional shifts during a task that underlies many cognitive processes and everyday tasks, searching for a target item among non-target items (e.g., scanning the road for potential collisions). There is evidence that increasing the mental workload by introducing additional tasks influences our ability to search our environment or interferes with processing...
Show morePerformance consequences have been long established when humans multitask. This research concerns the impact of distraction on the attentional shifts during a task that underlies many cognitive processes and everyday tasks, searching for a target item among non-target items (e.g., scanning the road for potential collisions). There is evidence that increasing the mental workload by introducing additional tasks influences our ability to search our environment or interferes with processing fixated information. In the current studies, I aimed to evaluate the changes in gaze behaviors during visual search to evaluate how multitasking impairs our attentional processes. Participants completed a visual search task (search for a target T among distractor L's) while wearing a heads-up display (Google Glass) which displays an unrelated word during the dual task condition, while the control condition required participants to complete the search task without distraction. The changes in oculomotor behavior were observed in four experiments: (1) evaluating general oculomotor behavior during distraction, (2) masking the display onset of the secondary information during an eye movement to reduce the possibility of distraction from the word appearing, (3) removing any occlusion of stimuli from the heads-up display by having no visual overlap of the two tasks, and (4) evaluating whether oculomotor behaviors were similar to previous results when the nature of the distracting task changes in sensory modality. Participants typically took longer to respond when distracted, except for when the word onset was masked and the word was present auditorily. Oculomotor results indicated an increase in fixation durations (occasionally for the initial saccade latency as well) and a reduction of target fixations when participants were distracted by secondary information. These results suggest that secondary visual information can impact how humans search their environment in a fashion which increases their time to respond and impacts selective visual processing.
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Date Issued: 2018
Identifier: CFE0007039, ucf:51993
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007039

Title: Gradient based MRF learning for image restoration and segmentation.
Creator: Samuel, Kegan, Tappen, Marshall, Da Vitoria Lobo, Niels, Foroosh, Hassan, Li, Xin, University of Central Florida
Abstract / Description: The undirected graphical model or Markov Random Field (MRF) is one of the more popular models used in computer vision and is the type of model with which this work is concerned. Models based on these methods have proven to be particularly useful in low-level vision systems and have led to state-of-the-art results for MRF-based systems. The research presented will describe a new discriminative training algorithm and its implementation.The MRF model will be trained by optimizing its parameters...
Show moreThe undirected graphical model or Markov Random Field (MRF) is one of the more popular models used in computer vision and is the type of model with which this work is concerned. Models based on these methods have proven to be particularly useful in low-level vision systems and have led to state-of-the-art results for MRF-based systems. The research presented will describe a new discriminative training algorithm and its implementation.The MRF model will be trained by optimizing its parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth. While previous work has relied on time-consuming iterative approximations or stochastic approximations, this work will demonstrate how implicit differentiation can be used to analytically differentiate the overall training loss with respect to the MRF parameters. This framework leads to an efficient, flexible learning algorithm that can be applied to a number of different models.The effectiveness of the proposed learning method will then be demonstrated by learning the parameters of two related models applied to the task of denoising images. The experimental results will demonstrate that the proposed learning algorithm is comparable and, at times, better than previous training methods applied to the same tasks.A new segmentation model will also be introduced and trained using the proposed learning method. The proposed segmentation model is based on an energy minimization framework that is novel in how it incorporates priors on the size of the segments in a way that is straightforward to implement. While other methods, such as normalized cuts, tend to produce segmentations of similar sizes, this method is able to overcome that problem and produce more realistic segmentations.
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Date Issued: 2012
Identifier: CFE0004595, ucf:49207
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004595

Title: SPATIO-TEMPORAL MAXIMUM AVERAGE CORRELATION HEIGHT TEMPLATES IN ACTION RECOGNITION AND VIDEO SUMMARIZATION.
Creator: Rodriguez, Mikel, Shah, Mubarak, University of Central Florida
Abstract / Description: Action recognition represents one of the most difficult problems in computer vision given that it embodies the combination of several uncertain attributes, such as the subtle variability associated with individual human behavior and the challenges that come with viewpoint variations, scale changes and different temporal extents. Nevertheless, action recognition solutions are critical in a great number of domains, such video surveillance, assisted living environments, video search, interfaces,...
Show moreAction recognition represents one of the most difficult problems in computer vision given that it embodies the combination of several uncertain attributes, such as the subtle variability associated with individual human behavior and the challenges that come with viewpoint variations, scale changes and different temporal extents. Nevertheless, action recognition solutions are critical in a great number of domains, such video surveillance, assisted living environments, video search, interfaces, and virtual reality. In this dissertation, we investigate template-based action recognition algorithms that can incorporate the information contained in a set of training examples, and we explore how these algorithms perform in action recognition and video summarization. First, we introduce a template-based method for recognizing human actions called Action MACH. Our approach is based on a Maximum Average Correlation Height (MACH) filter. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Next, we address three seldom explored challenges in template-based action recognition. The first is the recognition and localization of human actions in aerial videos obtained from unmanned aerial vehicles (UAVs), a new medium which presents unique challenges due to the small number of pixels per human, pose, and moving camera. The second issue we address is the incorporation of multiple positive and negative examples of a target action class when generating an action template. We address this issue by employing the Fukunaga-Koontz Transform as a means of generating a single quadratic template which, unlike traditional temporal templates (which rely on positive examples alone), effectively captures the variability associated with an action class by including both positive and negative examples in the template training process. Third, we explore the problem of generating video summaries that include specific actions of interest as opposed to all moving objects. In doing so, we explore the role of action templates in video summarization in an effort to provide a means of generating a compact video representation based on a set of activities of interest. We introduce an approach in which a user specifies the activities that interest him and the video is automatically condensed to a short clip which captures the most relevant events based on the user's preference. We follow the output summary video format of non-chronological video synopsis approaches, in which different events which occur at different times may be displayed concurrently, even though they never occur simultaneously in the original video. However, instead of assuming that all moving objects are interesting, priority is given to specific activities of interest which pertain to a user's query. This provides an efficient means of browsing through large collections of video for events of interest.
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Date Issued: 2010
Identifier: CFE0003313, ucf:48507
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003313

Title: Adversarial Attacks On Vision Algorithms Using Deep Learning Features.
Creator: Michel, Andy, Jha, Sumit Kumar, Leavens, Gary, Valliyil Thankachan, Sharma, University of Central Florida
Abstract / Description: Computer vision algorithms, such as those implementing object detection, are known to be sus-ceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause visionalgorithms to incorrectly classify inputs that they would have otherwise classified correctly. Anumber of approaches have been recently investigated to generate such adversarial examples fordeep neural networks. Many of these approaches either require grey-box access to the deep neuralnet being...
Show moreComputer vision algorithms, such as those implementing object detection, are known to be sus-ceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause visionalgorithms to incorrectly classify inputs that they would have otherwise classified correctly. Anumber of approaches have been recently investigated to generate such adversarial examples fordeep neural networks. Many of these approaches either require grey-box access to the deep neuralnet being attacked or rely on adversarial transfer and grey-box access to a surrogate neural network.In this thesis, we present an approach to the synthesis of adversarial examples for computer vi-sion algorithms that only requires black-box access to the algorithm being attacked. Our attackapproach employs fuzzing with features derived from the layers of a convolutional neural networktrained on adversarial examples from an unrelated dataset. Based on our experimental results,we believe that our validation approach will enable designers of cyber-physical systems and otherhigh-assurance use-cases of vision algorithms to stress test their implementations.
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Date Issued: 2017
Identifier: CFE0006898, ucf:51714
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006898

Title: Learning robotic manipulation from user demonstrations.
Creator: Rahmatizadeh, Rouhollah, Boloni, Ladislau, Turgut, Damla, Jha, Sumit Kumar, University of Central Florida
Abstract / Description: Personal robots that help disabled or elderly people in their activities of daily living need to be able to autonomously perform complex manipulation tasks. Traditional approaches to this problem employ task-specific controllers. However, these must to be designed by expert programmers, are focused on a single task, and will perform the task as programmed, not according to the preferences of the user. In this dissertation, we investigate methods that enable an assistive robot to learn to...
Show morePersonal robots that help disabled or elderly people in their activities of daily living need to be able to autonomously perform complex manipulation tasks. Traditional approaches to this problem employ task-specific controllers. However, these must to be designed by expert programmers, are focused on a single task, and will perform the task as programmed, not according to the preferences of the user. In this dissertation, we investigate methods that enable an assistive robot to learn to execute tasks as demonstrated by the user. First, we describe a learning from demonstration (LfD) method that learns assistive tasks that need to be adapted to the position and orientation of the user's head. Then we discuss a recurrent neural network controller that learns to generate movement trajectories for the end-effector of the robot arm to accomplish a task. The input to this controller is the pose of related objects and the current pose of the end-effector itself. Next, we discuss how to extract user preferences from the demonstration using reinforcement learning. Finally, we extend this controller to one that learns to observe images of the environment and generate joint movements for the robot to accomplish a desired task. We discuss several techniques that improve the performance of the controller and reduce the number of required demonstrations. One of this is multi-task learning: learning multiple tasks simultaneously with the same neural network. Another technique is to make the controller output one joint at a time-step, therefore to condition the prediction of each joint on the previous joints. We evaluate these controllers on a set of manipulation tasks and show that they can learn complex tasks, overcome failure, and attempt a task several times until they succeed.
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Date Issued: 2017
Identifier: CFE0006908, ucf:51686
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006908

Title: Human Detection, Tracking and Segmentation in Surveillance Video.
Creator: Shu, Guang, Shah, Mubarak, Boloni, Ladislau, Wang, Jun, Lin, Mingjie, Sugaya, Kiminobu, University of Central Florida
Abstract / Description: This dissertation addresses the problem of human detection and tracking in surveillance videos. Even though this is a well-explored topic, many challenges remain when confronted with data from real world situations. These challenges include appearance variation, illumination changes, camera motion, cluttered scenes and occlusion. In this dissertation several novel methods for improving on the current state of human detection and tracking based on learning scene-specific information in video...
Show moreThis dissertation addresses the problem of human detection and tracking in surveillance videos. Even though this is a well-explored topic, many challenges remain when confronted with data from real world situations. These challenges include appearance variation, illumination changes, camera motion, cluttered scenes and occlusion. In this dissertation several novel methods for improving on the current state of human detection and tracking based on learning scene-specific information in video feeds are proposed.Firstly, we propose a novel method for human detection which employs unsupervised learning and superpixel segmentation. The performance of generic human detectors is usually degraded in unconstrained video environments due to varying lighting conditions, backgrounds and camera viewpoints. To handle this problem, we employ an unsupervised learning framework that improves the detection performance of a generic detector when it is applied to a particular video. In our approach, a generic DPM human detector is employed to collect initial detection examples. These examples are segmented into superpixels and then represented using Bag-of-Words (BoW) framework. The superpixel-based BoW feature encodes useful color features of the scene, which provides additional information. Finally a new scene-specific classifier is trained using the BoW features extracted from the new examples. Compared to previous work, our method learns scene-specific information through superpixel-based features, hence it can avoid many false detections typically obtained by a generic detector. We are able to demonstrate a significant improvement in the performance of the state-of-the-art detector.Given robust human detection, we propose a robust multiple-human tracking framework using a part-based model. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. We address such problems by developing an online-learning tracking-by-detection method. Our approach learns part-based person-specific Support Vector Machine (SVM) classifiers which capture articulations of moving human bodies with dynamically changing backgrounds. With the part-based model, our approach is able to handle partial occlusions in both the detection and the tracking stages. In the detection stage, we select the subset of parts which maximizes the probability of detection. This leads to a significant improvement in detection performance in cluttered scenes. In the tracking stage, we dynamically handle occlusions by distributing the score of the learned person classifier among its corresponding parts, which allows us to detect and predict partial occlusions and prevent the performance of the classifiers from being degraded. Extensive experiments using the proposed method on several challenging sequences demonstrate state-of-the-art performance in multiple-people tracking.Next, in order to obtain precise boundaries of humans, we propose a novel method for multiple human segmentation in videos by incorporating human detection and part-based detection potential into a multi-frame optimization framework. In the first stage, after obtaining the superpixel segmentation for each detection window, we separate superpixels corresponding to a human and background by minimizing an energy function using Conditional Random Field (CRF). We use the part detection potentials from the DPM detector, which provides useful information for human shape. In the second stage, the spatio-temporal constraints of the video is leveraged to build a tracklet-based Gaussian Mixture Model for each person, and the boundaries are smoothed by multi-frame graph optimization. Compared to previous work, our method could automatically segment multiple people in videos with accurate boundaries, and it is robust to camera motion. Experimental results show that our method achieves better segmentation performance than previous methods in terms of segmentation accuracy on several challenging video sequences.Most of the work in Computer Vision deals with point solution; a specific algorithm for a specific problem. However, putting different algorithms into one real world integrated system is a big challenge. Finally, we introduce an efficient tracking system, NONA, for high-definition surveillance video. We implement the system using a multi-threaded architecture (Intel Threading Building Blocks (TBB)), which executes video ingestion, tracking, and video output in parallel. To improve tracking accuracy without sacrificing efficiency, we employ several useful techniques. Adaptive Template Scaling is used to handle the scale change due to objects moving towards a camera. Incremental Searching and Local Frame Differencing are used to resolve challenging issues such as scale change, occlusion and cluttered backgrounds. We tested our tracking system on a high-definition video dataset and achieved acceptable tracking accuracy while maintaining real-time performance.
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Date Issued: 2014
Identifier: CFE0005551, ucf:50278
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005551

Title: EDUCATIONAL VISION IN FLORIDA SCHOOL DISTRICTS: VISION ALIGNMENT AND LEADERSHIP STYLE.
Creator: Sikkenga, Cindy, House, Jess, University of Central Florida
Abstract / Description: The purpose of this study was to address a gap in the organizational leadership research related to the sharing, or alignment, of leadership vision across organizational levels, with a focus on educational vision alignment in Florida K-12 public school districts. The study also sought to determine to what extent, if any, there were differences among Florida school districts exhibiting different levels of educational vision alignment. The broad question addressed by the current research was...
Show moreThe purpose of this study was to address a gap in the organizational leadership research related to the sharing, or alignment, of leadership vision across organizational levels, with a focus on educational vision alignment in Florida K-12 public school districts. The study also sought to determine to what extent, if any, there were differences among Florida school districts exhibiting different levels of educational vision alignment. The broad question addressed by the current research was this: To what degree are the educational visions of superintendents and principals aligned within Florida K-12 public school districts? The following research questions further guided the study: 1. What common themes can be found in the published vision statements of the 67 Florida K-12 public school districts? 2. To what extent, if any, do Florida K-12 public school district superintendents and their respective principals agree with one another on the importance of the common themes found in Florida school districts' published vision statements? 3. What is the relationship, if any, between educational vision alignment levels in Florida K-12 public school districts and principals' perceptions of their superintendents' leadership styles? 4. To what extent, if any, are there differences among Florida K-12 public school districts exhibiting different levels of educational vision alignment? The Florida Educational Vision Questionnaire Superintendent Form (FEVQ-S), a researcher developed questionnaire, was administered to all 67 Florida K-12 public school district superintendents. With superintendent approval, two additional questionnaires were administered to a sample of 242 principals in 23 school districts. The Florida Educational Vision Questionnaire Principal Form (FEVQ-P) and the Multifactor Leadership Questionnaire Form 5X Rater (MLQ-5X) (Avolio, Bass, & Jung, 1999) were returned fully completed by 105 principals in 21 districts. A total of 81 principal responses in 20 districts were usable, yielding overall usable response rates of 29.9% (superintendents) and 33.5% (principals). Comparisons of FEVQ responses of superintendents and principals in each school district were made using a researcher developed measure, the Educational Vision Alignment Index (EVAI). Within each district, the EVAI was compared with the superintendent's leadership style as measured by the principals' responses to the MLQ-5X. School districts were then compared using data obtained from the FEVQ demographic items, the Florida School Indicators Report (FSIR) (FLDOE, 2003a), the 2004 School Grades by District Report (FLDOE, n.d.), and the online Florida Public School Superintendents report (FLDOE, 2005c). The FSIR contains data on district characteristics such as operating costs, per pupil expenditures, school staff composition, student membership, student mobility rates, student stability rates, and teacher descriptors. The 2004 School Grades by District report contains both the school grades for each district and the total number of schools per district. The Florida Public School Superintendents report contains general school district information and superintendent status (i.e., elected or appointed) information. Detailed data analyses related to each of the four research questions indicated that: 1. Several common themes can be found in the published vision statements of the 67 Florida K-12 public school districts, 2. Florida K-12 public school district superintendents and their respective principals agree with one another on the importance of some of these common themes, 3. Several relationships exist between the educational vision alignment levels in Florida K-12 public school districts and principals' perceptions of their superintendents' leadership styles, and 4. There are differences among Florida K-12 public school districts exhibiting different levels of educational vision alignment. The current study illustrated that in Florida K-12 public school districts whose superintendents were perceived to be transformational leaders, a strong alignment of educational vision between the superintendents and their principals was also apparent, particularly in those districts having elected superintendents. Using the two researcher developed tools, the Florida Educational Vision Questionnaire (FEVQ) and the Educational Vision Alignment Index (EVAI), it was shown that this alignment pertained to specific content items, or themes, derived from an analysis of the educational vision statements of the 67 Florida school districts. These results indicate that the current emphasis in Florida on the development of transformational leaders who are knowledgeable in techniques for developing and communicating shared visions is therefore warranted.
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Date Issued: 2006
Identifier: CFE0001349, ucf:46995
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001349

Title: MULTI-VIEW APPROACHES TO TRACKING, 3D RECONSTRUCTION AND OBJECT CLASS DETECTION.
Creator: khan, saad, Shah, Mubarak, University of Central Florida
Abstract / Description: Multi-camera systems are becoming ubiquitous and have found application in a variety of domains including surveillance, immersive visualization, sports entertainment and movie special effects amongst others. From a computer vision perspective, the challenging task is how to most efficiently fuse information from multiple views in the absence of detailed calibration information and a minimum of human intervention. This thesis presents a new approach to fuse foreground likelihood information...
Show moreMulti-camera systems are becoming ubiquitous and have found application in a variety of domains including surveillance, immersive visualization, sports entertainment and movie special effects amongst others. From a computer vision perspective, the challenging task is how to most efficiently fuse information from multiple views in the absence of detailed calibration information and a minimum of human intervention. This thesis presents a new approach to fuse foreground likelihood information from multiple views onto a reference view without explicit processing in 3D space, thereby circumventing the need for complete calibration. Our approach uses a homographic occupancy constraint (HOC), which states that if a foreground pixel has a piercing point that is occupied by foreground object, then the pixel warps to foreground regions in every view under homographies induced by the reference plane, in effect using cameras as occupancy detectors. Using the HOC we are able to resolve occlusions and robustly determine ground plane localizations of the people in the scene. To find tracks we obtain ground localizations over a window of frames and stack them creating a space time volume. Regions belonging to the same person form contiguous spatio-temporal tracks that are clustered using a graph cuts segmentation approach. Second, we demonstrate that the HOC is equivalent to performing visual hull intersection in the image-plane, resulting in a cross-sectional slice of the object. The process is extended to multiple planes parallel to the reference plane in the framework of plane to plane homologies. Slices from multiple planes are accumulated and the 3D structure of the object is segmented out. Unlike other visual hull based approaches that use 3D constructs like visual cones, voxels or polygonal meshes requiring calibrated views, ours is purely-image based and uses only 2D constructs i.e. planar homographies between views. This feature also renders it conducive to graphics hardware acceleration. The current GPU implementation of our approach is capable of fusing 60 views (480x720 pixels) at the rate of 50 slices/second. We then present an extension of this approach to reconstructing non-rigid articulated objects from monocular video sequences. The basic premise is that due to motion of the object, scene occupancies are blurred out with non-occupancies in a manner analogous to motion blurred imagery. Using our HOC and a novel construct: the temporal occupancy point (TOP), we are able to fuse multiple views of non-rigid objects obtained from a monocular video sequence. The result is a set of blurred scene occupancy images in the corresponding views, where the values at each pixel correspond to the fraction of total time duration that the pixel observed an occupied scene location. We then use a motion de-blurring approach to de-blur the occupancy images and obtain the 3D structure of the non-rigid object. In the final part of this thesis, we present an object class detection method employing 3D models of rigid objects constructed using the above 3D reconstruction approach. Instead of using a complicated mechanism for relating multiple 2D training views, our approach establishes spatial connections between these views by mapping them directly to the surface of a 3D model. To generalize the model for object class detection, features from supplemental views (obtained from Google Image search) are also considered. Given a 2D test image, correspondences between the 3D feature model and the testing view are identified by matching the detected features. Based on the 3D locations of the corresponding features, several hypotheses of viewing planes can be made. The one with the highest confidence is then used to detect the object using feature location matching. Performance of the proposed method has been evaluated by using the PASCAL VOC challenge dataset and promising results are demonstrated.
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Date Issued: 2008
Identifier: CFE0002073, ucf:47593
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002073

Title: Navigation of an Autonomous Differential Drive Robot for Field Scouting in Semi-structured Environments.
Creator: Freese, Douglas, Xu, Yunjun, Lin, Kuo-Chi, Kauffman, Jeffrey L., Behal, Aman, University of Central Florida
Abstract / Description: In recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary...
Show moreIn recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary encoding, time of flight as well as vision. To compensate for anticipated disturbances, variances and constraints contingent to the outdoor semi-structured environment, a differential style drive vehicle will be implemented as an easily controllable system to conduct tasks such as imaging and sampling.In order to verify the motion control of a robot, custom-designed for strawberry fields, the task is separated into multiple phases to manage the over-bed and cross-bed operation needs. In particular, during the cross-bed segment an elevated strawberry bed will provide distance references utilized in a logic filter and tuned PID algorithm for safe and efficient travel. Due to the significant sources of uncertainty such as wheel slip and the vehicle model, nonlinear robust controllers are designed for the cross-bed motion, purely relying on vision feedback. A simple image filter algorithm was developed for strawberry row detection, in which pixels corresponding to the bed center will be tracked while the vehicle is in controlled motion. This incorporated derivation and formulation of a bounded uncertainty parameter that will be employed in the nonlinear control. Simulation of the entire system was subsequently completed to ensure the control capability before successful validation in multiple commercial farms. It is anticipated that with the developed algorithms the authentication of fully autonomous robotic systems functioning in agricultural crops will provide heightened efficiency of needed costly services; scouting, disease detection, collection, and distribution.
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Date Issued: 2018
Identifier: CFE0007401, ucf:52743
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007401

Title: Action Recognition, Temporal Localization and Detection in Trimmed and Untrimmed Video.
Creator: Hou, Rui, Shah, Mubarak, Mahalanobis, Abhijit, Hua, Kien, Sukthankar, Rahul, University of Central Florida
Abstract / Description: Automatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above...
Show moreAutomatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above tasks by proposing. First, for video action recognition, we propose a category level feature learning method. Our proposed method automatically identifies such pairs of categories using a criterion of mutual pairwise proximity in the (kernelized) feature space, and a category-level similarity matrix where each entry corresponds to the one-vs-one SVM margin for pairs of categories. Second, for temporal action localization, we propose to exploit the temporal structure of actions by modeling an action as a sequence of sub-actions and present a computationally efficient approach. Third, we propose 3D Tube Convolutional Neural Network (TCNN) based pipeline for action detection. The proposed architecture is a unified deep network that is able to recognize and localize action based on 3D convolution features. It generalizes the popular faster R-CNN framework from images to videos. Last, an end-to-end encoder-decoder based 3D convolutional neural network pipeline is proposed, which is able to segment out the foreground objects from the background. Moreover, the action label can be obtained as well by passing the foreground object into an action classifier. Extensive experiments on several video datasets demonstrate the superior performance of the proposed approach for video understanding compared to the state-of-the-art.
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Date Issued: 2019
Identifier: CFE0007655, ucf:52502
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007655

Title: THE RHETORIC OF THE REGIONAL IMAGE: INTERPRETING THE VISUAL PRODUCTS OF REGIONAL PLANNNING.
Creator: Torres, Alissa, Bowdon, Melody, University of Central Florida
Abstract / Description: The Rhetoric of the Regional Image: Interpreting the Visual Products of Regional Planning investigates the manner in which visual conventions and visual contexts of regional visioning scenarios affect their interpretation by urban and regional planners, who use visual communication to meet the technical and rhetorical demands of their professional practice. The research assesses Central FloridaÃ‚Â's Ã‚Â"How Shall We Grow?Ã‚Â" regional...
Show moreThe Rhetoric of the Regional Image: Interpreting the Visual Products of Regional Planning investigates the manner in which visual conventions and visual contexts of regional visioning scenarios affect their interpretation by urban and regional planners, who use visual communication to meet the technical and rhetorical demands of their professional practice. The research assesses Central FloridaÃ‚Â's Ã‚Â"How Shall We Grow?Ã‚Â" regional land use scenario using focus groups and interviews with planning professionals, a corresponding survey of community values, and rhetorical analysis to explore the Ã‚Â"How Shall We Grow?Ã‚Â" scenario as persuasive communication. The Rhetoric of the Regional Image proposes specific recommendations for technology-based visual communication and scenario development in urban and regional planning practice, while contributing to literature in technical communication and rhetoric by examining plannersÃ‚Â' professional communication within their discourse community.
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Date Issued: 2010
Identifier: CFE0003511, ucf:48943
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003511

Title: VISION BASED TRAJECTORY TRACKING OF SPACE DEBRIS IN CLOSE PROXIMITY VIA INTEGRATED ESTIMATION AND CONTROL.
Creator: Li, Ni, Xu, Yunjun, University of Central Florida
Abstract / Description: The increasingly cluttered environment in space is placing a premium on techniques capable of tracking and estimating the trajectory of space debris. Unlike the debris smaller than 1 cm or larger than 10 cm, it is always a challenge for spacecraft or satellite mission designers to consider explicitly the ones ranged from 1 cm to 10 cm a priori. To tackle this challenge, a vision based debris' trajectory tracking method in close proximity using two cameras onboard satellites in a formation is...
Show moreThe increasingly cluttered environment in space is placing a premium on techniques capable of tracking and estimating the trajectory of space debris. Unlike the debris smaller than 1 cm or larger than 10 cm, it is always a challenge for spacecraft or satellite mission designers to consider explicitly the ones ranged from 1 cm to 10 cm a priori. To tackle this challenge, a vision based debris' trajectory tracking method in close proximity using two cameras onboard satellites in a formation is presented in the thesis. Also to differentiate the target debris from other clutters, data association is investigated. A two-stage nonlinear robust controller is developed to adjust the attitude of the satellites such that the desired field of view can be achieved for the target debris. Capabilities of the proposed integrated estimation and control methods are validated in the simulations.
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Date Issued: 2011
Identifier: CFE0003731, ucf:48775
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003731

Title: Development of 3D Vision Testbed for Shape Memory Polymer Structure Applications.
Creator: Thompson, Kenneth, Xu, Yunjun, Gou, Jihua, Lin, Kuo-Chi, University of Central Florida
Abstract / Description: As applications for shape memory polymers (SMPs) become more advanced, it is necessary to have the ability to monitor both the actuation and thermal properties of structures made of such materials. In this paper, a method of using three stereo pairs of webcams and a single thermal camera is studied for the purposes of both tracking three dimensional motion of shape memory polymers, as well as the temperature of points of interest within the SMP structure. The method used includes a stereo...
Show moreAs applications for shape memory polymers (SMPs) become more advanced, it is necessary to have the ability to monitor both the actuation and thermal properties of structures made of such materials. In this paper, a method of using three stereo pairs of webcams and a single thermal camera is studied for the purposes of both tracking three dimensional motion of shape memory polymers, as well as the temperature of points of interest within the SMP structure. The method used includes a stereo camera calibration with integrated local minimum tracking algorithms to locate points of interest on the material and measure their temperature through interpolation techniques. The importance of the proposed method is that it allows a means to cost effectively monitor the surface temperature of a shape memory polymer structure without having to place intrusive sensors on the samples, which would limit the performance of the shape memory effect. The ability to monitor the surface temperatures of a SMP structure allows for more complex configurations to be created while increasing the performance and durability of the material. Additionally, as compared to the previous version, both the functionalities of the testbed and the user interface have been significantly improved.
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Date Issued: 2015
Identifier: CFE0005893, ucf:50860
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005893

Title: Computerized Evaluatution of Microsurgery Skills Training.
Creator: Jotwani, Payal, Foroosh, Hassan, Hughes, Charles, Hua, Kien, University of Central Florida
Abstract / Description: The style of imparting medical training has evolved, over the years. The traditional methods of teaching and practicing basic surgical skills under apprenticeship model, no longer occupy the first place in modern technically demanding advanced surgical disciplines like neurosurgery. Furthermore, the legal and ethical concerns for patient safety as well as cost-effectiveness have forced neurosurgeons to master the necessary microsurgical techniques to accomplish desired results. This has lead...
Show moreThe style of imparting medical training has evolved, over the years. The traditional methods of teaching and practicing basic surgical skills under apprenticeship model, no longer occupy the first place in modern technically demanding advanced surgical disciplines like neurosurgery. Furthermore, the legal and ethical concerns for patient safety as well as cost-effectiveness have forced neurosurgeons to master the necessary microsurgical techniques to accomplish desired results. This has lead to increased emphasis on assessment of clinical and surgical techniques of the neurosurgeons. However, the subjective assessment of microsurgical techniques like micro-suturing under the apprenticeship model cannot be completely unbiased. A few initiatives using computer-based techniques, have been made to introduce objective evaluation of surgical skills.This thesis presents a novel approach involving computerized evaluation of different components of micro-suturing techniques, to eliminate the bias of subjective assessment. The work involved acquisition of cine clips of micro-suturing activity on synthetic material. Image processing and computer vision based techniques were then applied to these videos to assess different characteristics of micro-suturing viz. speed, dexterity and effectualness. In parallel subjective grading on these was done by a senior neurosurgeon. Further correlation and comparative study of both the assessments was done to analyze the efficacy of objective and subjective evaluation.
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Date Issued: 2015
Identifier: CFE0006221, ucf:51056
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006221

Title: Visual Geo-Localization and Location-Aware Image Understanding.
Creator: Roshan Zamir, Amir, Shah, Mubarak, Jha, Sumit, Sukthankar, Rahul, Lin, Mingjie, Fathpour, Sasan, University of Central Florida
Abstract / Description: Geo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images.In this dissertation, we address three questions central to...
Show moreGeo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images.In this dissertation, we address three questions central to geo-spatial analysis of ground-level imagery: \textbf{1) How to geo-localize images and videos captured at unknown locations? 2) How to refine the geo-location of already geo-tagged data? 3) How to utilize the extracted geo-tags?}We present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing the consistency among their global features (e.g., GIST) using GMCP. Our evaluations using a new data set of 102k Street View images shows the proposed method outperforms the state-of-the-art by 10 percent.Geo-localization of images can be extended to geo-localization of a video. We have developed a novel method for estimating the geo-spatial trajectory of a moving camera with unknown intrinsic parameters in a city-scale. The proposed method is based on a three step process: 1) individual geo-localization of video frames using Street View images to obtain the likelihood of the location (latitude and longitude) given the current observation, 2) Bayesian tracking to estimate the frame location and video's temporal evolution using previous state probabilities and current likelihood, and 3) applying a novel Minimum Spanning Trees based trajectory reconstruction to eliminate trajectory loops or noisy estimations. Thus far, we have assumed reliable geo-tags for reference imagery are available through crowdsourcing. However, crowdsourced images are well known to suffer from the acute shortcoming of having inaccurate geo-tags. We have developed the first method for refinement of GPS-tags which automatically discovers the subset of corrupted geo-tags and refines them. We employ Random Walks to discover the uncontaminated subset of location estimations and robustify Random Walks with a novel adaptive damping factor that conforms to the level of noise in the input. In location-aware image understanding, we are interested in improving the image analysis by putting it in the right geo-spatial context. This approach is of particular importance as the majority of cameras and mobile devices are now being equipped with GPS chips. Therefore, developing techniques which can leverage the geo-tags of images for improving the performance of traditional computer vision tasks is of particular interest. We have developed a location-aware multimodal approach which incorporates business directories, textual information, and web images to identify businesses in a geo-tagged query image.
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Date Issued: 2014
Identifier: CFE0005544, ucf:50282
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005544

Title: TOWARDS A SELF-CALIBRATING VIDEO CAMERA NETWORK FOR CONTENT ANALYSIS AND FORENSICS.
Creator: Junejo, Imran, Foroosh, Hassan, University of Central Florida
Abstract / Description: Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space....
Show moreDue to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how the network as a whole can be calibrated, such that each camera as a unit in the network is aware of its orientation with respect to all the other cameras in the network. Different types of cameras might be present in a multiple camera network and novel techniques are presented for efficient calibration of these cameras. Specifically: (i) For a stationary camera, we derive new constraints on the Image of the Absolute Conic (IAC). These new constraints are shown to be intrinsic to IAC; (ii) For a scene where object shadows are cast on a ground plane, we track the shadows on the ground plane cast by at least two unknown stationary points, and utilize the tracked shadow positions to compute the horizon line and hence compute the camera intrinsic and extrinsic parameters; (iii) A novel solution to a scenario where a camera is observing pedestrians is presented. The uniqueness of formulation lies in recognizing two harmonic homologies present in the geometry obtained by observing pedestrians; (iv) For a freely moving camera, a novel practical method is proposed for its self-calibration which even allows it to change its internal parameters by zooming; and (v) due to the increased application of the pan-tilt-zoom (PTZ) cameras, a technique is presented that uses only two images to estimate five camera parameters. For an automatically configurable multi-camera network, having non-overlapping field of view and possibly containing moving cameras, a practical framework is proposed that determines the geometry of such a dynamic camera network. It is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the geometry of a dynamic network. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic as well as on real data. Applications to path modeling, GPS coordinate estimation, and configuring mixed-reality environment are explored.
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Date Issued: 2007
Identifier: CFE0001743, ucf:47296
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001743

Computer Vision (13)	+ -
computer vision (12)	+ -
Machine Learning (4)	+ -
action recognition (4)	+ -
object detection (3)	+ -

optical flow (3)	+ -
Action Recognition (2)	+ -
Deep Learning (2)	+ -
Edith Johnstone (2)	+ -
Shape Memory Polymer (2)	+ -
Structural Health Monitoring (2)	+ -
Testbed (2)	+ -
deep learning (2)	+ -
leadership (2)	+ -
machine learning (2)	+ -
tracking (2)	+ -
vision (2)	+ -
2-D animation (1)	+ -
3D reconstruction (1)	+ -
Action Detection (1)	+ -
Action Localization (1)	+ -
Activities of Daily Living (1)	+ -
Adversarial Attack (1)	+ -
Agriculture (1)	+ -
Andy Clark (1)	+ -
Assistive Robotic (1)	+ -
Autonomous ground vehicle (1)	+ -
Bridges (1)	+ -
Cancer (1)	+ -
Cancer Diagnosis (1)	+ -

University of Central Florida (42)	+ -
Shah, Mubarak (13)	+ -
Foroosh, Hassan (6)	+ -
Xu, Yunjun (5)	+ -
Sukthankar, Rahul (4)	+ -

Bagci, Ulas (3)	+ -
Boloni, Ladislau (3)	+ -
Da Vitoria Lobo, Niels (3)	+ -
Lin, Mingjie (3)	+ -
Tappen, Marshall (3)	+ -
Behal, Aman (2)	+ -
Catbas, Necati (2)	+ -
Doherty, Walter (2)	+ -
Gong, Boqing (2)	+ -
Gou, Jihua (2)	+ -
Heinrich, Mark (2)	+ -
Hu, Haiyan (2)	+ -
Hua, Kien (2)	+ -
Jha, Sumit (2)	+ -
Jha, Sumit Kumar (2)	+ -
Jones, Anna (2)	+ -
Kauffman, Jeffrey L. (2)	+ -
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Lin, Kuo-Chi (2)	+ -
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da Vitoria Lobo, Niels (2)	+ -
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Ardeshir Behrostaghi, Shervin (1)	+ -
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