Perceptual image quality assessment (IQA) and sparse signal representation have recently emerged as high-impact research topics in the field of image processing. Here we make one...
Abdul Rehman, Zhou Wang, Dominique Brunet, Edward ...
In this paper, a new class of audio representations is introduced, together with a corresponding fast decomposition algorithm. The main feature of these representations is that th...
The Common Spatial Pattern (CSP) method is a powerful technique for feature extraction from multichannel neural activity and widely used in brain computer interface (BCI) applicat...
Consider the decomposition of a signal into features that undergo transformations drawn from a continuous family. Current methods discretely sample the transformations and apply s...
Chaitanya Ekanadham, Daniel Tranchina, Eero P. Sim...
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. ...
This paper presents a simple randomised algorithm for recovering high-dimensional sparse functions, i.e. functions f : [0, 1]d → R which depend effectively only on k out of d va...
This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulate...
Jie Yang, Abdesselam Bouzerdoum, Fok Hing Chi Tivi...
Online learned tracking is widely used for it’s adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of erro...
Abstract--An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of ...
The sparse data is becoming increasingly common and available in many real-life applications. However, relative little attention has been paid to effectively model the sparse data ...