Journal Information
Image and Vision Computing (IVC)
http://www.journals.elsevier.com/image-and-vision-computing/
Impact Factor:
2.747
Publisher:
ELSEVIER
ISSN:
0262-8856
Viewed:
8215
Tracked:
16

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Call For Papers
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.

In addition to regular manuscripts, Image and Vision Computing Journal solicits manuscripts for the Opinions Column, aimed at initiating a free forum for vision researchers to express their opinions on past, current, or future successes and challenges in research and the community.

An opinion paper should be succinct and focused on a particular topic. Addressing multiple related topics is also possible if this helps making the point. While posing questions helps raising awareness about certain issues, ideally, an opinion paper should also suggest a concrete direction how to address the issues. Topics of interest include, but are not limited to:

    Comments on success and challenges in a (sub-) field of computer vision,
    Remarks on new frontiers in computer vision
    Observations on current practices and trends in research, and suggestions for overcoming unsatisfying aspects
    Observations on current practices and trends in the community regarding, e.g., reviewing process, organizing conferences, how journals are run, and suggestions for overcoming unsatisfying aspects
    Reviews of early seminal work that may have fallen out of fashion
    Summaries of the evolution of one's line of research
    Recommendations for educating new generations of vision researchers.
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Advances in Domain Adaptation for Computer Vision
Submission Date: 2020-05-31

In daily routines, humans, not only learn and apply knowledge for visual tasks but also have intrinsic abilities to transfer knowledge between related vision tasks. For example, if a new vision task is relevant to any previous learning, it is possible to transfer the learned knowledge for handling the new vision task. In developing new computer vision algorithms, it is desired to utilize these capabilities to make the algorithms adaptable. Generally, traditional computer vision methods do not adapt to a new task and have to learn the new task from the beginning. These methods do not consider that the two visual tasks may be related and the knowledge gained in one may be applied to learn the other one efficiently in lesser time. Domain adaptation for computer vision is the area of research, which attempts to mimic this human behavior by transferring the knowledge learned in one or more source domains and use it for learning the related visual processing task in the target domain. Recent advances in domain adaptation, particularly in cotraining, transfer learning, and online learning have benefited computer vision research significantly. For example, learning from high-resolution source domain images and transferring the knowledge to learning low-resolution target domain information. This special issue will focus on the recent advances in domain adaptation for different computer vision tasks. Topics of interest include, but are not limited to: Domain adaptation for machine Learning frameworks for learning deep representations Domain adaptation for face detection/recognition and tracking Domain adaptation for object detection/ recognition and tracking Domain adaptation and hybrid models for real-time computer vision tasks Domain adaptation for human pose detection/recognition and estimation Domain adaptation for event/action detection and recognition Domain adaptation for few-shot learning Domain adaptation for deep neural network optimization
Last updated by Dou Sun in 2020-01-04
Special Issue on Deep Learning for Panoramic Vision on Mobile Devices
Submission Date: 2020-05-31

In the last few years, remarkable progress was made with mobile consumer devices. Modern smartphones and tablet computers offer multi-core processors and graphics processing cores which open up new application possibilities such as deep learning-based computer vision tasks. Deep learning has received much attention from the communities of computer vision and computer graphics due to its excellent representativeness for images. With the development of deep learning theory and technology, the performance of computer vision algorithms including object detection and tracking, face recognition, and 3D reconstruction have made tremendous progress. However, computer vision technology relies on the valid information from the input image and video, and the performance of the algorithm is essentially constrained by the quality of source image/video. Panorama plays important role in capturing large-scale dynamic scenes for both macro and micro domains. Benefited from the recent progress of panoramic cameras, the capture of panoramic image/video becomes more and more convenient. In particular, along with the emergence of panoramic image/video, the corresponding computer vision tasks remain unsolved, due to the extremely high-resolution, large-scale, huge-data that induced by the panoramic camera. Moreover, with development of mobile devices (mobile phones with GPU), their computing power become more and more powerful. However, the research computer vision technology on mobile devices is relatively backward, especially panorama-based computer vision technology. As a result, the recent diffusion of deep learning and the development of modern mobile devices have encouraged computer vision community to explore new solutions for panorama-based computer vision tasks on mobile devices. This special session aims to bring the latest results over advanced deep learning techniques for panoramic vision on mobile devices. It can help technicians to exchange the latest technical progresses.
Last updated by Dou Sun in 2020-01-04
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