An Effective Segmentation for Noise-Based Image Verification Using Gamma Mixture Models
Identifieur interne : 000748 ( Main/Exploration ); précédent : 000747; suivant : 000749An Effective Segmentation for Noise-Based Image Verification Using Gamma Mixture Models
Auteurs : Ling Cai [République populaire de Chine] ; Yiren Xu [République populaire de Chine] ; Lei He [États-Unis] ; Yuming Zhao [République populaire de Chine] ; Xin Yang [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: Image verification has been widely used in numerous websites to prevent them from batch registration or automated posting. One category of the image verification is generated by adding noise into character or digit images to make them hard to be recognized by Optical Character Recognition (OCR). In this paper, we propose a novel probability gradient function for active contour models to efficiently segment this type of images for easier recognition. Experiments on a set of images with different intensities and types of noise show the superiority of the proposed probability gradient to traditional method. The purpose of our paper is to warn some websites who are still using such kind of verification: they should improve their defense method to prevent them from the potential risk.
Url:
DOI: 10.1007/978-3-642-12297-2_3
Affiliations:
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One category of the image verification is generated by adding noise into character or digit images to make them hard to be recognized by Optical Character Recognition (OCR). In this paper, we propose a novel probability gradient function for active contour models to efficiently segment this type of images for easier recognition. Experiments on a set of images with different intensities and types of noise show the superiority of the proposed probability gradient to traditional method. 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