For our project, we apply the method of the alternating direction of multipliers and sequential convex optimization to sparse coding of images. The motivation behind sparse coding of images is to model how the brain is able to efficiently utilize the human visual system for a variety of tasks, such as separating a car from a background, as well as general classification tasks. Sparse coding aims to determine
Abstract. Images can be coded accurately using a sparse set of vectors from an overcomplete dictiona...
We consider the problem of lossy compression of images using sparse representations from overcomplet...
Many existing works related to lossy-to-lossless image compression are based on the lifting concept....
Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given ...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
Sparse coding is a basic task in many fields including signal processing, neuroscience and machine l...
Image processing problems have always been challenging due to the complexity of the signal. These pr...
Sparse coding is a promising theme in computer vision. Most of the existing sparse coding methods ar...
Example-based learning of codes that statistically encode general image classes is of vital importan...
Sparse coding has become an increasingly popular method in learning and vision for a variety of clas...
International audienceDictionary learning for sparse representations is generally conducted in two a...
International audienceThis work focuses on several optimization problems involved in recovery of spa...
We propose recovering 1D piecewice linear signal using a sparsity-based method consisting of two ste...
Abstract. Images can be coded accurately using a sparse set of vectors from a learned overcomplete d...
Abstract. Images can be coded accurately using a sparse set of vectors from an overcomplete dictiona...
We consider the problem of lossy compression of images using sparse representations from overcomplet...
Many existing works related to lossy-to-lossless image compression are based on the lifting concept....
Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given ...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
Sparse coding is a basic task in many fields including signal processing, neuroscience and machine l...
Image processing problems have always been challenging due to the complexity of the signal. These pr...
Sparse coding is a promising theme in computer vision. Most of the existing sparse coding methods ar...
Example-based learning of codes that statistically encode general image classes is of vital importan...
Sparse coding has become an increasingly popular method in learning and vision for a variety of clas...
International audienceDictionary learning for sparse representations is generally conducted in two a...
International audienceThis work focuses on several optimization problems involved in recovery of spa...
We propose recovering 1D piecewice linear signal using a sparsity-based method consisting of two ste...
Abstract. Images can be coded accurately using a sparse set of vectors from a learned overcomplete d...
Abstract. Images can be coded accurately using a sparse set of vectors from an overcomplete dictiona...
We consider the problem of lossy compression of images using sparse representations from overcomplet...
Many existing works related to lossy-to-lossless image compression are based on the lifting concept....