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This paper investigates a new learning formulation called <i>structured sparsity</i>, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea. A general theory is developed for learning with structured(More)
We describe a mixture density propagation algorithm to estimate 3D human motion in monocular video sequences based on observations encoding the appearance of image silhouettes. Our approach is discriminative rather than generative, therefore it does not require the probabilistic inversion of a predictive observation model. Instead, it uses a large human(More)
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State-of-the-art single image deblurring techniques are sensitive to image noise. Even a small amount of noise, which is inevitable in low-light conditions, can degrade the quality of blur kernel estimation dramatically. The recent approach of Tai and Lin [17] tries to iteratively denoise and deblur a blurry and noisy image. However, as we show in this(More)
The major challenge that faces American Sign Language (ASL) recognition now is developing methods that will scale well with increasing vocabulary size. Unlike in spoken languages, phonemes can occur simultaneously in ASL. The number of possible combinations of phonemes is approximately 1.5 × 10 9 , which cannot be tackled by conventional hidden Markov(More)
The major challenge that faces American Sign Language (ASL) recognition now is to develop methods that will scale well with increasing vocabulary size. Unlike in spoken languages , phonemes can occur simultaneously in ASL. The number of possible combinations of phonemes after enforcing linguistic constraints is approximately 5:5 10 8 : Gesture recognition,(More)
We describe algorithms for recognizing human motion in monocular video sequences, based on discriminative Conditional Random Fields (CRF) and Maximum Entropy Markov Models (MEMM). Existing approaches to this problem typically use gen-erative structures like the Hidden Markov Model (HMM). Therefore they have to make simplifying, often unrealistic assumptions(More)
Optical flow provides a constraint on the motion of a deformable model. We derive and solve a dynamic system incorporating flow as a hard constraint, producing a model-based least-squares optical flow solution. Our solution also ensures the constraint remains satisfied when combined with edge information, which helps combat tracking error accumulation.(More)