2-D direction histogram based entropic thresholding

Abstract Local image features are effective descriptors for image analysis and are also important cues for image segmentation. In this paper, we propose a novel entropic thresholding approach. This approach incorporates local features into a conventional entropic method to implement the thresholding. The local features are obtained from an orientation histogram to describe the edge property of the local neighborhood. To verify the performance of our method, thresholding was carried out on different types of images and compared with some well-known entropic approaches. Experimental results show that using the local edge property can give a better thresholding result.

[1]  W. Pratt.  Digital Image Processing: Piks Scientific Inside , 1978 .

[2]  Prasanna K. Sahoo,et al.  Threshold selection using Renyi's entropy , 1997, Pattern Recognit..

[3]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[4]  A. D. Brink.  Thresholding of digital images using two-dimensional entropies , 1992, Pattern Recognit..

[5]  Gurdial Arora,et al.  A thresholding method based on two-dimensional Renyi's entropy , 2004, Pattern Recognit..

[6]  Xuelong Li,et al.  Efficient HOG human detection , 2011, Signal Process..

[7]  Martin D. Levine,et al.  Dynamic Measurement of Computer Generated Image Segmentations , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Márcio Portes de Albuquerque,et al.  Image thresholding using Tsallis entropy , 2004, Pattern Recognit. Lett..

[9]  Shyang Chang,et al.  A new criterion for automatic multilevel thresholding , 1995, IEEE Trans. Image Process..

[10]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[11]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[12]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[13]  Zhiguo Cao,et al.  Entropic thresholding based on gray-level spatial correlation histogram , 2008, 2008 19th International Conference on Pattern Recognition.

[14]  Akira Asano,et al.  Hybrid Image Thresholding Method using Edge Detection , 2009 .

[15]  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..

[16]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[17]  Adiljan Yimit,et al.  Fast Method for Two-dimensional Renyi’s Entropy-based Thresholding , 2012 .

[18]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[19]  Frank Y. Shih,et al.  Image Processing and Pattern Recognition: Fundamentals and Techniques , 2010 .

[20]  N. Pavesic,et al.  Gray level thresholding using the Havrda and Charvat entropy , 2000, 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099).

[21]  Thierry Pun,et al.  Entropic thresholding, a new approach , 1981 .

[22]  Wei Li,et al.  Fully affine invariant SURF for image matching , 2012, Neurocomputing.

[23]  Prasanna K. Sahoo,et al.  Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy , 2006, Pattern Recognit. Lett..

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

[25]  Ahmed S. Abutaleb,et al.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989, Comput. Vis. Graph. Image Process..