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Geodesic Active Contours
A novel scheme for the detection of object boundaries based on active contours evolving in time according to intrinsic geometric measures of the image, allowing stable boundary detection when their gradients suffer from large variations, including gaps. Expand
Online Learning for Matrix Factorization and Sparse Coding
A new online optimization algorithm is proposed, based on stochastic approximations, which scales up gracefully to large data sets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems. Expand
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS
LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It isExpand
Online dictionary learning for sparse coding
A new online optimization algorithm for dictionary learning is proposed, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples, and leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets. Expand
Image inpainting
A novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators, and does not require the user to specify where the novel information comes from. Expand
Non-local sparse models for image restoration
Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost. Expand
Classification and clustering via dictionary learning with structured incoherence and shared features
A clustering framework within the sparse modeling and dictionary learning setting is introduced, using a novel measurement for the quality of the sparse representation, inspired by the robustness of the ℓ1 regularization term in sparse coding. Expand
Sparse Representation for Computer Vision and Pattern Recognition
This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study. Expand
Sparse Representation for Color Image Restoration
This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper. Expand
Supervised Dictionary Learning
A novel sparse representation for signals belonging to different classes in terms of a shared dictionary and discriminative class models is proposed, with results on standard handwritten digit and texture classification tasks. Expand