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Home > Society and Social Sciences > Psychology > Feature Representation for Generic Object Detection and Recognition: Computer Vision and Human Vision.(English)
Feature Representation for Generic Object Detection and Recognition: Computer Vision and Human Vision.(English)

Feature Representation for Generic Object Detection and Recognition: Computer Vision and Human Vision.(English)

          
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About the Book

Object recognition/detection has always been a challenging topic for both computer scientists and psychologists. We study the categorical object recognition/detection problem from views of both computer vision and human vision, with the focus on feature representation. We propose representing general object categories using multiple types of complementary features, and using AdaBoost to select the most discriminative features. By adopting a heterogeneous-feature model, our method achieved state-of-the-art performance at the time it was proposed. We also present a computational model which describes human eye movements during a visual search task. Our experimental results show that a purely top-down model provides an excellent description of human search behavior. We then incorporate our heterogeneous-feature representation into the human eye movements model, and further explore human search behavior when the search target is categorical. Previous work in human vision mostly uses specific object as stimuli. The model is shown to have strong agreement with human behavior. Motivated by the search behavior of humans, we propose to use multi-resolution as a general framework for object category detection which achieves real-time detection speed with fast training and high accuracy. Other state-of-the-art object detection methods are rarely shown to gain in all the above aspects. We also explore the concept of "visual similarity" in humans from the point of view of "nontargets," using the heterogeneous-feature model. Moreover, we find out target-like distractors can form a learnable class, which may open new research areas for machine learning/computer vision. In addition, we apply the visual similarity concept to the problem of inferring the search target of human subject when the target is absent from the display.


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Product Details
  • ISBN-13: 9781243574459
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 254 mm
  • No of Pages: 132
  • Series Title: English
  • Sub Title: Computer Vision and Human Vision.
  • Width: 203 mm
  • ISBN-10: 1243574453
  • Publisher Date: 03 Sep 2011
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 9 mm
  • Weight: 277 gr


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Feature Representation for Generic Object Detection and Recognition: Computer Vision and Human Vision.(English)
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Feature Representation for Generic Object Detection and Recognition: Computer Vision and Human Vision.(English)
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