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Applied Sciences
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Current Trends and Future Perspectives on Computer Vision and Pattern...
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Special Issue "Current Trends and Future Perspectives on 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: 31 July 2023 | Viewed by 2401

Special Issue Editors

Dr. Weijun Li

E-Mail Website
Guest Editor
Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
Interests: pattern recognition; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Ning

E-Mail Website
Guest Editor
a Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China b Center of Materials Science and Optoelectronics Engineering School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China
Interests: pattern recognition; image classification; neural network; convolutional network; computer vision; object detection
Special Issues, Collections and Topics in MDPI journals
Dr. Sahraoui Dhelim

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
Interests: IoT; social computing; intelligent transportation systems; IoT; social networks analysis; mobile edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in Computer Vision and Pattern Recognition have accelerated the development of intelligent applications for numerous industries and domains. Such solutions are not only seamlessly integrated in the environment, but typically have large adaptability for unexpected conditions, which increases their usefulness for real-world problems. Recent advances in Computer Vision and Pattern Recognition have had many successes but also have several limitations and there is limited understanding of their inner workings. It is remains a major challenge in the deployment of Computer Vision and Pattern Recognition algorithms in real-world scenarios. Therefore, this paper, on the Current Trends and Future Perspectives on Computer Vision and Pattern Recognition, seeks to collect the most recent approaches and findings, as well as discuss the current challenges of Computer Vision and Pattern Recognition solutions for a wide variety of applications. We expect this Special Issue to tackle the research concerns in the closely linked fields of Computer Vision and Pattern Recognition, such as Machine Learning, Data Mining, Computer Vision and Image Processing. We encourage interdisciplinary study and application in these fields.

Important new theories, methods, applications and systems in emerging areas of Computer Vision and Pattern Recognition are welcome high-quality submissions. The topics of interest include, but are not limited to:

  • Interpretable Machine Learning for Computer Vision;
  • Computer vision theory；
  • Semi-supervised, weakly supervised and unsupervised learning frameworks for Pattern Recognition systems;
  • Embodied vision: active agents, simulation;
  • Automated Deep Learning, including one or multiple stages of the machine learning process (e.g., data pre-processing, network architecture selection, hyper-parameter optimisation);
  • 3D from multi-view, sensors and single images;
  • Automated Deep Learning, including one or multiple stages of the machine learning process (e.g., data pre-processing, network architecture selection, hyper-parameter optimisation);
  • Multimodal learning;
  • Ethics/Privacy issues in deploying Pattern Recognition-based systems;
  • Virtual and augmented reality content and systems;
  • Benchmarks of current Pattern-Recognition-based solutions for real-world problems;

Dr. Weijun Li
Dr. Xin Ning
Dr. Sahraoui Dhelim
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 2300 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

  • pattern-recognition systems
  • machine learning
  • computer vision
  • virtual reality
  • object detection and classfication

Published Papers (4 papers)

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Article
Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism
by
Tielin Ru
and
Ziheng Zhu
Appl. Sci. 2023, 13(5), 2996; https://doi.org/10.3390/app13052996 - 26 Feb 2023
Viewed by 271
Abstract
Multi-person behavior event recognition has become an increasingly challenging research field in human–computer interaction. With the rapid development of deep learning and computer vision, it plays an important role in the inference and analysis of real sports events, that is, given the video [...] Read more.
Multi-person behavior event recognition has become an increasingly challenging research field in human–computer interaction. With the rapid development of deep learning and computer vision, it plays an important role in the inference and analysis of real sports events, that is, given the video frequency of sports events, when letting it analyze and judge the behavior trend of athletes, often faced with the limitations of large-scale data sets and hardware, it takes a lot of time, and the accuracy of the results is not high. Therefore, we propose a deep clustering learning network for motion recognition under the self-attention mechanism, which can efficiently solve the accuracy and efficiency problems of sports event analysis and judgment. This method can not only solve the problem of gradient disappearance and explosion in the recurrent neural network (RNN), but also capture the internal correlation between multiple people on the sports field for identification, etc., by using the long and short-term memory network (LSTM), and combine the motion coding information in the key frames with the deep embedded clustering (DEC) to better analyze and judge the complex behavior change types of athletes. In addition, by using the self-attention mechanism, we can not only analyze the whole process of the sports video macroscopically, but also focus on the specific attributes of the movement, extract the key posture features of the athletes, further enhance the features, effectively reduce the amount of parameters in the calculation process of self-attention, reduce the computational complexity, and maintain the ability to capture details. The accuracy and efficiency of reasoning and judgment are improved. Through verification on large video datasets of mainstream sports, we achieved high accuracy and improved the efficiency of inference and prediction. It is proved that the method is effective and feasible in the analysis and reasoning of sports videos. Full article
(This article belongs to the Special Issue Current Trends and Future Perspectives on Computer Vision and Pattern Recognition)
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Article
An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
by
Yaohua Deng
,
Chengwang Guo
,
Zilin Zhang
,
Linfeng Zou
,
Xiali Liu
and
Shengyu Lin
Appl. Sci. 2023, 13(4), 2622; https://doi.org/10.3390/app13042622 - 17 Feb 2023
Viewed by 422
Abstract
Data imbalance and large data probability distribution discrepancies are major factors that reduce the accuracy of remaining useful life (RUL) prediction of high-reliability rotating machinery. In feature extraction, most deep transfer learning models consider the overall features but rarely attend to the local [...] Read more.
Data imbalance and large data probability distribution discrepancies are major factors that reduce the accuracy of remaining useful life (RUL) prediction of high-reliability rotating machinery. In feature extraction, most deep transfer learning models consider the overall features but rarely attend to the local target features that are useful for RUL prediction; insufficient attention paid to local features reduces the accuracy and reliability of prediction. By considering the contribution of input data to the modeling output, a deep learning model that incorporates the attention mechanism in feature selection and extraction is proposed in our work; an unsupervised clustering method for classification of rotating machinery performance state evolution is put forward, and a similarity function is used to calculate the expected attention of input data to build an input data extraction attention module; the module is then fused with a gated recurrent unit (GRU), a variant of a recurrent neural network, to construct an attention-GRU model that combines prediction calculation and weight calculation for RUL prediction. Tests on public datasets show that the attention-GRU model outperforms traditional GRU and LSTM in RUL prediction, achieves less prediction error, and improves the performance and stability of the model. Full article
(This article belongs to the Special Issue Current Trends and Future Perspectives on Computer Vision and Pattern Recognition)
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Article
Point Cloud Repair Method via Convex Set Theory
by
Tianzhen Dong
,
Yi Zhang
,
Mengying Li
and
Yuntao Bai
Appl. Sci. 2023, 13(3), 1830; https://doi.org/10.3390/app13031830 - 31 Jan 2023
Viewed by 482
Abstract
The point cloud is the basis for 3D object surface reconstruction. An incomplete point cloud significantly reduces the accuracy of downstream work such as 3D object reconstruction and recognition. Therefore, point-cloud repair is indispensable work. However, the original shape of the point cloud [...] Read more.
The point cloud is the basis for 3D object surface reconstruction. An incomplete point cloud significantly reduces the accuracy of downstream work such as 3D object reconstruction and recognition. Therefore, point-cloud repair is indispensable work. However, the original shape of the point cloud is difficult to restore due to the uncertainty of the position of the new filling point. Considering the advantages of the convex set in dealing with uncertainty problems, we propose a point-cloud repair method via a convex set that transforms a point-cloud repair problem into a construction problem of the convex set. The core idea of the proposed method is to discretize the hole boundary area into multiple subunits and add new 3D points to the specific subunit according to the construction properties of the convex set. Specific subunits must be located in the hole area. For the selection of the specific subunit, we introduced Markov random fields (MRF) to transform them into the maximal a posteriori (MAP) estimation problem of random field labels. Variational inference was used to approximate MAP and calculate the specific subunit that needed to add new points. Our method iteratively selects specific subunits and adds new filling points. With the increasing number of iterations, the specific subunits gradually move to the center of the hole region until the hole is completely repaired. The quantitative and qualitative results of the experiments demonstrate that our method was superior to the compared method. Full article
(This article belongs to the Special Issue Current Trends and Future Perspectives on Computer Vision and Pattern Recognition)
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Article
Monocular 3D Object Detection Based on Pseudo Multimodal Information Extraction and Keypoint Estimation
by
Dan Zhao
,
Chaofeng Ji
and
Guizhong Liu
Appl. Sci. 2023, 13(3), 1731; https://doi.org/10.3390/app13031731 - 29 Jan 2023
Viewed by 651
Abstract
Three-dimensional object detection is an essential and fundamental task in the field of computer vision which can be widely used in various scenarios such as autonomous driving and visual navigation. In view of the current insufficient utilization of image information in current monocular [...] Read more.
Three-dimensional object detection is an essential and fundamental task in the field of computer vision which can be widely used in various scenarios such as autonomous driving and visual navigation. In view of the current insufficient utilization of image information in current monocular camera-based 3D object detection algorithms, we propose a monocular 3D object detection algorithm based on pseudo-multimodal information extraction and keypoint estimation. We utilize the original image to generate pseudo-lidar and a bird’s-eye view, and then feed the fused data of the original image and pseudo-lidar to the keypoint-based network for an initial 3D box estimation, finally using the bird’s-eye view to refine the initial 3D box. The experimental performance of our method exceeds state-of-the-art algorithms under the evaluation criteria of 3D object detection and localization on the KITTI dataset, achieving the best experimental performance so far. Full article
(This article belongs to the Special Issue Current Trends and Future Perspectives on Computer Vision and Pattern Recognition)
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