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Mathematics
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Advanced Machine Vision with Mathematics
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Advanced Machine Vision with Mathematics

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1119

Special Issue Editor

Dr. Longfei Zhou

E-Mail Website
Guest Editor
Biomedical, Industrial and Systems Engineering Department, Gannon University, Erie, PA 16541, USA
Interests: smart manufacturing; machine learning; computer vision; simulation; scheduling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine vision technologies have undergone significant advancements in recent years, fueling their widespread applications across various industries. Leveraging computer vision, artificial intelligence, and hardware innovations, these technologies are transforming automation, inspection, and analysis processes. In manufacturing, machine vision is used for quality control, defect detection, and product traceability, enhancing production efficiency and reducing errors. In agriculture, it aids in crop monitoring, pest control, and yield optimization. In healthcare, it supports medical image analysis and diagnosis, improving patient care. The automotive sector relies on machine vision for autonomous vehicles, enabling navigation and obstacle detection. Retail benefits from facial recognition and inventory management, while security employs it for surveillance and access control. Environmental monitoring, robotics, and augmented reality also harness machine vision. With the ever-evolving technology landscape, the potential applications of machine vision continue to expand, offering enhanced precision, efficiency, and innovation across numerous domains.
How to further apply mathematical models, algorithms and techniques in machine vision is an essential problem worthy of study. This special issue aims to invite and publish recent research studies on the latest advances in various intersections of machine vision technologies and applied mathematics, and their recent applications in various industries. Topics include, but are not limited to, the following:

  • Image classification;
  • object recognition and detection;
  • image segmentation;
  • feature extraction;
  • image registration;
  • object tracking;
  • 3D vision;
  • generative models;
  • biometrics;
  • deep learning for machine vision;
  • visual slam;
  • medical imaging;
  • other applications of machine vision.

Dr. Longfei Zhou
Guest Editor

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

  • image classification
  • object recognition and detection
  • image segmentation
  • feature extraction
  • image registration
  • object tracking
  • 3D vision
  • generative models
  • biometrics
  • deep learning for machine vision
  • visual slam
  • medical imaging

Published Papers (2 papers)

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Research

16 pages, 59324 KiB  
Article
A New Biorthogonal Spline Wavelet-Based K-Layer Network for Underwater Image Enhancement
by Dujuan Zhou, Zhanchuan Cai and Dan He
Mathematics 2024, 12(9), 1366; https://doi.org/10.3390/math12091366 - 30 Apr 2024
Viewed by 423
Abstract
Wavelet decomposition is pivotal for underwater image processing, known for its ability to analyse multi-scale image features in the frequency and spatial domains. In this paper, we propose a new biorthogonal cubic special spline wavelet (BCS-SW), based on the Cohen–Daubechies–Feauveau (CDF) wavelet construction [...] Read more.
Wavelet decomposition is pivotal for underwater image processing, known for its ability to analyse multi-scale image features in the frequency and spatial domains. In this paper, we propose a new biorthogonal cubic special spline wavelet (BCS-SW), based on the Cohen–Daubechies–Feauveau (CDF) wavelet construction method and the cubic special spline algorithm. BCS-SW has better properties in compact support, symmetry, and frequency domain characteristics. In addition, we propose a K-layer network (KLN) based on the BCS-SW for underwater image enhancement. The KLN performs a K-layer wavelet decomposition on underwater images to extract various frequency domain features at multiple frequencies, and each decomposition layer has a convolution layer corresponding to its spatial size. This design ensures that the KLN can understand the spatial and frequency domain features of the image at the same time, providing richer features for reconstructing the enhanced image. The experimental results show that the proposed BCS-SW and KLN algorithm has better image enhancement effect than some existing algorithms. Full article
(This article belongs to the Special Issue Advanced Machine Vision with Mathematics)
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24 pages, 21453 KiB  
Article
U-Net-Based Learning Using Enhanced Lane Detection with Directional Lane Attention Maps for Various Driving Environments
by Seung-Hwan Lee and Sung-Hak Lee
Mathematics 2024, 12(8), 1206; https://doi.org/10.3390/math12081206 - 17 Apr 2024
Viewed by 461
Abstract
Recent advancements in optical and electronic sensor technologies, coupled with the proliferation of computing devices (such as GPUs), have enabled real-time autonomous driving systems to become a reality. Hence, research in algorithmic advancements for advanced driver assistance systems (ADASs) is rapidly expanding, with [...] Read more.
Recent advancements in optical and electronic sensor technologies, coupled with the proliferation of computing devices (such as GPUs), have enabled real-time autonomous driving systems to become a reality. Hence, research in algorithmic advancements for advanced driver assistance systems (ADASs) is rapidly expanding, with a primary focus on enhancing robust lane detection capabilities to ensure safe navigation. Given the widespread adoption of cameras on the market, lane detection relies heavily on image data. Recently, CNN-based methods have attracted attention due to their effective performance in lane detection tasks. However, with the expansion of the global market, the endeavor to achieve reliable lane detection has encountered challenges presented by diverse environmental conditions and road scenarios. This paper presents an approach that focuses on detecting lanes in road areas traversed by vehicles equipped with cameras. In the proposed method, a U-Net based framework is employed for training, and additional lane-related information is integrated into a four-channel input data format that considers lane characteristics. The fourth channel serves as the edge attention map (E-attention map), helping the modules achieve more specialized learning regarding the lane. Additionally, the proposition of an approach to assign weights to the loss function during training enhances the stability and speed of the learning process, enabling robust lane detection. Through ablation experiments, the optimization of each parameter and the efficiency of the proposed method are demonstrated. Also, the comparative analysis with existing CNN-based lane detection algorithms shows that the proposed training method demonstrates superior performance. Full article
(This article belongs to the Special Issue Advanced Machine Vision with Mathematics)
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