Sparse coding based visual tracking: Review and experimental comparison
Recently, sparse coding has been successfully applied in visual tracking. The goal of this
paper is to review the state-of-the-art tracking methods based on sparse coding. We first …
paper is to review the state-of-the-art tracking methods based on sparse coding. We first …
Time-series classification methods: Review and applications to power systems data
Chapter Overview The diffusion in power systems of distributed renewable energy
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT
Abstract The Artificial Intelligence Internet of Things (AIoT) is an emerging concept aiming to
perceive, understand and connect the 'intelligent things' to make the intercommunication of …
perceive, understand and connect the 'intelligent things' to make the intercommunication of …
Removing rain from a single image via discriminative sparse coding
Visual distortions on images caused by bad weather conditions can have a negative impact
on the performance of many outdoor vision systems. One often seen bad weather is rain …
on the performance of many outdoor vision systems. One often seen bad weather is rain …
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …
processing, image processing, computer vision, and pattern recognition. Sparse …
A survey of deep learning methods and software tools for image classification and object detection
PN Druzhkov, VD Kustikova - Pattern Recognition and Image Analysis, 2016 - Springer
Deep learning methods for image classification and object detection are overviewed. In
particular we consider such deep models as autoencoders, restricted Boltzmann machines …
particular we consider such deep models as autoencoders, restricted Boltzmann machines …
Convolutional neural networks analyzed via convolutional sparse coding
Convolutional neural networks (CNN) have led to many state-of-the-art results spanning
through various fields. However, a clear and profound theoretical understanding of the …
through various fields. However, a clear and profound theoretical understanding of the …
A statistical perspective on algorithmic leveraging
One popular method for dealing with large-scale data sets is sampling. Using the empirical
statistical leverage scores as an importance sampling distribution, the method of algorithmic …
statistical leverage scores as an importance sampling distribution, the method of algorithmic …
Top-down visual saliency via joint CRF and dictionary learning
J Yang, MH Yang - IEEE transactions on pattern analysis and …, 2016 - ieeexplore.ieee.org
Top-down visual saliency is an important module of visual attention. In this work, we propose
a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a …
a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a …
Spectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model
A novel superpixel-based discriminative sparse model (SBDSM) for spectral-spatial
classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is …
classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is …