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Application of Information Theory to Computer Vision and Image Processing II

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6033

Special Issue Editors


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Guest Editor
Engineering Faculty, Universidad Autónoma de Baja California, Mexicali 21100, Mexico
Interests: optics; structural health monitoring; machine vision; remote sensing; support vector machine; measurement error; mobile robotics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Applied Physics, Autonomous University of Baja California, Mexicali 21100, Mexico
Interests: automated metrology; 3D coordinates measurement; robotic navigation; machine vision; simulation of the robotic swarms behaviour
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Engineering Faculty, Universidad Autónoma de Baja California, Mexicali 21100, Mexico
Interests: machine vision; stereo vision; systems laser; scanner control; digital image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Systems, Tecnológico Nacional de México, IT de Mexicali, Mexicali 21376, Mexico
Interests: machine vision; stereo vision; systems laser; scanner control; analogic and digital processing
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 “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 II” is open to continue the inclusion of relevant papers of related topics.

The application of information theory to computer vision and image processing has significantly contributed to advancing the understanding and capabilities of computer science. Mathematics methods are applied to signal and image processing for quantifying and obtaining accurate information with enhanced efficiency upon every innovation. Providing 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, including the entropy theory to estimate the average amount of uncertainty or randomness in a dataset, where a high entropy indicates a higher level of unpredictability, while low entropy suggests a more predictable and structured dataset.

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

Dr. Wendy Flores-Fuentes
Dr. Oleg Sergiyenko
Prof. Dr. Julio Cesar Rodríguez-Quiñonez
Dr. Jesús Elías Miranda-Vega
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)

Related Special Issue

Published Papers (5 papers)

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Research

35 pages, 6001 KiB  
Article
Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images
by Amal Altamimi and Belgacem Ben Youssef
Entropy 2024, 26(4), 316; https://doi.org/10.3390/e26040316 - 5 Apr 2024
Viewed by 953
Abstract
Rapid and continuous advancements in remote sensing technology have resulted in finer resolutions and higher acquisition rates of hyperspectral images (HSIs). These developments have triggered a need for new processing techniques brought about by the confined power and constrained hardware resources aboard satellites. [...] Read more.
Rapid and continuous advancements in remote sensing technology have resulted in finer resolutions and higher acquisition rates of hyperspectral images (HSIs). These developments have triggered a need for new processing techniques brought about by the confined power and constrained hardware resources aboard satellites. This article proposes two novel lossless and near-lossless compression methods, employing our recent seed generation and quadrature-based square rooting algorithms, respectively. The main advantage of the former method lies in its acceptable complexity utilizing simple arithmetic operations, making it suitable for real-time onboard compression. In addition, this near-lossless compressor could be incorporated for hard-to-compress images offering a stabilized reduction at nearly 40% with a maximum relative error of 0.33 and a maximum absolute error of 30. Our results also show that a lossless compression performance, in terms of compression ratio, of up to 2.6 is achieved when testing with hyperspectral images from the Corpus dataset. Further, an improvement in the compression rate over the state-of-the-art k2-raster technique is realized for most of these HSIs by all four variations of our proposed lossless compression method. In particular, a data reduction enhancement of up to 29.89% is realized when comparing their respective geometric mean values. Full article
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18 pages, 7216 KiB  
Article
Style-Enhanced Transformer for Image Captioning in Construction Scenes
by Kani Song, Linlin Chen and Hengyou Wang
Entropy 2024, 26(3), 224; https://doi.org/10.3390/e26030224 - 1 Mar 2024
Viewed by 1056
Abstract
Image captioning is important for improving the intelligence of construction projects and assisting managers in mastering construction site activities. However, there are few image-captioning models for construction scenes at present, and the existing methods do not perform well in complex construction scenes. According [...] Read more.
Image captioning is important for improving the intelligence of construction projects and assisting managers in mastering construction site activities. However, there are few image-captioning models for construction scenes at present, and the existing methods do not perform well in complex construction scenes. According to the characteristics of construction scenes, we label a text description dataset based on the MOCS dataset and propose a style-enhanced Transformer for image captioning in construction scenes, simply called SETCAP. Specifically, we extract the grid features using the Swin Transformer. Then, to enhance the style information, we not only use the grid features as the initial detail semantic features but also extract style information by style encoder. In addition, in the decoder, we integrate the style information into the text features. The interaction between the image semantic information and the text features is carried out to generate content-appropriate sentences word by word. Finally, we add the sentence style loss into the total loss function to make the style of generated sentences closer to the training set. The experimental results show that the proposed method achieves encouraging results on both the MSCOCO and the MOCS datasets. In particular, SETCAP outperforms state-of-the-art methods by 4.2% CIDEr scores on the MOCS dataset and 3.9% CIDEr scores on the MSCOCO dataset, respectively. Full article
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16 pages, 27918 KiB  
Article
Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement
by Hua Wang, Jianzhong Cao and Jijiang Huang
Entropy 2024, 26(3), 184; https://doi.org/10.3390/e26030184 - 22 Feb 2024
Viewed by 878
Abstract
Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to [...] Read more.
Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF. Full article
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21 pages, 5149 KiB  
Article
A Real-Time and Robust Neural Network Model for Low-Measurement-Rate Compressed-Sensing Image Reconstruction
by Pengchao Chen, Huadong Song, Yanli Zeng, Xiaoting Guo and Chaoqing Tang
Entropy 2023, 25(12), 1648; https://doi.org/10.3390/e25121648 - 12 Dec 2023
Viewed by 902
Abstract
Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for [...] Read more.
Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for robust reconstruction results. This paper proposes a neural network called RootsNet, which integrates the CS mechanism into the network to prevent error propagation. So, RootsNet knows what will happen if some modules in the network go wrong. It also implements real-time and successfully reconstructed extremely low measurement rates that are impossible for traditional optimization-theory-based methods. For qualitative validation, RootsNet is implemented in two real-world measurement applications, i.e., a near-field microwave imaging system and a pipeline inspection system, where RootsNet easily saves 60% more measurement time and 95% more data compared with the state-of-the-art optimization-theory-based reconstruction methods. Without losing generality, comprehensive experiments are performed on general datasets, including evaluating the key components in RootsNet, the reconstruction uncertainty, quality, and efficiency. RootsNet has the best uncertainty performance and efficiency, and achieves the best reconstruction quality under super low-measurement rates. Full article
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17 pages, 5045 KiB  
Article
Part-Aware Point Cloud Completion through Multi-Modal Part Segmentation
by Fuyang Yu, Runze Tian, Xuanjun Wang and Xiaohui Liang
Entropy 2023, 25(12), 1588; https://doi.org/10.3390/e25121588 - 27 Nov 2023
Viewed by 1081
Abstract
Point cloud completion aims to generate high-resolution point clouds using incomplete point clouds as input and is the foundational task for many 3D visual applications. However, most existing methods suffer from issues related to rough localized structures. In this paper, we attribute these [...] Read more.
Point cloud completion aims to generate high-resolution point clouds using incomplete point clouds as input and is the foundational task for many 3D visual applications. However, most existing methods suffer from issues related to rough localized structures. In this paper, we attribute these problems to the lack of attention to local details in the global optimization methods used for the task. Thus, we propose a new model, called PA-NET, to guide the network to pay more attention to local structures. Specifically, we first use textual embedding to assist in training a robust point assignment network, enabling the transformation of global optimization into the co-optimization of local and global aspects. Then, we design a novel plug-in module using the assignment network and introduce a new loss function to guide the network’s attention towards local structures. Numerous experiments were conducted, and the quantitative results demonstrate that our method achieves novel performance on different datasets. Additionally, the visualization results show that our method efficiently resolves the issue of poor local structures in the generated point cloud. Full article
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