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Application of Information Theory to Computer Vision and Image Processing, 3rd Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1754

Special Issue Editors

Dr. Jesús Elías Miranda-Vega

E-Mail Website
Guest Editor
Tecnológico Nacional de México, IT de Mexicali, Mexicali 21376, México
Interests: machine vision; stereo vision; systems laser; scanner control; analogic and digital processing
Special Issues, Collections and Topics in MDPI journals
Dr. Wendy Flores-Fuentes

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autonoma de Baja California, Mexicali B.C. 21280, Mexico
Interests: fourth industrial revolution; artificial intelligence; cybersystems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Julio Cesar Rodríguez-Quiñonez

E-Mail
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali B.C. 21280, Mexico
Interests: machine vision; stereo vision; systems laser; scanner control; digital image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Oleg Sergiyenko

E-Mail Website
Guest Editor
Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali B.C. 21100, Mexico
Interests: automated metrology; 3D coordinate measurement; robotic navigation; machine vision; simulation of robotic swarm behavior
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that due to the great success of the first and second volume of “Application of Information Theory to Computer Vision and Image Processing”, a new Special Issue titled “Application of Information Theory to Computer Vision and Image Processing, 3rd Edition” is now open for the submission of relevant papers of related topics.

The application of information theory to computer vision and image processing has significantly contributed to advancing our understanding and the capabilities of computer science. Mathematical methods are applied to signal and image processing for quantifying and obtaining accurate information with enhanced efficiency upon every innovation. Moreover, they provide valuable tools and techniques for the development of intelligent and adaptive machine vision systems for measuring and analyzing the amount of information contained within a signal and an image. These include the entropy theory, which is used to estimate the average amount of uncertainty or randomness in a dataset, where high entropy indicates a higher level of unpredictability, while low entropy suggests a more predictable and structured dataset.

This Special Issue aims to publish papers on information theory, measurement methods, data processing, tools, and techniques for the design and instrumentation used in machine vision systems via the application of computer vision and image processing for analyzing, processing, and understanding visual data based on the principles of information content, redundancy, and statistical properties.

Dr. Jesús Elías Miranda-Vega
Dr. Wendy Flores-Fuentes
Prof. Dr. Julio Cesar Rodríguez-Quiñonez
Dr. Oleg Sergiyenko
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. Entropy is an international peer-reviewed open access monthly 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

  • information theory
  • entropy and coding theory (data compression, watermark, minimizing data loss, visual information in a more compact form, transmission, storage)
  • computer vision (identify relevant features and patterns)
  • machine vision (data analysis and understanding, segmentation, registration, denoising and restoration, object recognition, classification and tracking)
  • cyber-physical systems
  • instrumentation
  • signal and image processing
  • measurements (3D spatial coordinates, redundancy, statistical properties)
  • artificial intelligence
  • applications (navigation, surveillance, facial recognition, medicine, robotics, entertainment, and more)

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 7163 KiB  
Article
Entropy-Regularized Attention for Explainable Histological Classification with Convolutional and Hybrid Models
by Pedro L. Miguel, Leandro A. Neves, Alessandra Lumini, Giuliano C. Medalha, Guilherme F. Roberto, Guilherme B. Rozendo, Adriano M. Cansian, Thaína A. A. Tosta and Marcelo Z. do Nascimento
Entropy 2025, 27(7), 722; https://doi.org/10.3390/e27070722 - 3 Jul 2025
Viewed by 310
Abstract
Deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) perform well in histological image classification, but often lack interpretability. We introduce a unified framework that adds an attention branch and CAM Fostering, an entropy-based regularizer, to improve Grad-CAM visualizations. [...] Read more.
Deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) perform well in histological image classification, but often lack interpretability. We introduce a unified framework that adds an attention branch and CAM Fostering, an entropy-based regularizer, to improve Grad-CAM visualizations. Six backbone architectures (ResNet-50, DenseNet-201, EfficientNet-b0, ResNeXt-50, ConvNeXt, CoatNet-small) were trained, with and without our modifications, on five H&E-stained datasets. We measured explanation quality using coherence, complexity, confidence drop, and their harmonic mean (ADCC). Our method increased the ADCC in five of the six backbones; ResNet-50 saw the largest gain (+15.65%), and CoatNet-small achieved the highest overall score (+2.69%), peaking at 77.90% on the non-Hodgkin lymphoma set. The classification accuracy remained stable or improved in four models. These results show that combining attention and entropy produces clearer, more informative heatmaps without degrading performance. Our contributions include a modular architecture for both convolutional and hybrid models and a comprehensive, quantitative explainability evaluation suite. Full article
(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing, 3rd Edition)
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17 pages, 4019 KiB  
Article
Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization
by Yaling Dang, Fei Duan and Jia Chen
Entropy 2025, 27(7), 677; https://doi.org/10.3390/e27070677 - 25 Jun 2025
Viewed by 578
Abstract
Automatic classification of oil-painting styles holds significant promise for art history, digital archiving, and forensic investigation by offering objective, scalable analysis of visual artistic attributes. In this paper, we introduce a deep conditional information bottleneck (CIB) framework, built atop ResNet-50, for fine-grained style [...] Read more.
Automatic classification of oil-painting styles holds significant promise for art history, digital archiving, and forensic investigation by offering objective, scalable analysis of visual artistic attributes. In this paper, we introduce a deep conditional information bottleneck (CIB) framework, built atop ResNet-50, for fine-grained style classification of oil paintings. Unlike traditional information bottleneck (IB) approaches that minimize the mutual information I(X;Z) between input X and latent representation Z, our CIB minimizes the conditional mutual information I(X;Z∣Y), where Y denotes the painting’s style label. We implement this conditional term using a matrix-based Rényi’s entropy estimator, thereby avoiding costly variational approximations and ensuring computational efficiency. We evaluate our method on two public benchmarks: the Pandora dataset (7740 images across 12 artistic movements) and the OilPainting dataset (19,787 images across 17 styles). Our method outperforms the prevalent ResNet with a relative performance gain of 13.1% on Pandora and 11.9% on OilPainting. Beyond quantitative gains, our approach yields more disentangled latent representations that cluster semantically similar styles, facilitating interpretability. Full article
(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing, 3rd Edition)
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35 pages, 1553 KiB  
Article
Efficient Learning-Based Robotic Navigation Using Feature-Based RGB-D Pose Estimation and Topological Maps
by Eder A. Rodríguez-Martínez, Jesús Elías Miranda-Vega, Farouk Achakir, Oleg Sergiyenko, Julio C. Rodríguez-Quiñonez, Daniel Hernández Balbuena and Wendy Flores-Fuentes
Entropy 2025, 27(6), 641; https://doi.org/10.3390/e27060641 - 15 Jun 2025
Viewed by 628
Abstract
Robust indoor robot navigation typically demands either costly sensors or extensive training data. We propose a cost-effective RGB-D navigation pipeline that couples feature-based relative pose estimation with a lightweight multi-layer-perceptron (MLP) policy. RGB-D keyframes extracted from human-driven traversals form nodes of a topological [...] Read more.
Robust indoor robot navigation typically demands either costly sensors or extensive training data. We propose a cost-effective RGB-D navigation pipeline that couples feature-based relative pose estimation with a lightweight multi-layer-perceptron (MLP) policy. RGB-D keyframes extracted from human-driven traversals form nodes of a topological map; edges are added when visual similarity and geometric–kinematic constraints are jointly satisfied. During autonomy, LightGlue features and SVD give six-DoF relative pose to the active keyframe, and the MLP predicts one of four discrete actions. Low visual similarity or detected obstacles trigger graph editing and Dijkstra replanning in real time. Across eight tasks in four Habitat-Sim environments, the agent covered 190.44 m, replanning when required, and consistently stopped within 0.1 m of the goal while running on commodity hardware. An information-theoretic analysis over the Multi-Illumination dataset shows that LightGlue maximizes per-second information gain under lighting changes, motivating its selection. The modular design attains reliable navigation without metric SLAM or large-scale learning, and seamlessly accommodates future perception or policy upgrades. Full article
(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing, 3rd Edition)
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