Special Issue "Computer Vision and Image Processing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 28 February 2023 | Viewed by 3737

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 submissions that pass pre-check are 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 2500 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.

Keywords

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

Published Papers (4 papers)

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Research

Article
PFD-SLAM: A New RGB-D SLAM for Dynamic Indoor Environments Based on Non-Prior Semantic Segmentation
Remote Sens. 2022, 14(10), 2445; https://doi.org/10.3390/rs14102445 - 19 May 2022
Viewed by 416
Abstract
Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of [...] Read more.
Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of moving targets. Furthermore, the mathematical models are implemented at the feature-level of segmentation, which is likely to cause sub or over-segmentation of dynamic features. To address this problem, different from most feature-level dynamic segmentation based on mathematical models, a non-prior semantic dynamic segmentation based on a particle filter is proposed in this paper, which aims to attain the motion object segmentation. Firstly, GMS and optical flow are used to calculate an inter-frame difference image, which is considered an observation measurement of posterior estimation. Then, a motion equation of a particle filter is established using Gaussian distribution. Finally, our proposed segmentation method is integrated into the front end of visual SLAM and establishes a new dynamic SLAM, PFD-SLAM. Extensive experiments on the public TUM datasets and real dynamic scenes are conducted to verify location accuracy and practical performances of PFD-SLAM. Furthermore, we also compare experimental results with several state-of-the-art dynamic SLAM methods in terms of two evaluation indexes, RPE and ATE. Still, we provide visual comparisons between the camera estimation trajectories and ground truth. The comprehensive verification and testing experiments demonstrate that our PFD-SLAM can achieve better dynamic segmentation results and robust performances. Full article
(This article belongs to the Special Issue Computer Vision and Image Processing)
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Article
Traffic Anomaly Prediction System Using Predictive Network
Remote Sens. 2022, 14(3), 447; https://doi.org/10.3390/rs14030447 - 18 Jan 2022
Viewed by 438
Abstract
Anomaly anticipation in traffic scenarios is one of the primary challenges in action recognition. It is believed that greater accuracy can be obtained by the use of semantic details and motion information along with the input frames. Most state-of-the art models extract semantic [...] Read more.
Anomaly anticipation in traffic scenarios is one of the primary challenges in action recognition. It is believed that greater accuracy can be obtained by the use of semantic details and motion information along with the input frames. Most state-of-the art models extract semantic details and pre-defined optical flow from RGB frames and combine them using deep neural networks. Many previous models failed to extract motion information from pre-processed optical flow. Our study shows that optical flow provides better detection of objects in video streaming, which is an essential feature in further accident prediction. Additional to this issue, we propose a model that utilizes the recurrent neural network which instantaneously propagates predictive coding errors across layers and time steps. By assessing over time the representations from the pre-trained action recognition model from a given video, the use of pre-processed optical flows as input is redundant. Based on the final predictive score, we show the effectiveness of our proposed model on three different types of anomaly classes as Speeding Vehicle, Vehicle Accident, and Close Merging Vehicle from the state-of-the-art KITTI, D2City and HTA datasets. Full article
(This article belongs to the Special Issue Computer Vision and Image Processing)
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Article
Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction
Remote Sens. 2021, 13(19), 3990; https://doi.org/10.3390/rs13193990 - 06 Oct 2021
Viewed by 755
Abstract
Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissimilarity occur when acquiring multitemporal VHR satellite images [...] Read more.
Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissimilarity occur when acquiring multitemporal VHR satellite images owing to external environmental factors, and these errors cause various inaccuracies, thereby hindering the effective use of multitemporal VHR satellite images. Such errors can be minimized by applying preprocessing methods such as image registration and relative radiometric normalization (RRN). However, as the data used in image registration and RRN differ, data consistency and computational efficiency are impaired, particularly when processing large amounts of data, such as a large volume of multitemporal VHR satellite images. To resolve these issues, we proposed an integrated preprocessing method by extracting pseudo-invariant features (PIFs), used for RRN, based on the conjugate points (CPs) extracted for image registration. To this end, the image registration was performed using CPs extracted using the speeded-up robust feature algorithm. Then, PIFs were extracted based on the CPs by removing vegetation areas followed by application of the region growing algorithm. Experiments were conducted on two sites constructed under different acquisition conditions to confirm the robustness of the proposed method. Various analyses based on visual and quantitative evaluation of the experimental results were performed from geometric and radiometric perspectives. The results evidence the successful integration of the image registration and RRN preprocessing steps by achieving a reasonable and stable performance. Full article
(This article belongs to the Special Issue Computer Vision and Image Processing)
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Article
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
Cited by 3 | Viewed by 847
Abstract
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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