Journal Information
Image and Vision Computing (IVC)
http://www.journals.elsevier.com/image-and-vision-computing/
Impact Factor:
2.747
Publisher:
Elsevier
ISSN:
0262-8856
Viewed:
9906
Tracked:
20

Call For Papers
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.

In addition to regular manuscripts, Image and Vision Computing Journal solicits manuscripts for the Opinions Column, aimed at initiating a free forum for vision researchers to express their opinions on past, current, or future successes and challenges in research and the community.

An opinion paper should be succinct and focused on a particular topic. Addressing multiple related topics is also possible if this helps making the point. While posing questions helps raising awareness about certain issues, ideally, an opinion paper should also suggest a concrete direction how to address the issues. Topics of interest include, but are not limited to:

    Comments on success and challenges in a (sub-) field of computer vision,
    Remarks on new frontiers in computer vision
    Observations on current practices and trends in research, and suggestions for overcoming unsatisfying aspects
    Observations on current practices and trends in the community regarding, e.g., reviewing process, organizing conferences, how journals are run, and suggestions for overcoming unsatisfying aspects
    Reviews of early seminal work that may have fallen out of fashion
    Summaries of the evolution of one's line of research
    Recommendations for educating new generations of vision researchers.
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Learning with Manifolds in Computer Vision
Submission Date: 2021-01-31

Manifold Learning (ML) has been the subject of intensive study over the past two decades in the computer vision and machine learning communities. Originally, manifold learning techniques aim to identify the underlying structure (usually low-dimensional) of data from a set of, typically high-dimensional, observations. The recent advances in deep learning make one wonder whether data-driven learning techniques can benefit from the theoretical findings from ML studies. This innocent looking question becomes more important if we note that deep learning techniques are notorious for being data-hungry and (mostly) supervised. On the contrary, many ML techniques unravel data structures without much supervision. This special issue aims at raising the question of how classical ML techniques can help deep learning and vice versa, and targets works and studies investigating how to bridge the gap. Besides, the use of Riemannian geometry in tackling/modelling various problems in computer vision has seen a surge of interest recently. The benefits of geometrical thinking can be understood by noting that in many applications, data naturally lies on smooth manifolds, hence distances and similarity measures computed by considering the geometry of the space naturally result in better and more accurate modelling. Various studies demonstrate the benefits of geometrical techniques in analysing images and videos such as face recognition, activity classification, object detection and classification, and structure from motion to name a few. This special issue addresses challenges and future directions related to the application of non-linear manifold and machine learning in computer vision. Topics and Guidelines This special issue targets researchers and practitioners from both industry and academia to provide a forum in which to publish recent state-of-the-art achievements in Non-Euclidean geometry and machine learning for computer vision. Topics of interest include, but are not limited to: ● Theoretical Advances related to manifold learning ● Dimensionality Reduction (e.g., Locally Linear Embedding, Laplacian Eigenmaps) ● Clustering ● Kernel methods ● Metric Learning ● Time series on non-linear manifolds ● Transfer learning on non-linear manifolds ● Generative Models on non-linear manifolds ● Subspace Methods ● Advanced Optimization Techniques (constrained and non-convex optimization techniques on non-linear manifolds) ● Mathematical Models for learning sequences ● Mathematical Models for learning Shapes ● Deep learning and non-linear manifolds ● Low-rank factorization methods ● Graph-based Analysis ● Learning via Hyperbolic geometry And related applications in computer vision (a non-exhaustive list in provided below): ● Face recognition ● Image/video analysis and classification ● Action/activity recognition ● Behavior analysis ● Facial expressions recognition ● Person Re-Identification ● Face generation ● Facial expression generation ● Fine-grained recognition ● Visual inspection
Last updated by Dou Sun in 2020-08-11
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