Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Volume 1
Tomas Pajdla, Jiri Matas
Springer, Jun 14, 2004 - Computers - 633 pages
The four-volume set comprising LNCS volumes 3021/3022/3023/3024 constitutes the refereed proceedings of the 8th European Conference on Computer Vision, ECCV 2004, held in Prague, Czech Republic, in May 2004. The 190 revised papers presented were carefully reviewed and selected from a total of 555 papers submitted. The four books span the entire range of current issues in computer vision. The papers are organized in topical sections on tracking; feature-based object detection and recognition; geometry; texture; learning and recognition; information-based image processing; scale space, flow, and restoration; 2D shape detection and recognition; and 3D shape representation and reconstruction.
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Tracking with the EM Contour Algorithm
A Multiview Approach to Segmenting and Tracking
Hausdorff Kernel for 3D Object Acquisition and Detection
169 other sections not shown
affine transformation algorithm analysis annihilation applied approach approximation Bessel camera catastrophe cluster components Computer Vision constraints contour corresponding covariance covariance matrix critical curve critical points curvature database defined denotes density derivatives detection detector dimensional distribution ECCV edge edge detection eigenvalues eigenvectors entropy equation error estimate feature Figure Fold catastrophe frame function Gaussian geometry given gradient histogram human motion IEEE IEEE Trans Image Processing image sequences intensity interest point iteration Kalman filter kernel key frames likelihood linear manifold matching matrix method minimization MLESAC motion capture multiple noise normal object obtained occlusion optical flow orientation parameters particle filtering pixels plane polybone probabilistic problem Proc recognition recursive filters regions representation resolution robust rotation sampling scale space scale space saddle scene segmentation shape shown shows signal smoothing spatial spatio-temporal statistics structure super-resolution surface temporal tensor texture tracking transformation values vector