Journal Information
Pattern Recognition (PR)
http://www.journals.elsevier.com/pattern-recognition/
Impact Factor:
3.962
Publisher:
ELSEVIER
ISSN:
0031-3203
Viewed:
10546
Tracked:
55

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Call For Papers
Pattern Recognition is the official journal of the Pattern Recognition Society. The Society was formed to fill a need for information exchange among research workers in the pattern recognition field. Up to now, we ''pattern-recognitionophiles'' have been tagging along in computer science, information theory, optical processing techniques, and other miscellaneous fields. Because this work in pattern recognition presently appears in widely spread articles and as isolated lectures in conferences in many diverse areas, the purpose of the journal Pattern Recognition is to give all of us an opportunity to get together in one place to publish our work. The journal will thereby expedite communication among research scientists interested in pattern recognition.

We consider pattern recognition in the broad sense, and we assume that the journal will be read by people with a common interest in pattern recognition but from many diverse backgrounds. These include biometrics, target recognition, biological taxonomy, meteorology, space science, classification methods, character recognition, image processing, industrial applications, neural computing, and many others.

The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition.
Last updated by Wang Yu in 2019-01-22
Special Issues
Special Issue on Pattern Recognition Methods for Bridging the Vision-Language Gap in Multimodal Data Analysis
Submission Date: 2020-02-12

The explosive growth of visual and textual data (both on the WorldWideWeb and held in private repositories by diverse institutions and companies) has led to urgent requirements in terms of searching, processing and understanding of multimedia content, by a machine. Solutions for providing access to and understanding such multimodal source data depend on bridging the semantic gap between vision and language. To solve this problem calls for expertise from the cognate fields of computer vision, image processing, text and document analysis, machine learning and pattern recognition. This problem also finds applications in the fast-emerging areas of multimedia data analysis and cross-modality learning. In this special issue, we aim to assemble recent advances in pattern recognition relevant to the vision-and-language problem, encompassing big-data applications involving multimedia data and deep learning algorithms. The scope of the call for papers covers the use of pattern recognition and machine learning techniques for understanding cross-modal information, especially to those involving vision-and-language. Both original research as well as state-of-the-art literature reviews, are welcome for submission. However, submitted papers must be within the scope of the Pattern Recognition Journal, advancing the available pattern recognition methodology in this domain. Papers outside the remit of the journal will be rejected without review. The list of possible topics includes, but is not limited to: Novel pattern recognition and machine learning methods which combine language and vision Pattern recognition and machine learning for visual captioning, dialogue, and question answering Sequence learning towards bridging vision, language and multimedia data Language as an inference mechanism for structuring and reasoning about visual perception Transfer learning across multimodal data Pattern recognition for visual synthesis from language Semantic scene graph generation from images with pattern recognition and machine learning methods Cross-modality pattern recognition and machine learning for representation and learning, retrieval and generation, and zero/few-shot learning. Pattern recognition and machine learning for multimedia data analysis and understanding
Last updated by Dou Sun in 2019-09-14
Special Issue on Modeling and Learning for Matching: Models, Methods and Applications
Submission Date: 2020-12-30

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.
Last updated by Dou Sun in 2019-03-31
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