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Electronics
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Recent Advances in Image Processing and Computer Vision
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Recent Advances in Image Processing and Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 7714

Special Issue Editors

Dr. Vignesh Suresh

E-Mail Website
Guest Editor
Research & Development, Alcon Laboratories LLC, Fort Worth, TX 76134, USA
Interests: computer vision; machine vision; deep learning; optical metrology
Dr. Beiwen Li

E-Mail Website1 Website2
Guest Editor
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
Interests: superfast 3D optical sensing; multi-scale 3D optical metrology; machine/computer vision; in-situ manufacturing inspection and quality control; lasers; light sources and sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Zheng

E-Mail Website
Guest Editor
Meta Reality Lab Research, Redmond, WA 98052, USA
Interests: 3D imaging; AR/VR; deep learning; computer graphics

Special Issue Information

Dear Colleagues,

Computer vision is a research topic that is actively studied/explored by researchers from various domains. Advancements in image processing methods have paved the way for novel computer vision algorithms. Advanced computer vision methods have enabled automation in surgical procedures in healthcare, the quality inspection of parts in industries etc. The objective of this Special Issue of Electronics is to represent the state-of-the-art research progress in image processing and computer vision methods and their application in various domains. We invite researchers to contribute their original and unique articles, as well as review articles. Topics include, but are not limited to, the following areas:

  • Object detection;
  • Semantic segmentation;
  • Defect detection;
  • Digital image enhancement;
  • Advances in machine vision/computer vision;
  • 3D imaging;
  • Optical metrology;
  • Fringe analysis;
  • Applications of 3D imaging.

Dr. Vignesh Suresh
Dr. Beiwen Li
Dr. Yi Zheng
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. Electronics 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

  • detection
  • segmentation
  • counting
  • 3D imaging
  • optical metrology
  • image processing
  • image analysis
  • digital images
  • deep learning

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Published Papers (5 papers)

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Research

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13 pages, 2929 KiB  
Article
Increasing Offline Handwritten Chinese Character Recognition Using Separated Pre-Training Models: A Computer Vision Approach
by Xiaoli He, Bo Zhang and Yuan Long
Electronics 2024, 13(15), 2893; https://doi.org/10.3390/electronics13152893 - 23 Jul 2024
Viewed by 401
Abstract
Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges [...] Read more.
Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges include the diversity of glyphs resulting from different writers’ styles and habits, the vast number of Chinese character labels, and the presence of morphological similarities among characters. To address these challenges, an optimization method based on a separated pre-training model was proposed. The method aims to enhance the accuracy and robustness of recognizing similar character images by exploring potential correlations among them. In experiments, the HWDB and Chinese Calligraphy Styles by Calligraphers datasets were employed, utilizing precision, recall, and the Macro-F1 value as evaluation metrics. We employ a convolutional self-encoder model characterized by high recognition accuracy and robust performance. The experimental results demonstrated that the separated pre-training models improved the performance of the convolutional auto-encoder model, particularly in handling error-prone characters, resulting in an approximate 6% increase in precision. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing and Computer Vision)
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19 pages, 27425 KiB  
Article
N-DEPTH: Neural Depth Encoding for Compression-Resilient 3D Video Streaming
by Stephen Siemonsma and Tyler Bell
Electronics 2024, 13(13), 2557; https://doi.org/10.3390/electronics13132557 - 29 Jun 2024
Viewed by 525
Abstract
Recent advancements in 3D data capture have enabled the real-time acquisition of high-resolution 3D range data, even in mobile devices. However, this type of high bit-depth data remains difficult to efficiently transmit over a standard broadband connection. The most successful techniques for tackling [...] Read more.
Recent advancements in 3D data capture have enabled the real-time acquisition of high-resolution 3D range data, even in mobile devices. However, this type of high bit-depth data remains difficult to efficiently transmit over a standard broadband connection. The most successful techniques for tackling this data problem thus far have been image-based depth encoding schemes that leverage modern image and video codecs. To our knowledge, no published work has directly optimized the end-to-end losses of a depth encoding scheme sandwiched around a lossy image compression codec. We present N-DEPTH, a compression-resilient neural depth encoding method that leverages deep learning to efficiently encode depth maps into 24-bit RGB representations that minimize end-to-end depth reconstruction errors when compressed with JPEG. N-DEPTH’s learned robustness to lossy compression expands to video codecs as well. Compared to an existing state-of-the-art encoding method, N-DEPTH achieves smaller file sizes and lower errors across a large range of compression qualities, in both image (JPEG) and video (H.264) formats. For example, reconstructions from N-DEPTH encodings stored with JPEG had dramatically lower error while still offering 29.8%-smaller file sizes. When H.264 video was used to target a 10 Mbps bit rate, N-DEPTH reconstructions had 85.1%-lower root mean square error (RMSE) and 15.3%-lower mean absolute error (MAE). Overall, our method offers an efficient and robust solution for emerging 3D streaming and 3D telepresence applications, enabling high-quality 3D depth data storage and transmission. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing and Computer Vision)
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29 pages, 9073 KiB  
Article
Color Histogram Contouring: A New Training-Less Approach to Object Detection
by Tamer Rabie, Mohammed Baziyad, Radhwan Sani, Talal Bonny and Raouf Fareh
Electronics 2024, 13(13), 2522; https://doi.org/10.3390/electronics13132522 - 27 Jun 2024
Viewed by 442
Abstract
This paper introduces the Color Histogram Contouring (CHC) method, a new training-less approach to object detection that emphasizes the distinctive features in chrominance components. By building a chrominance-rich feature vector with a bin size of 1, the proposed CHC method exploits the precise [...] Read more.
This paper introduces the Color Histogram Contouring (CHC) method, a new training-less approach to object detection that emphasizes the distinctive features in chrominance components. By building a chrominance-rich feature vector with a bin size of 1, the proposed CHC method exploits the precise information in chrominance features without increasing bin sizes, which can lead to false detections. This feature vector demonstrates invariance to lighting changes and is designed to mimic the opponent color axes used by the human visual system. The proposed CHC algorithm iterates over non-zero histogram bins of unique color features in the model, creating a feature vector for each, and emphasizes those matching in both the scene and model histograms. When both model and scene histograms for these unique features align, it ensures the presence of the model in the scene image. Extensive experiments across various scenarios show that the proposed CHC technique outperforms the benchmark training-less Swain and Ballard method and the algorithm of Viola and Jones. Additionally, a comparative experiment with the state-of-the-art You Only Look Once (YOLO) technique reveals that the proposed CHC technique surpasses YOLO in scenarios with limited training data, highlighting a significant advancement in training-less object detection. This approach offers a valuable addition to computer vision, providing an effective training-less solution for real-time autonomous robot localization and mapping in unknown environments. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing and Computer Vision)
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Review

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30 pages, 8442 KiB  
Review
Single-Image Super-Resolution Challenges: A Brief Review
by Shutong Ye, Shengyu Zhao, Yaocong Hu and Chao Xie
Electronics 2023, 12(13), 2975; https://doi.org/10.3390/electronics12132975 - 6 Jul 2023
Cited by 9 | Viewed by 3259
Abstract
Single-image super-resolution (SISR) is an important task in image processing, aiming to achieve enhanced image resolution. With the development of deep learning, SISR based on convolutional neural networks has also gained great progress, but as the network deepens and the task of SISR [...] Read more.
Single-image super-resolution (SISR) is an important task in image processing, aiming to achieve enhanced image resolution. With the development of deep learning, SISR based on convolutional neural networks has also gained great progress, but as the network deepens and the task of SISR becomes more complex, SISR networks become difficult to train, which hinders SISR from achieving greater success. Therefore, to further promote SISR, many challenges have emerged in recent years. In this review, we briefly review the SISR challenges organized from 2017 to 2022 and focus on the in-depth classification of these challenges, the datasets employed, the evaluation methods used, and the powerful network architectures proposed or accepted by the winners. First, depending on the tasks of the challenges, the SISR challenges can be broadly classified into four categories: classic SISR, efficient SISR, perceptual extreme SISR, and real-world SISR. Second, we introduce the datasets commonly used in the challenges in recent years and describe their characteristics. Third, we present the image evaluation methods commonly used in SISR challenges in recent years. Fourth, we introduce the network architectures used by the winners, mainly to explore in depth where the advantages of their network architectures lie and to compare the results of previous years’ winners. Finally, we summarize the methods that have been widely used in SISR in recent years and suggest several possible promising directions for future SISR. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing and Computer Vision)
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Other

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21 pages, 4920 KiB  
Tutorial
3D Imaging with Fringe Projection for Food and Agricultural Applications—A Tutorial
by Badrinath Balasubramaniam, Jiaqiong Li, Lingling Liu and Beiwen Li
Electronics 2023, 12(4), 859; https://doi.org/10.3390/electronics12040859 - 8 Feb 2023
Cited by 2 | Viewed by 2204
Abstract
The rising global population, in conjunction with the increasing demand, decreasing labor supply, and increasing costs in the agricultural sector, has induced a need for automation in this industry. Many of these tasks are simplified using depth images and are accomplished using the [...] Read more.
The rising global population, in conjunction with the increasing demand, decreasing labor supply, and increasing costs in the agricultural sector, has induced a need for automation in this industry. Many of these tasks are simplified using depth images and are accomplished using the help of 3D sensing technology such as stereo vision and time of flight methods. While there are various merits to these approaches, there is a need for high-speed, high-accuracy 3D profiling approaches in this rapidly advancing industry. Fringe Projection Profilometry is a variation of structured light technology, which has the advantage of having high speed in the kilohertz range, and sub-millimeter accuracy, which could be extremely beneficial for this sector to adopt. In this article, we seek to provide a tutorial on this technology, explain its various principles along with the basic methodology, and expound on its advantages. We demonstrate some example results using soybean roots and spinach leaves to show its utility, discuss potential reasons as to why this has not yet been widely adopted by this industry, review its potential limitations, and examine possible ways those limitations can be addressed so that they do not present a roadblock in its adoption. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing and Computer Vision)
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