Sharing multiple secrets in visual cryptography

The secret sharing schemes in conventional visual cryptography are characterized by encoding one shared secret into a set of random transparencies which reveal the secret to the human visual system when they are superimposed. In this paper, we propose a visual secret sharing scheme that encodes a set of x 2 secrets into two circle shares such that none of any single share leaks the secrets and the x secrets can be obtained one by one by stacking the first share and the rotated second shares with x different rotation angles. This is the first true result that discusses the sharing ability in visual cryptography up to any general number of multiple secrets in two circle shares.     

An efficient segmentation algorithm for CAPTCHAs with line cluttering and character warping

A CAPTCHA is a test designed to distinguish computer programs from human beings, in order to prevent the abuse of networked resources. Academic research into CAPTCHAs includes designing friendly and secure CAPTCHA systems and defeating existing CAPTCHA systems. Traditionally, defeating a CAPTCHA test requires two procedures: segmentation and recognition. Recent research shows that the problem of segmentation is much harder than recognition. In this paper, two new segmentation techniques called projection and middle-axis point separation are proposed for CAPTCHAs with line cluttering and character warping. Experimental results show the proposed techniques can achieve segmentation rates of about 75%.     

A Vision-Based Augmented-Reality System For Multiuser Collaborative Environments

This work presented a novel vision-based augmented-reality system for applications in multiuser collaborative environments. The kernel technology of this vision-based system locates the cameras that are utilized to point and simulate the positions of multiple viewers. Camera calibration based on computer vision is employed during the camera's locating process. The applications in multiuser collaborative environments allow the viewers to view various positions and in numerous directions. However, traditional calibration approaches are not suitable for these cases sufficiently. A novel calibration pattern based on pseudo-random arrays is designed for multiuser collaborative applications. The pattern has a simple and regular structure, easily extracts features, achieves robust recognition using local information, and does not limit viewer positions and directions. Experimental results indicate that the proposed system provides a effective platform for applications in multiuser collaborative environments.