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| April 12
| April 12
| Jun-Cheng Chen
| Jun-Cheng Chen
|
| Ambiguities in Camera Self-Calibration
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|-
| April 19
| April 19
Iris recognition is one of the most popular approaches for human authentication, since the iris patterns are unique for each person and remain stable for long periods of time. However, existing algorithms for iris recognition require clean iris images, which limit their utility in unconstrained environments like surveillance. In this work, we develop an unconstrained iris recognition algorithm by modeling the inherent structure in clean iris images using sparse representations. The proposed algorithm recognizes the test image and also predicts the quality of acquisition. We further extend the introduced algorithm by a quality based fusion framework, which combine the recognition results from multiple test images. Extensive evaluation on existing datasets clearly demonstrate the utility of the proposed algorithm for recognition and image quality estimation.
Iris recognition is one of the most popular approaches for human authentication, since the iris patterns are unique for each person and remain stable for long periods of time. However, existing algorithms for iris recognition require clean iris images, which limit their utility in unconstrained environments like surveillance. In this work, we develop an unconstrained iris recognition algorithm by modeling the inherent structure in clean iris images using sparse representations. The proposed algorithm recognizes the test image and also predicts the quality of acquisition. We further extend the introduced algorithm by a quality based fusion framework, which combine the recognition results from multiple test images. Extensive evaluation on existing datasets clearly demonstrate the utility of the proposed algorithm for recognition and image quality estimation.
===Ambiguities in Camera Self-Calibration===
Speaker: Jun-Cheng Chen -- Date: April 12, 2012
Structure from motion (SfM) is the problem of computing the 3D scene and camera parameters from a video or collection of images. SfM problems can be further classified as calibrated and uncalibrated. In calibrated SfM, the internal camera parameters are known. This is a much easier problem than the uncalibrated case, where these parameters are unknown. Solving for the internal camera parameters are known as the camera self/auto calibration problem. Critical motion sequences (CMS) are those sequences/videos from which internal parameters cannot be determined uniquely, that is, there are many different settings of internal parameters that give rise to the same video. In this talk, we are going to show that three cases of motions, (1) pure translation, (2) single rotation, and (3) single rotation about X/Y/Z-axis and translation, are CMS, and the necessary and sufficient conditions of a sequence not being a CMS.
