Geodesic Active Contours
- V. Caselles, R. Kimmel, G. Sapiro
- Computer ScienceProceedings of IEEE International Conference on…
- 20 June 1995
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.
Online Learning for Matrix Factorization and Sparse Coding
- J. Mairal, F. Bach, J. Ponce, G. Sapiro
- Computer ScienceJournal of machine learning research
- 1 August 2009
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.
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS
- M. Weinberger, G. Seroussi, G. Sapiro
- Computer ScienceIEEE Transactions on Image Processing
- 1 August 2000
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 is…
Online dictionary learning for sparse coding
- J. Mairal, F. Bach, J. Ponce, G. Sapiro
- Computer ScienceInternational Conference on Machine Learning
- 14 June 2009
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.
Image inpainting
- M. Bertalmío, G. Sapiro, V. Caselles, C. Ballester
- ArtInternational Conference on Computer Graphics and…
- 1 July 2000
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.
Non-local sparse models for image restoration
- J. Mairal, F. Bach, J. Ponce, G. Sapiro, Andrew Zisserman
- Computer ScienceIEEE International Conference on Computer Vision
- 1 September 2009
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.
Deep Video Deblurring for Hand-Held Cameras
- Shuochen Su, M. Delbracio, Jue Wang, G. Sapiro, W. Heidrich, O. Wang
- Computer ScienceComputer Vision and Pattern Recognition
- 21 July 2017
This work introduces a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames, and shows that the features learned extend todeblurring motion blur that arises due to camera shake in a wide range of videos.
Classification and clustering via dictionary learning with structured incoherence and shared features
- Ignacio Francisco Ramírez Paulino, P. Sprechmann, G. Sapiro
- Computer ScienceIEEE Computer Society Conference on Computer…
- 13 June 2010
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.
Sparse Representation for Computer Vision and Pattern Recognition
- John Wright, Yi Ma, J. Mairal, G. Sapiro, Thomas S. Huang, Shuicheng Yan
- Computer ScienceProceedings of the IEEE
- 29 April 2010
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.
See all by looking at a few: Sparse modeling for finding representative objects
- Ehsan Elhamifar, G. Sapiro, R. Vidal
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 16 June 2012
The proposed framework and theoretical foundations are illustrated with examples in video summarization and image classification using representatives and can be extended to detect and reject outliers in datasets, and to efficiently deal with new observations and large datasets.
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