Journal Information
International Journal of Computer Vision
http://www.springer.com/computer/image+processing/journal/11263
Impact Factor:
8.222
Publisher:
Springer
ISSN:
0920-5691
Viewed:
6215
Tracked:
12

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Call For Papers
The International Journal of Computer Vision (IJCV) provides a forum for the dissemination of new research results in the rapidly growing field of computer vision. Now publishing 15 issues a year, International Journal of Computer Vision presents high-quality, original contributions to the science and engineering of this rapidly growing field.

Regular articles (up to 25 journal pages) present major technical advances of broad general interest.
Short articles (up to 10 pages) provide a fast publication path for novel research results.
Survey articles (up to 30 pages) offer critical reviews of the state of the art and/or tutorial presentations of pertinent topics.
Book reviews, position papers, and editorials by leading scientific figures will from time to time complement the technical content of the journal.
Additional, on-line material such as still images and video sequences, data sets, and software is encouraged.

Papers should cover:

Mathematical, physical and computational aspects of computer vision: image formation, processing, analysis, and interpretation; machine learning techniques; statistical approaches; sensors.
Applications: image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media, medicine, human-machine interaction, surveillance.
Connections with human perception: computational and architectural aspects of human vision.

EDITORIAL POLICIES:

There are no page charges in IJCV.
Papers are published on line in advance of print publication.
Responses to position papers are welcome.
Editorials, position papers, and their responses will be available online free of charge.
IJCV has a liberal copyright policy.

Academic and industrial researchers in computer vision, robotics, and artificial intelligence, as well as psychologists and neuroscientists interested in the connection between computer and human vision, will find IJCV to be the essential forum for important results in the field.
Last updated by Dou Sun in 2017-09-15
Special Issues
Special Issue on Deep Learning for Face Analysis
Submission Date: 2018-01-15

Deep learning is one of the most important breakthroughs in the field of artificial intelligence over the last decade. It has achieved great success in speech recognition, natural language processing, computer vision, and multimedia. Many face analysis tasks, including face detection, alignment, reconstruction, and recognition, benefit from the powerful representation learning capability of deep learning techniques. Not only there has been a constantly growing flow of related research papers, but also substantial progress has been achieved in real-world applications such as security, video surveillance, and human-computer interaction. While substantial progress has been achieved in face analysis with deep learning, many issues still remain and new problems emerge. For instance, the scalability of deep networks to large-scale unconstrained recognition needs be improved. In-the-wild facial attributes recognition with imbalance class distribution is still challenging. The accuracy and efficiency of detecting faces with a wide range of scales in a crowded scene still see a large room for improvement. This special issue presents a great platform to make a definitive statement about the state of the art by providing a significant collective contribution to this emerging field of study. Specifically, we aim to solicit original contributions that: (1) present state-of-the-art theories related to deep learning for face analysis; (2) develop novel methods and applications; (3) survey the recent progress in this area; and (4) establish benchmark datasets. Topics of Interest The list of possible topics includes, but is not limited to: • Theory o Deep learning o Cross-domain feature learning and fusion o Transfer learning o Multitask learning o Generative adversarial learning o Multi-instance learning o Weakly supervised learning o Reinforcement learning o Zero-shot / One-shot learning • Applications o Face detection o Face alignment and tracking o Face recognition o Face verification o Face clustering o Face attribute recognition (including age and gender) o Facial expression recognition o Face hallucination and completion o 3D face reconstruction o Face parsing o Face sketch synthesis and recognition
Last updated by Dou Sun in 2017-10-13
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