Journal Information
Journal of Visual Communication and Image Representation (JVCIR)
http://www.journals.elsevier.com/journal-of-visual-communication-and-image-representation/
Impact Factor:
2.259
Publisher:
Elsevier
ISSN:
1047-3203
Viewed:
8872
Tracked:
14

Call For Papers
The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.

Research Areas include:

• Image scanning, sampling, and tessellation
• Image representation by partial information
• Local and global schemes of image representation
• Analog and digital image processing
• Fractals and mathematical morphology
• Image understanding and scene analysis
• Deterministic and stochastic image modeling
• Visual data reduction and compression
• Image coding and video communication
• Biological and medical imaging
• Early processing in biological visual systems
• Psychophysical analysis of visual perception
• Astronomical and geophysical imaging
• Visualization of nonlinear natural phenomena
• real-time imaging
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Deep Low-Rank and Sparse Analytics for Robust Visual Intelligence
Submission Date: 2020-10-20

Sparse/low-rank analytics and representation have been emerging as important topics for robust image processing and visual representation due to their great success to image restoration, de-noising and classification, etc. Although recent decade has witnessed lots of efforts on the study of sparse/low-rank analytics, and significant progress were made to improve the representation ability, some issues still remain unsolved. For example, sparse/low-rank representation algorithms usually utilize the single-layer structures, so they fail to obtain the deep representations with more useful and valuable hidden hierarchical information discovered. With the fast development of deep learning and deep neural networks, it will be helpful to propose the deep/multi-layer sparse and low-rank representation frameworks for robust visual representation. It is known that both deep learning and sparse/low-rank coding are powerful representation learning systems based on different mechanisms and principles, but how to integrate them to improve the performance is still unclear and noteworthy exploring, which is the main goal of this special issue. Although certain efforts have been made to incorporate the deep neural networks into sparse/low-rank analytics, most designs of so-called deep frameworks are still less straightforward. For example, some algorithms use deep features of deep models for sparse/low-rank analytics, or perform the sparse/low-rank analytics firstly and use the recovered data for deep learning. Although certain deep features or representations can be obtained by this kind of deep sparse/low-rank analytics, they still suffer from some drawbacks. For example, they only simply add together multiple shallow sparse/low-rank coding layers, so current models still cannot produce accurate representations of visual data. Thus, it is now necessary to explore advanced integrated deep sparse/low-rank coding algorithms and theories for robust visual representation. In this special issue, we solicit original research papers from diverse research fields, developing new deep sparse/low-rank analytics model for representing and understanding visual data, which aims to reduce the gap between sparse/low-rank coding and deep learning. The topics of interest include, but are not limited to: Survey/vision/review of sparse/low-rank visual analytics Deep/multi-layer sparse coding or low-rank coding Relations between sparse/low-rank coding and deep learning Deep representation learning Deep sparse or low-rank coding neural network Convolutional sparse/low-rank coding Robust sparse/low-rank subspace discovery Theory and optimization for deep representation learning Applications to robust image processing (e.g., restoration and de-noising) and recognition
Last updated by Dou Sun in 2020-07-30
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