Special Issue "Computer Vision and Image Processing"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 May 2022.

Special Issue Editors

Dr. Hemanth Venkateswara
E-Mail Website
Guest Editor
Computing Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
Interests: computer vision and machine learning; transfer learning; active learning; zero-shot learning
Dr. Chengcai Leng
E-Mail Website
Guest Editor
School of Mathematics, Northwest University, Xi'an 710127, China
Interests: image processing; computer vision and pattern recognition
Dr. Anup Basu
E-Mail Website
Guest Editor
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
Interests: computer vision; multimedia; distribution learning; image processing; active vision; perceptual factors

Special Issue Information

Dear Colleagues,

This special issue in Computer Vision and Image Processing will explore new directions integrating emerging and new techniques in machine learning and optimization. For example, we welcome contributions in Distribution Learning, Active Learning, Non-Negative Matrix Factorization and many related areas to process or understand images and videos. Topics of interest include, but are not limited to:

  • Image, video, and 3D scene processing
  • Remote sensing and satellite image processing
  • 3D imaging, visualization, animation, virtual reality and 3DTV
  • Classification, Clustering and Machine Learning for Multimedia
  • Image and Video processing and understanding for Smart Cars and Smart Homes
  • Perceptually guided imaging and vision
  • Neural networks and learning based optimization
  • Emerging techniques in learning for image, video and 3D vision

Dr. Hemanth Venkateswara
Dr. Chengcai Leng
Dr. Anup Basu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Computer Vision
  • Image Processing
  • Remote Sensing
  • InSAR Satellite Data Analysis
  • Distribution Learning
  • Active Learning
  • Smart Multimedia

Published Papers (1 paper)

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Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving
Remote Sens. 2021, 13(9), 1828; https://doi.org/10.3390/rs13091828 - 07 May 2021
Viewed by 319
Motion estimation is crucial to predict where other traffic participants will be at a certain period of time, and accordingly plan the route of the ego-vehicle. This paper presents a novel approach to estimate the motion state by using region-level instance segmentation and [...] Read more.
Motion estimation is crucial to predict where other traffic participants will be at a certain period of time, and accordingly plan the route of the ego-vehicle. This paper presents a novel approach to estimate the motion state by using region-level instance segmentation and extended Kalman filter (EKF). Motion estimation involves three stages of object detection, tracking and parameter estimate. We first use a region-level segmentation to accurately locate the object region for the latter two stages. The region-level segmentation combines color, temporal (optical flow), and spatial (depth) information as the basis for segmentation by using super-pixels and Conditional Random Field. The optical flow is then employed to track the feature points within the object area. In the stage of parameter estimate, we develop a relative motion model of the ego-vehicle and the object, and accordingly establish an EKF model for point tracking and parameter estimate. The EKF model integrates the ego-motion, optical flow, and disparity to generate optimized motion parameters. During tracking and parameter estimate, we apply edge point constraint and consistency constraint to eliminate outliers of tracking points so that the feature points used for tracking are ensured within the object body and the parameter estimates are refined by inner points. Experiments have been conducted on the KITTI dataset, and the results demonstrate that our method presents excellent performance and outperforms the other state-of-the-art methods either in object segmentation and parameter estimate. Full article
(This article belongs to the Special Issue Computer Vision and Image Processing)
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