Object Detection and Recognition in Natural Settings
Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based...
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ndltd-pdx.edu-oai-pdxscholar.library.pdx.edu-open_access_etds-19252019-10-20T04:39:13Z Object Detection and Recognition in Natural Settings Dittmar, George William Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based on the model proposed by Hubel & Wiesel [10]. This thesis investigates the ability of an HMAX-like system (GLIMPSE [20]) to perform object-detection in cluttered natural scenes. I evaluate these results using the StreetScenes database from MIT [1, 8]. This thesis addresses three questions: (1) Can the GLIMPSE-based object detection system replicate the results on object-detection reported by Bileschi using HMAX? (2) Which features computed by GLIMPSE lead to the best object-detection performance? (3) What effect does elimination of clutter in the training sets have on the performance of our system? As part of this thesis, I built an object detection and recognition system using GLIMPSE [20] and demonstrate that it approximately replicates the results reported in Bileschi's thesis. In addition, I found that extracting and combining features from GLIMPSE using different layers of the HMAX model gives the best overall invariance to position, scale and translation for recognition tasks, but comes with a much higher computational overhead. Further contributions include the creation of modified training and test sets based on the StreetScenes database, with removed clutter in the training data and extending the annotations for the detection task to cover more objects of interest that were not in the original annotations of the database. 2013-01-04T08:00:00Z text application/pdf https://pdxscholar.library.pdx.edu/open_access_etds/926 https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1925&context=open_access_etds Dissertations and Theses PDXScholar Artificial intelligence -- Computer programs Machine learning Computer vision Artificial Intelligence and Robotics Other Computer Sciences |
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Artificial intelligence -- Computer programs Machine learning Computer vision Artificial Intelligence and Robotics Other Computer Sciences Dittmar, George William Object Detection and Recognition in Natural Settings |
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Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based on the model proposed by Hubel & Wiesel [10]. This thesis investigates the ability of an HMAX-like system (GLIMPSE [20]) to perform object-detection in cluttered natural scenes. I evaluate these results using the StreetScenes database from MIT [1, 8]. This thesis addresses three questions: (1) Can the GLIMPSE-based object detection system replicate the results on object-detection reported by Bileschi using HMAX? (2) Which features computed by GLIMPSE lead to the best object-detection performance? (3) What effect does elimination of clutter in the training sets have on the performance of our system? As part of this thesis, I built an object detection and recognition system using GLIMPSE [20] and demonstrate that it approximately replicates the results reported in Bileschi's thesis. In addition, I found that extracting and combining features from GLIMPSE using different layers of the HMAX model gives the best overall invariance to position, scale and translation for recognition tasks, but comes with a much higher computational overhead. Further contributions include the creation of modified training and test sets based on the StreetScenes database, with removed clutter in the training data and extending the annotations for the detection task to cover more objects of interest that were not in the original annotations of the database. |
author |
Dittmar, George William |
author_facet |
Dittmar, George William |
author_sort |
Dittmar, George William |
title |
Object Detection and Recognition in Natural Settings |
title_short |
Object Detection and Recognition in Natural Settings |
title_full |
Object Detection and Recognition in Natural Settings |
title_fullStr |
Object Detection and Recognition in Natural Settings |
title_full_unstemmed |
Object Detection and Recognition in Natural Settings |
title_sort |
object detection and recognition in natural settings |
publisher |
PDXScholar |
publishDate |
2013 |
url |
https://pdxscholar.library.pdx.edu/open_access_etds/926 https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1925&context=open_access_etds |
work_keys_str_mv |
AT dittmargeorgewilliam objectdetectionandrecognitioninnaturalsettings |
_version_ |
1719271194887192576 |