Object localization using adaptive feature selection

S. Youngkyoo Hwang; Jungbae Kim; Seongdeok Lee

doi:10.1117/12.805840

19 January 2009 Object localization using adaptive feature selection

S. Youngkyoo Hwang, Jungbae Kim, Seongdeok Lee

Proceedings Volume 7252, Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques; 72520Q (2009) https://doi.org/10.1117/12.805840
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

'Fast and robust' are the most beautiful keywords in computer vision. Unfortunately they are in trade-off relationship. We present a method to have one's cake and eat it using adaptive feature selections. Our chief insight is that it compares reference patterns to query patterns, so that it selects smartly more important and useful features to find target. The probabilities of pixels in the query to belong to the target are calculated from importancy of features. Our framework has three distinct advantages: 1 - It saves computational cost dramatically to the conventional approach. This framework makes it possible to find location of an object in real-time. 2 - It can smartly select robust features of a reference pattern as adapting to a query pattern. 3- It has high flexibility on any feature. It doesn't matter which feature you may use. Lots of color space, texture, motion features and other features can fit perfectly only if the features meet histogram criteria.

Citation Download Citation

S. Youngkyoo Hwang, Jungbae Kim, and Seongdeok Lee "Object localization using adaptive feature selection", Proc. SPIE 7252, Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques, 72520Q (19 January 2009); https://doi.org/10.1117/12.805840

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KEYWORDS

Computer vision technology

Machine vision

Feature selection

Sensors

Feature extraction

Image processing

Detection and tracking algorithms

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