Dimitris N. Metaxas

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We present a comprehensive methodology for realistically animating liquid phenomena Physically accurate D mo tion is achieved by performing a two stage calculation over an arbitrary environment of static obstacles surrounded by uid A nite di erence approximation to the Navier Stokes equations is rst applied to a low resolution vox elized representation of(More)
Recent image retrieval algorithms based on local features indexed by a vocabulary tree and holistic features indexed by compact hashing codes both demonstrate excellent scalability. However, their retrieval precision may vary dramatically among queries. This motivates us to investigate how to fuse the ordered retrieval sets given by multiple retrieval(More)
Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity(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)
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)
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× 109, which cannot be tackled by conventional hidden Markov(More)
This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. For face detection, we propose a group sparse learning method to automatically select the most salient facial(More)