Hand gesture recognition by histogram based kernel using density measure

This paper presents the recognition of hand gesture in the research field of machine vision. Vision based hand gesture recognition has the capacity to develop a tool for Human Machine Interaction (HCI). The automated threshold methods were used as pre-processing steps for extraction of feature vector using chain code histogram (CCH). Then, construct the kernel based on histogram of chain code using density measure to obtain discriminative feature descriptor for efficient recognition of hand gesture using Support Vector Machine (SVM). Cluster based threshold techniques involves Otsu thtresholding (OT), Ridler and Calvard thresholding (RCT), and Kittler and Illingworth thresholding (KIT) are used to segment the region of interest for feature extraction. In this paper, CCH based on various segmentation methods were compared to measure the recognition rate by SVM classifier. The proposed RCT-CCH based kernel method increase the recognition rate of hand posture by 90%, compared with cluster based thresholds.

[1]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[2]  Venu Govindaraju,et al.  Improved k-nearest neighbor classification , 2002, Pattern Recognit..

[3]  Wayne Nilback.  An introduction to digital image processing , 1985 .

[4]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[5]  C. K. Lee,et al.  Real-time thresholding using histogram concavity (image processing) , 1992, [1992] Proceedings of the IEEE International Symposium on Industrial Electronics.

[6]  Azriel Rosenfeld,et al.  Histogram concavity analysis as an aid in threshold selection , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[8]  António dos Anjos,et al.  Bi-Level Image Thresholding - A Fast Method , 2008, BIOSIGNALS.

[9]  Hong Gu,et al.  Imbalanced classification using support vector machine ensemble , 2011, Neural Computing and Applications.

[10]  B. Kapralos,et al.  I An Introduction to Digital Image Processing , 2022 .

[11]  Khairuddin Omar,et al.  An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows , 2011, Pattern Recognit. Lett..

[12]  M. Hrúz,et al.  Local binary pattern based features for sign language recognition , 2011, Pattern Recognition and Image Analysis.

[13]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..