Gianfranco Doretto Daniel Cremers Paolo Favaro Stefano Soatto Computer Science Department, UCLA, Los Angeles, CA

1. Modeling, Analysis and Recognition of Dynamic Visual Processes Gianfranco Doretto Daniel Cremers Paolo Favaro Stefano SoattoComputer Science Department, UCLA, Los Angeles, CA Dynamic Textures Video Partitioning Synopsis Examples Synopsis Examples RECOGNITION MOTION SEGMENTATION Development of analytical and computational tools for the study of video-based models of sequences of natural images acquired from vision sensors. Development of analytical and computational tools for the partitioning of a video-sequence into regions which are homogeneous with respect to certain properties of interest. Statistical models of dynamic textures can be used to classify different dynamic visual processes into their appropriate category Using a variational formulation that includes optical flow, it is possible to segment motion in video sequences Original 1st closest 2st closest 3st closest 4st closest Performance: using a linear dynamical system with a state dimension of 20 components, and reduced patches of 48x48 pixels we obtain a recognition rate of 89.5% using PCA and subspace angles Applications SEGMENTATION WITH SHAPE PRIORS Applications By learning the shape statistics from images, it is possible to segment and track partially occluded complex shaped objects • Video coding • Surveillance • Robot navigation • Recognition • Ecosystem monitoring • Image-based rendering PARAMETER MANIPULATION From a statistical model of a dynamic texture one can infer some physical properties of the physical process that generated the images • Surveillance • Video coding • Medical image analysis • Object tracking • Human-machine interaction SEGMENTATION Statistical models of dynamic visual processes are useful to partition video sequences where the image spatial statistics is not discriminative Analytical Tools Analytical Tools • Statistical modeling of visual signals • Linear and nonlinear system identification • Variational methods • Multi-resolution analysis • Algebraic geometry • Variational methods • Statistical shape models • Levelset methods • Partial differential equations UCLAVISIONLABORATORY http://vision.ucla.edu