Computer Vision

Introduction to the theory of the fundamental problems in computer vision, evidences from biological vision, mathematical models and computational algorithms for their solution, and description of selected applications. Optical sensors and image formation. Color. 2D/3D processing of spatio-temporal visual signals: brief review of multidimensional linear filter and  Fourier/Gabor analysis. Morphological operators and nonlinear filters. Multiscale image analysis with linear (Gaussian scale-space) and nonlinear methods (geometric diffusion). Shape and Texture analysis. Estimation of 2D/3D visual motion. Stereopsis and multiple-view geometry. Curve/surface evolution, active contours and level sets. Image segmentation.  3D reconstruction.  Object detection and recognition.  Brief description of applications in: artificial intelligence, biomedicine, robotics, digital arts, internet.