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2013 IEEE Conference on Computer Vision and Pattern Recognition (2003)
Madison, Wisconsin
June 18, 2003 to June 20, 2003
ISSN: 1063-6919
ISBN: 0-7695-1900-8
pp: 134
Jitendra Malik , University of California, Berkeley
Greg Mori , University of California, Berkeley
In this paper we explore object recognition in clutter. We test our object recognition techniques on Gimpy and EZ-Gimpy, examples of visual CAPTCHAs. A CAPTCHA ("Completely Automated Public Turing test to Tell Computers and Humans Apart") is a program that can generate and grade tests that most humans can pass, yet current computer programs can?t pass. EZ-Gimpy (see Fig. 1, 5), currently used by Yahoo, and Gimpy (Fig. 2,9) are CAPTCHAs based on word recognition in the presence of clutter. These CAPTCHAs provide excellent test sets since the clutter they contain is adversarial; it is designed to confuse computer programs. We have developed efficient methods based on shape context matching that can identify the word in an EZ-Gimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time. The problem of identifying words in such severe clutter provides valuable insight into the more general problem of object recognition in scenes. The methods that we present are instances of a framework designed to tackle this general problem.
Jitendra Malik, Greg Mori, "Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 01, no. , pp. 134, 2003, doi:10.1109/CVPR.2003.1211347
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