Special Issue on "AVC-MAA: Advances in Visual Correspondence: Models, Algorithms and Applications"
摘要截稿:
全文截稿: 2018-05-31
影响因子: 3.255
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 3区
• 小类 : 计算机:人工智能 - 3区
Overview
Visual correspondence is a key problem in many computer vision and pattern recognition tasks. The past decades have witnessed the rapid expansion of the frontier for automatic correspondence establishment among images/graphics, which is largely due to the advances in computational capacity, data availability and new algorithmic paradigms. Although the visual correspondence problem has been extensively studied in the context of multi-view geometry, its more generalized forms, along with underlying connections among different methods and settings, have not been fully explored. Meanwhile, the combination of big visual data and the deep learning paradigm has achieved significant success in many perceptual tasks; however, the existing paradigm is still far from a panacea to the correspondence problem, which often calls for more careful treatments on the local and global structures. In this special issue, we attempt to assemble recent advances in the correspondence problem, considering the explosions of big visual data applications and the deep learning algorithms.
This special issue will feature original research papers related to the models and algorithms for robust establishment of correspondence, together with applications to real-world problems. The main topics of interest (but are not limited to):
-- Graph matching and image registration: 1) Graph representation and modeling by using image/ graphics data; 2) Robust matching/registration theory and approaches for establishing visual correspondences over two or more images/graphics; 3) Partial, one-to-many or many-to-many matching models and algorithms, especially with major noise and outliers; 4) Similarity between graphs/graphics and graph clustering/classification. 5) Cross-network matching.
-- Tracking and optical flow: 1) Multiple object tracking and association; 2) Robust and/or efficient optical flow methods; 3) Visual trajectory analytics; 4) Person Re-ID.
-- Correspondence for 3-D vision: 1) Calibration, pose estimation and visual SLAM; 2) Depth estimation and 3-D reconstruction.
-- Learning for/by matching: 1) Learning graph structure and similarity from data with established or unestablished correspondences; 2) Learning image feature representation from established or loosely established correspondence; 3) Common/similar objects discovery and recognition from images.
-- Applications: Application of correspondence technology to solve any real-world image understanding problems including object detection/recognition among images/graphics, image stitching, 2-D/3-D recovery, robot vision, photogrammetry and remote sensing, industrial imaging, embed system etc.