Conditional State-Space Models (CSSM) [1]

We consider the problem of predicting a sequence of real-valued multivariate states from a given measurement sequence. Its typical application in computer vision is the task of motion estimation. State-Space Models are widely used generative Read the rest


Discriminative Learning of Dynamical Systems (D-LDS) [1]

We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hidden Markov Models in sequence tagging problems in discrete domains. Read the rest


D-Dynamics – Discriminative Dynamic Model Learning

Traditionally, dynamic models are learned to best represent the “observations” such as the silhouettes on an object moving in the sequence of video frames. The “true” object state, it’s position, pose, velocity, is missing Read the rest

Sequence Grouping

We consider the problem of learning density mixture models for Classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent the density at all points in the sample space. Discriminative learning, on the other hand, aims

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Human Motion

Marginal Nonlinear Dynamic System (MNDS) [1]

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. To automate the process of motion modeling we consider a class of learned dynamic models cast in the framework Read the rest

Face Recognition

Video-based Face Tracking and Recognition with Visual Constraints [1]

We address the problem of tracking and recognition of faces in real-world, noisy videos. We identify faces using a tracker that adaptively builds the target model reflecting changes in appearance, typical Read the rest

Image Segmentation

Image segmentation is one of the most important steps leading to the analysis of image data. The goal is dividing the image into parts that have homogeneous attributes, and have a strong correlation with objects or areas of the real Read the rest

Shape Modeling

Shape analysis is an important process for many computer vision applications, including image classification, recognition, retrieval, registration, segmentation, etc. An ideal shape model should be both invariant to global transformations and robust to local distortions. In this work we developed Read the rest