Modern Computer Vision and Pattern Recognition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 4805

Special Issue Editors

School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China
Interests: pattern recognition; machine learning; computer vision; emerging applications of AI
School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China
Interests: computer vision; pattern recognition; image and video analysis

Special Issue Information

Dear Colleagues,

It is our pleasure to present a Special Issue of Applied Sciences on "Modern Computer Vision and Pattern Recognition" and to invite concerned authors to submit original articles on related topics.

In recent years, thanks to modern artificial intelligence techniques such as deep learning and reinforcement learning, remarkable progress has been made in computer vision and pattern recognition. Many influential approaches have been proposed, and they have effectively addressed pattern recognition and computer vision tasks such as object detection and recognition, face recognition, action localization, and person re-identification. However, the more general application scenarios in industrial, medical, transportation, and social areas bring new challenges, such as training data scarcity, data noise, complex label relations, high dimensionality, and imbalance distribution. These challenges urgently require modern computer vision and pattern recognition approaches to provide better solutions.

In this Special Issue, we invite submissions exploring advanced research and emerging applications in the fields of computer vision and pattern recognition.

Dr. Sheng Huang
Dr. Yongxin Ge
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. Applied Sciences 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.

Keywords

  • object recognition and detection
  • biometrics
  • image and video analysis
  • representation learning
  • text mining
  • dimensionality reduction
  • few/zero-shot learning
  • pattern discovery and recognition
  • emerging applications of computer vision and pattern recognition

Published Papers (4 papers)

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Research

16 pages, 3158 KiB  
Article
Semantic Segmentation of 3D Point Clouds in Outdoor Environments Based on Local Dual-Enhancement
by Kai Zhang, Yi An, Yunhao Cui and Hongxiang Dong
Appl. Sci. 2024, 14(5), 1777; https://doi.org/10.3390/app14051777 - 22 Feb 2024
Viewed by 452
Abstract
Semantic segmentation of 3D point clouds in drivable areas is very important for unmanned vehicles. Due to the imbalance between the size of various outdoor scene objects and the sample size, the object boundaries are not clear, and small sample features cannot be [...] Read more.
Semantic segmentation of 3D point clouds in drivable areas is very important for unmanned vehicles. Due to the imbalance between the size of various outdoor scene objects and the sample size, the object boundaries are not clear, and small sample features cannot be extracted. As a result, the semantic segmentation accuracy of 3D point clouds in outdoor environment is not high. To solve these problems, we propose a local dual-enhancement network (LDE-Net) for semantic segmentation of 3D point clouds in outdoor environments for unmanned vehicles. The network is composed of local-global feature extraction modules, and a local feature aggregation classifier. The local-global feature extraction module captures both local and global features, which can improve the accuracy and robustness of semantic segmentation. The local feature aggregation classifier considers the feature information of neighboring points to ensure clarity of object boundaries and the high overall accuracy of semantic segmentation. Experimental results show that provides clearer boundaries between various objects, and has higher identification accuracy for small sample objects. The LDE-Net has good performance for semantic segmentation of 3D point clouds in outdoor environments. Full article
(This article belongs to the Special Issue Modern Computer Vision and Pattern Recognition)
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20 pages, 2193 KiB  
Article
A Lightweight Robust Distance Estimation Method for Navigation Aiding in Unsupervised Environment Using Monocular Camera
by Ka Seng Chou, Teng Lai Wong, Kei Long Wong, Lu Shen, Davide Aguiari, Rita Tse, Su-Kit Tang and Giovanni Pau
Appl. Sci. 2023, 13(19), 11038; https://doi.org/10.3390/app131911038 - 07 Oct 2023
Cited by 1 | Viewed by 1653
Abstract
This research addresses the challenges of visually impaired individuals’ independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target objects. Utilizing a monocular camera, the method captures cuboid objects (e.g., fences, [...] Read more.
This research addresses the challenges of visually impaired individuals’ independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target objects. Utilizing a monocular camera, the method captures cuboid objects (e.g., fences, pillars) for near-front distance estimation. A Field of View (FOV) model calculates the camera’s angle and arbitrary pitch relative to the target Point of Interest (POI) within the image. Experimental results demonstrate the method’s proficiency in detecting distances between objects and the source camera, employing the FOV and Point of View (POV) principles. The approach achieves a mean absolute percentage error (MAPE) of 6.18% and 6.24% on YOLOv4-tiny and YOLOv4, respectively, within 10 m. The distance model only contributes a maximum error of 4% due to POV simplification, affected by target object characteristics, height, and selected POV. The proposed distance estimation method shows promise in drone racing navigation, EV autopilot, and aiding visually impaired individuals. It offers valuable insights into dynamic 3D environment distance estimation, advancing computer vision and autonomous systems. Full article
(This article belongs to the Special Issue Modern Computer Vision and Pattern Recognition)
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20 pages, 3587 KiB  
Article
Improved YOLOv4-Tiny Target Detection Method Based on Adaptive Self-Order Piecewise Enhancement and Multiscale Feature Optimization
by Dengsheng Cai, Zhigang Lu, Xiangsuo Fan, Wentao Ding and Bing Li
Appl. Sci. 2023, 13(14), 8177; https://doi.org/10.3390/app13148177 - 13 Jul 2023
Cited by 2 | Viewed by 789
Abstract
To improve the accuracy of material identification under low contrast conditions, this paper proposes an improved YOLOv4-tiny target detection method based on an adaptive self-order piecewise enhancement and multiscale feature optimization. The model first constructs an adaptive self-rank piecewise enhancement algorithm to enhance [...] Read more.
To improve the accuracy of material identification under low contrast conditions, this paper proposes an improved YOLOv4-tiny target detection method based on an adaptive self-order piecewise enhancement and multiscale feature optimization. The model first constructs an adaptive self-rank piecewise enhancement algorithm to enhance low-contrast images and then considers the fast detection ability of the YOLOv4-tiny network. To make the detection network have a higher accuracy, this paper adds an SE channel attention mechanism and an SPP module to this lightweight backbone network to increase the receptive field of the model and enrich the expression ability of the feature map. The network can pay more attention to salient information, suppress edge information, and effectively improve the training accuracy of the model. At the same time, to better fuse the features of different scales, the FPN multiscale feature fusion structure is redesigned to strengthen the fusion of semantic information at all levels of the network, enhance the ability of network feature extraction, and improve the overall detection accuracy of the model. The experimental results show that compared with the mainstream network framework, the improved YOLOv4-tiny network in this paper effectively improves the running speed and target detection accuracy of the model, and its mAP index reaches 98.85%, achieving better detection results. Full article
(This article belongs to the Special Issue Modern Computer Vision and Pattern Recognition)
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17 pages, 9294 KiB  
Article
Optimization of a Multi-Type PMSM Based on Pyramid Neural Network
by Xiaoyu Liu, Wenqian Peng, Liuyin Xie and Xiang Zhang
Appl. Sci. 2023, 13(11), 6810; https://doi.org/10.3390/app13116810 - 03 Jun 2023
Viewed by 1204
Abstract
In this paper, a novel bat algorithm based on the quantum computing concept and pyramid neural network (PNN) is presented and applied to the electromagnetic motor optimization problem. Due to the problems of high loss, high temperature rise and threatening motor safety, it [...] Read more.
In this paper, a novel bat algorithm based on the quantum computing concept and pyramid neural network (PNN) is presented and applied to the electromagnetic motor optimization problem. Due to the problems of high loss, high temperature rise and threatening motor safety, it is necessary to optimize the design of high-speed permanent magnet synchronous motor (HPMSM) structure. In order to use less training data and avoid the problem of large computational costs due to repeated finite element simulation in the electromagnetic structure design, this paper adopted a performance-driven method to establish the PMSM model. This model could effectively reduce the dimensions of the parameter space and establish an effective high-quality model within a wide range of parameters. For the purpose of obtaining a reliable proxy model with less training data, this paper adopted a pyramid-shaped neural network, which could reduce the risk of overtraining and improve the utilization of specific problem knowledge embedded in the training data set. The quantum bat algorithm (QBA) was used to optimize the structure of the PMSM. Compared with the classical GA and PSO algorithms, the QBA has the characteristics of a rapid convergence speed, simple structure, strong searching ability and stronger local jumping mechanism. The correctness and effectiveness of the proposed PNN-based QBA method were verified using simulation analysis and a prototype test. Full article
(This article belongs to the Special Issue Modern Computer Vision and Pattern Recognition)
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