• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.181-186
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• 2014

The paper proposes a new method of segmentation-based feature extraction to improve performance in pedestrian upper-body detection. General pedestrian detectors that use local features are often plagued by false positives due to the locality. Color information of multi parts of the upper body is utilized in figure-ground segmentation scheme to extract an salient, "global" shape feature capable of reducing the false positives. The performance of the multi-part color segmentation-based feature is evaluated by changing color spaces and the parameters of color histogram. The experimental result from an upper-body dataset shows that the proposed feature is effective in reducing the false positives of local feature-based detectors.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.5
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• pp.241-246
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• 2015

Although many different methods have been used to solve stereo correspondent problems, the deviation of accuracy is too big. Among those many methods, the one that uses segmentation information of input image has received high attention in academic field since it is very close to vision recognition. In this thesis, the existing method of acquiring a single value by using the segment information and initial disparity value was viewed in NP-hard problem to propose a new method. In order to verify the validity of the proposed method, well-known data were used for experiment and the resulted data was analyzed. Although there were some disadvantages in the time aspect, it showed somewhat useful results in the accuracy aspect.