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Computer Vision and Machine Learning for Intelligent Systems
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Computer Vision and Machine Learning for Intelligent Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 8917

Special Issue Editors

Dr. Chen Li

E-Mail Website
Guest Editor
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
Interests: microscopic image and medical image analysis; artificial intelligence; pattern recognition; machine learning; machine vision; multimedia retrieval; intelligent microscopic imaging technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun Xu

E-Mail Website
Guest Editor
Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: computational pathology; medical image analysis; computer-aided prevention, diagnosis, prognosis, and treatment of diseases; multi-modality medical data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer vision (CV) and machine learning (ML) are two important sub-domains in artificial intelligence (AI), playing significant roles in the research and practice of intelligent systems and their applications. CV usually includes image denoising, image inpainting, image segmentation, image classification, image clocation, object detection, image captioning, image generation, object identification, and image reconstruction tasks. ML usually includes supervised and unsupervised learning related to classification and clustering tasks. Both CV and ML are fundamental and essential techniques for intelligent systems and applications, such as through face recognition, fingerprint recognition, fire monitor, and medical and biological information analysis systems. The aim of this Special Issue is to publicize new ideas, original trend analyses, originally developed software, new methods, and other research results in relation to CV and ML for intelligent systems and their applications. Both researchers and practitioners are welcome to submit their papers. In particular, we hope that interdisciplinary researchers can contribute to this Special Issue in relation to topics including but not limited to “biology and ML”, “chemistry and ML”, “environmental science and CV”, “material and CV”, “education and ML”, and “medicine and CV”. 

Dr. Chen Li
Prof. Dr. Jun Xu
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. Sensors 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

  • computer vision
  • machine vision
  • robot vision
  • machine learning
  • artificial intelligence
  • pattern recognition
  • intelligent system
  • intelligent application
  • data classification
  • data clustering

Published Papers (3 papers)

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Research

23 pages, 1231 KiB  
Article
Efficient Transformer-Based Compressed Video Modeling via Informative Patch Selection
by Tomoyuki Suzuki and Yoshimitsu Aoki
Sensors 2023, 23(1), 244; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010244 - 26 Dec 2022
Viewed by 2222
Abstract
Recently, Transformer-based video recognition models have achieved state-of-the-art results on major video recognition benchmarks. However, their high inference cost significantly limits research speed and practical use. In video compression, methods considering small motions and residuals that are less informative and assigning short code [...] Read more.
Recently, Transformer-based video recognition models have achieved state-of-the-art results on major video recognition benchmarks. However, their high inference cost significantly limits research speed and practical use. In video compression, methods considering small motions and residuals that are less informative and assigning short code lengths to them (e.g., MPEG4) have successfully reduced the redundancy of videos. Inspired by this idea, we propose Informative Patch Selection (IPS), which efficiently reduces the inference cost by excluding redundant patches from the input of the Transformer-based video model. The redundancy of each patch is calculated from motions and residuals obtained while decoding a compressed video. The proposed method is simple and effective in that it can dynamically reduce the inference cost depending on the input without any policy model or additional loss term. Extensive experiments on action recognition demonstrated that our method could significantly improve the trade-off between the accuracy and inference cost of the Transformer-based video model. Although the method does not require any policy model or additional loss term, its performance approaches that of existing methods that do require them. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning for Intelligent Systems)
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22 pages, 4984 KiB  
Article
Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification
by Cesar Torres, Claudia I. Gonzalez and Gabriela E. Martinez
Sensors 2022, 22(15), 5892; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155892 - 07 Aug 2022
Cited by 3 | Viewed by 2188
Abstract
Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a [...] Read more.
Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task. We propose and compare two convolutional neural network architectures to solve the task. Fuzzy edge-detection techniques are compared against their classical counterparts (Sobel, Prewitt, and morphological gradient edge-detection) and with grayscale and color images in the RGB color space. The fuzzy preprocessing methodologies highlight the most essential features of each image, achieving favorable results when compared to the classical preprocessing methodologies and against a pre-trained model with both proposed models, as well as achieving a reduction in training times of more than 20% compared to RGB images. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning for Intelligent Systems)
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18 pages, 6062 KiB  
Article
COIN: Counterfactual Image Generation for Visual Question Answering Interpretation
by Zeyd Boukhers, Timo Hartmann and Jan Jürjens
Sensors 2022, 22(6), 2245; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062245 - 14 Mar 2022
Cited by 3 | Viewed by 2294
Abstract
Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour [...] Read more.
Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models’ behaviour. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning for Intelligent Systems)
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