Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation
International Journal of Computer Vision
Distortion estimation techniques in solving visual CAPTCHAs
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Automatic gradient threshold determination for edge detection
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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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.