Journal Information
Computer Vision and Image Understanding (CVIU)
Impact Factor:

Call For Papers
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.

Research Areas Include:

• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems
Last updated by Dou Sun in 2019-01-05
Special Issues
Special Issue on Adversarial Deep Learning in Biometrics & Forensics
Submission Date: 2019-10-01

SCOPE In the short course of a few years, deep learning has changed the rules of the game in a wide array of scientific disciplines, achieving state-of-the-art performance in major pattern recognition application areas. Notably, it has been used recently even in fields like image biometrics and forensics (e.g. face recognition, forgery detection and localization, source camera identification, etc). However, recent studies have shown their vulnerability to adversarial attacks: a trained model can be easily deceived by introducing a barely noticeable perturbation in the input image. Such a weakness is obviously more critical for security-related applications calling for possible countermeasures. Indeed, adversarial deep learning will create high impact in the field of Biometrics and Forensics in the near future. The aim of this special issue is hence to gather innovative contributions on methods able to resist adversarial attacks on deep neural networks applied both in image biometrics and forensics. Therefore, it will encourage proposals of novel approaches and more robust solutions. TOPICS Submissions are encouraged, but not limited, to the following topics: Adversarial biometric recognition Attack transferability in biometric applications Physical attacks in biometric authentication systems Attacks to person re-identification systems Poisoned enrollment datasets Multimodal biometric systems as a defense Blind defense at test time for forensic and biometric systems Novel counter-forensics methods Design of robust forgery detectors Adversarial patches in forensic applications Image anonymization Adversarial attack and defense in video forensics Steganography and steganalysis in adversarial settings Cryptography-based methods
Last updated by Dou Sun in 2019-06-09
Special Issue on Deep Learning for Image Restoration
Submission Date: 2019-12-15

Scope Recent years have witnessed significant advances in image restoration and related low-level vision problems due to the use of kinds of deep models. The image restoration methods based on deep models do not need statistical priors and achieve impressive performance. However, there still exist several problems. For example, 1) synthesizing realistic degraded images as the training data for neural networks is quite challenging as it is difficult to obtain image pairs in real-world applications; 2) as the deep models are usually based on black-box end-to-end trainable networks, it is difficult to analyze which parts really help the restoration problems; 3) using deep neural networks to model the image formation process is promising but still lacks efficient algorithms; 4) the accuracy and efficiency for real-world applications still see a large room for improvement. This special issue provides a significant collective contribution to this field and focuses on soliciting original algorithms, theories and applications for image restoration and related low-level vision problems. Specifically, we aim to solicit the research papers that 1) propose theories related to deep learning for image restoration and related problems; 2) develop state-of-the-art algorithms for real-world applications; 3) present thorough literature reviews/surveys about the recent progress in this field; 4) establish real-world benchmark datasets for image restoration and related low-level vision problems. Topics Topics of interest include, but are not limited to: Theory: Deep learning Generative adversarial learning Weakly supervised learning Semi-supervised learning Unsupervised learning Algorithms and applications: Image/video deblurring, denoising, super-resolution, dehazing, deraining, etc. Image/video filtering, editing, and analysis Image/video enhancement and other related low-level vision problems Low-quality image analysis and related high-level vision problems
Last updated by Dou Sun in 2019-06-09
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