Modeling and Learning for Matching: Models, Methods and Applications

Aim and Scope:

The special issue will focus on the recent advance in modeling and learning to solve the matching problem in pattern recognition. The capability of finding correspondence among images, graphics, point sets and other structures has been a fundamental in many pattern recognition. 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 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 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 matching problem, which often calls for more careful treatments on the local and global structures. Also, there are emerging methods for discovering latent graph structures that enrich the applicability for graph matching. In this special issue, we attempt to assemble recent advances in the matching 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.

Main Topics of Interest (but are not limited to):

Object matching: 1) Graph representation and modeling using image/graphics data; 2) Robust matching/registration for visual correspondences over two or multiple images/graphics/point sets; 3) Partial, one-to-many/many-to-many matching, in the presence of major noise and outliers; 4) Similarity between graphs/graphics and graph clustering/classification; 5) Cross-network matching, e.g. social networks and other forms e.g. protein network; 6) Incremental matching of a series of objects; 7) Shape matching.

Tracking and optical flow estimation: 1) Single/multiple object tracking and data association; 2) Robust and/or efficient optical flow methods; 3) Object co-detection; 4) Visual trajectory generation and modeling; 5) Person Re-ID; 6) Planar Object Tracking

Correspondence for 3-D vision: 1) Calibration, and pose estimation; 2) visual SLAM; 3) Depth estimation and 3-D reconstruction;

Learning for/by permutation and 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; 4) Learning for matching and permutation.

Structure discovery from data: 1) structure inference from behavior data e.g. time series and event sequence; 2) latent structure matching based on behavior data

Applications: Application of matching technology to solve any real-world visual 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.

Important dates:

Submission deadline: November 15, 2019- December 30, 2019

First review notification: April 30, 2020

Revised submission due: August 31, 2020

Final acceptance: October 31, 2020

Organizers:

Bin Luo

Professor

Anhui University

(https://scholar.google.com/citations?user=0qaDapcAAAAJ&hl=zh-CN)

Email: luobin@ahu.edu.cn

Junchi Yan

Research Professor

Shanghai Jiao Tong University, China

(http://thinklab.sjtu.edu.cn)

Email: yanjunchi@sjtu.edu.cn

Bo Jiang

Associate Professor

Anhui University

(http://www.escience.cn/people/jiangboahu/index.html)

Email: jiangbo@ahu.edu.cn

Francesc Serratosa

Professor

Universitat Rovira i Virgili

(http://deim.urv.cat/~francesc.serratosa/)

Email: francesc.serratosa@urv.cat

Deepti Pachauri

Researcher

3M Corporate Research Lab, USA

(http://pages.cs.wisc.edu/~pachauri/)

Email: deepti.pachauri@gmail.com

Submission Guidelines:

Manuscript must be within the scope of Pattern Recognition journal. Manuscript preparation guidelines are available at https://www.elsevier.com/journals/pattern-recognition/0031-3203/guide-for-authors. All papers will be handled via Elsevier Editorial System: http://ees.elsevier.com/pr. Please select \”VSI: MLM\” when selecting article type name during the submission process.