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- Title
- Digital Image Processing Using NTEC Facilities.
- Creator
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Roesch, James F., Simons, Jr., Fred O., Engineering
- Abstract / Description
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University of Central Florida College of Engineering Thesis; Digital image enhancement refers to the improvement of a given image for human interpretation. Digital image processing facilities are those in which hardware and software computing elements are combined in such a way as to enable the processing of digital images. This report describes the use of the Naval Training Equipment Center (NTEC) Computer Systems Laboratory computing facilities to enhance digital images. Described are two...
Show moreUniversity of Central Florida College of Engineering Thesis; Digital image enhancement refers to the improvement of a given image for human interpretation. Digital image processing facilities are those in which hardware and software computing elements are combined in such a way as to enable the processing of digital images. This report describes the use of the Naval Training Equipment Center (NTEC) Computer Systems Laboratory computing facilities to enhance digital images. Described are two major hardware systems, the IKONAS RDS-3000 raster display graphics system and the VAX-11/780, and the digital image processing program (DIMPRP) written by the author. Digital image enhancement theory and practice are addressed through a discussion of the DIMPRP software. Finally, enhancements to the NTEC digital image processing facility such as improvements in hardware reliability, documentation, and increased speed of program esecution are discussed.
Show less - Date Issued
- 1984
- Identifier
- CFR0008160, ucf:53072
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFR0008160
- Title
- AUTONOMOUS ROBOTIC AUTOMATION SYSTEMWITH VISION FEEDBACK.
- Creator
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Rosino, Jeffery, Qu, Zhihua, University of Central Florida
- Abstract / Description
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In this thesis, a full design, development and application of an autonomous robotic automation system using vision feedback is performed. To realize this system, a cylindrical manipulator configuration is implemented, using a personal computer (PC) based PID controller from National Instruments. Full autonomous control will be achieved via a programmable human machine interface (HMI) developed on a PC using Borland C++ Builder. The vision feedback position control is accomplished using an...
Show moreIn this thesis, a full design, development and application of an autonomous robotic automation system using vision feedback is performed. To realize this system, a cylindrical manipulator configuration is implemented, using a personal computer (PC) based PID controller from National Instruments. Full autonomous control will be achieved via a programmable human machine interface (HMI) developed on a PC using Borland C++ Builder. The vision feedback position control is accomplished using an ordinary "off-the-shelf" web camera. The manuscript is organized as follows; After Chapter 1, an introduction to automation history and its role in the manufacturing industry, Chapter 2 discusses and outlines the development of the robotic kinematics and dynamics of the system. A control strategy is also developed and simulated in this chapter. Chapter 3 discusses color image processing and shows the development of the algorithm used for the vision feedback position control. Chapter 4 outlines the system development, which includes the hardware and software. Chapter 5 concludes with a summary, and improvement section. The process used as a basis for the design and development of this thesis of this thesis topic was constructed from a manual capacitor orientation check test station. A more detailed definition and objective is presented in the introduction.
Show less - Date Issued
- 2004
- Identifier
- CFE0000277, ucf:46220
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000277
- Title
- Analytical study of computer vision-based pavement crack quantification using machine learning techniques.
- Creator
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Mokhtari, Soroush, Yun, Hae-Bum, Nam, Boo Hyun, Catbas, Necati, Shah, Mubarak, Xanthopoulos, Petros, University of Central Florida
- Abstract / Description
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Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack...
Show moreImage-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time.Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance.Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application.
Show less - Date Issued
- 2015
- Identifier
- CFE0005671, ucf:50186
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005671
