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| April 19
| April 19
| Sima Taheri
| Sima Taheri
|
| Facial Expression Analysis Systems
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| April 26
| April 26
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.
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.
===Facial Expression Analysis Systems===
Speaker: [http://www.umiacs.umd.edu/~taheri/ Sima Taheri] -- Date: April 19, 2012
The goal of facial expression analysis is to create systems that can automatically analyze and recognize facial feature changes and facial motion due to facial expressions from visual information. This has been an active research topic for several years and has attracted the interest of many computer vision researchers and behavioral scientists, with applications in behavioral science, security, animation, and human-computer interaction. In this talk, I will briefly describe the components of a facial expression analysis system and review some previous work. Then I will talk about my work, View-Invariant Expression Analysis using Analytic Shape Manifolds and Structure-Preserving Sparse Decomposition for Facial Expression Analysis.