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Mathematics
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Mathematical Computation for Pattern Recognition and Computer Vision
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Mathematical Computation for Pattern Recognition and Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1196

Special Issue Editors

Prof. Dr. Ying-Hui Wang

E-Mail Website
Guest Editor
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Interests: virtual reality; visualization technology; computer vision
Dr. Wei Li

E-Mail Website
Guest Editor
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Interests: artificial intelligence; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pattern recognition and computer vision have always been inseparable from the support of mathematical tools, and increasingly reflect the importance of advanced mathematics. With the development of mathematical tools, it is necessary to further explore the compatibility between pattern recognition and computer vision. Therefore, the focus of this topic includes topics related to pattern recognition and computer recognition, as well as content in image processing, image recognition, multi view 3D restoration, single-image 3D restoration, 3D object recognition, 3D modeling, and other aspects. We hope to drive the development of mathematical theory through research and application in pattern recognition and computer vision; At the same time, advanced mathematical methods can also be used to enhance and solve related technical problems in the fields of pattern recognition and computational vision.

Prof. Dr. Ying-Hui Wang
Dr. Wei Li
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. Mathematics 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 2600 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

  • artificial intelligence
  • pattern recognition
  • computer vision
  • feature extraction
  • feature matching
  • image processing
  • single-image 3d restoration
  • multiple view geometry
  • 3D point cloud
  • 3D object recognition
  • machine learning
  • neural network
  • deep learning

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Published Papers (1 paper)

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Research

15 pages, 6843 KiB  
Article
TSPconv-Net: Transformer and Sparse Convolution for 3D Instance Segmentation in Point Clouds
by Xiaojuan Ning, Yule Liu, Yishu Ma, Zhiwei Lu, Haiyan Jin, Zhenghao Shi and Yinghui Wang 
Mathematics 2024, 12(18), 2926; https://doi.org/10.3390/math12182926 - 20 Sep 2024
Viewed by 817
Abstract
Current deep learning approaches for indoor 3D instance segmentation often rely on multilayer perceptrons (MLPs) for feature extraction. However, MLPs struggle to effectively capture the complex spatial relationships inherent in 3D scene data. To address this issue, we propose a novel and efficient [...] Read more.
Current deep learning approaches for indoor 3D instance segmentation often rely on multilayer perceptrons (MLPs) for feature extraction. However, MLPs struggle to effectively capture the complex spatial relationships inherent in 3D scene data. To address this issue, we propose a novel and efficient framework for 3D instance segmentation called TSPconv-Net. In contrast to existing methods that primarily depend on MLPs for feature extraction, our framework integrates a more robust feature extraction model comprising the offset-attention (OA) mechanism and submanifold sparse convolution (SSC). The proposed framework is an end-to-end network architecture. TSPconv-Net consists of a backbone network followed by a bounding box module. Specifically, the backbone network utilizes the OA mechanism to extract global features and employs SSC for local feature extraction. The bounding box module then conducts instance segmentation based on the extracted features. Experimental results demonstrate that our approach outperforms existing work on the S3DIS dataset while maintaining computational efficiency. TSPconv-Net achieves 68.6% mPrec, 52.5% mRec, and 60.1% mAP on the test set, surpassing 3D-BoNet by 3.0% mPrec, 5.4% mRec, and 2.6% mAP. Furthermore, it demonstrates high efficiency, completing computations in just 326 s. Full article
(This article belongs to the Special Issue Mathematical Computation for Pattern Recognition and Computer Vision)
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