Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications 2016
DOI: 10.2991/icmmita-16.2016.160
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Human Shape Recognition Algorithm Design Based on Hu Moments and Zernike Moments

Abstract: Abstract. The invariant moment feature of target is taken as an important approach to human shape recognition. This paper gives a general review of Hu moments and Zernike moments in human shape recognition, and utilizes the minimum distance classifier to classify some moving targets. By comparing their characteristics in specific applications, this paper provides some basis for the selection of invariant moments in human shape recognition algorithm.

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Cited by 2 publications
(2 citation statements)
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“…e Sobol' sequence is obtained by constructing a group called the 'direction number' [43]. Assuming that m i is a positive odd number less than 2 n , then…”
Section: Sensitivity Analysismentioning
confidence: 99%
“…e Sobol' sequence is obtained by constructing a group called the 'direction number' [43]. Assuming that m i is a positive odd number less than 2 n , then…”
Section: Sensitivity Analysismentioning
confidence: 99%
“…Global features include contour representations, shape descriptors, and texture features. Shape Matrices, Invariant Moments (Hu, Zerinke), Histogram Oriented Gradients (HOG), and Co-HOG are some examples of global descriptors [23]. Local features describe the image patches (key points in the image) of an object.…”
Section: Introductionmentioning
confidence: 99%