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DETECTING CURVED OBJECTS AGAINST CLUTTERED BACKGROUNDS
| Title: | DETECTING CURVED OBJECTS AGAINST CLUTTERED BACKGROUNDS. |
 58 views 21 downloads |
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| Name(s): |
Prokaj, Jan, Author Lobo, Niels, Committee Chair University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2008 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| 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 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%. | |
| Identifier: | CFE0002102 (IID), ucf:47535 (fedora) | |
| Note(s): |
2008-05-01 M.S. Engineering and Computer Science, School of Electrical Engineering and Computer Science Masters This record was generated from author submitted information. |
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| Subject(s): |
computer vision pattern recognition object detection |
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| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0002102 | |
| Restrictions on Access: | public | |
| Host Institution: | UCF |
