Volume 26 | Issue 11

IEEE Transactions on Circuits and Systems for Video Technology covers all aspects of visual information relating to video or that have the potential to impact future developments in the field of video technology and video systems, including but not limited to:

  1. (a) image/video processing: acquisition, representation, display, processing, transform, filtering, enhancement, restoration, watermarking;
  2. (b) image/video analysis and computer vision: characterization, classification, detection, tracking, assessment, segmentation, summarization, understanding, motion estimation, feature extraction, machine learning, machine intelligence, pattern analysis, pattern recognition, neural networks;
  3. (c) image/video compression: quantization, compression, quality assessment, rate control, error resilience, multiview, standards;
  4. (d) image/video communication: coding, streaming, distribution, interaction, networking, transport, wireless and mobile systems;
  5. (e) image/video storage: archives, networks, content management, databases, indexing, search, retrieval;
  6. (f) image/video hardware/software systems: architecture, hardware, software, multiprocessors, parallel processors, algorithms, VLSI, circuits, high-speed, real-time, low-power systems;
  7. (g) image/video applications: synthetic imaging, augmented imaging, video gaming, virtual reality, audio-visual systems, human-computer interaction, multimedia systems, multi-camera systems, surveillance, security, forensics, medical imaging, big data systems, cloud computing, and other video-related technologies.

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Dan Schonfeld
Editor in Chief - 2016 - 2017
ECE Dept. (M/C 154)
University of Illinois at Chicago (UIC)
851 S. Morgan Street – 1020 SEO
Chicago, Illinois

Dr. Shipeng Li
Deputy Editor-in-Chief
Microsoft Research Asia


Convolutional neural network (CNN) has achieved the state-of-the-art performance in many different visual tasks. Learned from a large-scale training data set, CNN features are much more discriminative and accurate than the handcrafted features. Moreover, CNN features are also transferable among different domains. On the other hand, traditional dictionary-based features (such as BoW and spatial... Read more on IEEE Xplore

How to build a robust and accurate appearance model is a crucial problem in object tracking. However, in most existing tracking methods, the structures among the adjacent video frames and neighboring regions, which may be helpful to improve the representation capability of the appearance model, have not been fully exploited. In this paper, we propose a novel tracking method by taking into account... Read more on IEEE Xplore

Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking. Structured learning has shown good results when applied to tracking whole targets, but applying this approach to a part-based target model is complicated by the need to model the relationships between parts and to avoid lengthy initialization processes. We thus... Read more on IEEE Xplore

A contrast enhancement method termed stratified parametric-oriented histogram equalization (SPOHE) is proposed to effectively provide a regional enhanced effect without visual artifacts, e.g., halo or blocking artifacts, which is normally incurred in the former simplified enhancement methods. First, the stratified sampling theory is applied to uniformly sample the original image through many... Read more on IEEE Xplore

Motion is one of the most important cues to separate foreground objects from the background in a video. Using a stationary camera, it is usually assumed that the background is static, while the foreground objects are moving most of the time. However, in practice, the foreground objects may show infrequent motions, such as abandoned objects and sleeping persons. Meanwhile, the background may... Read more on IEEE Xplore