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Special Issue "Application of Information Theory to Computer Vision and Image Processing"

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

Deadline for manuscript submissions: 3 July 2023 | Viewed by 5416

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Special Issue Editors

Dr. Wendy Flores-Fuentes

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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

Dr. Oleg Sergiyenko

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

Dr. Julio Cesar Rodriguez-Quinonez

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Guest Editor

Engineering Faculty, Universidad Autónoma de Baja California, Mexicali 21100, Mexico
Interests: machine vision; stereo vision; systems laser; scanner control; digital image processing

Dr. Jesús Elías Miranda-Vega

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Guest Editor

Tecnológico Nacional de México, Mexicali 21376, Mexico
Interests: machine vision; stereo vision; systems laser; scanner control; analogic and digital processing

Special Issue Information

Dear Colleagues,

World perception is the product of complex optical and physical processes of the human visual system, allowing light stimuli to penetrate through the pupils to reach the retina composed of photoreceptors that transform light into electrochemical energy, which can then be transmitted to the brain to organize, interpret, and analyze the information received and create a perceived reality. In a similar optical and physical process, machine vision is the eyes of cybernetic systems for joining the virtual and real world to coexist in human lives, looking to integrate this technology into our daily lives with creativity and globalization view through interconnectivity. This is possible due to the advanced technologies of sensors and systems to acquire and compute information. Such tasks are based on the integration of optoelectronics devices for sensors and cameras. Sensors, artificial intelligence algorithms, embedded systems, robust control, inertial navigation systems, robotics, interconnectivity, Big Data, information interchange within robotic swarms, and cloud computing form the basis of machine vision developments for cyber-physical systems to collaborate with humans and their real and virtual environments and activities.

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 via the application of computer vision and image processing.

Dr. Wendy Flores-Fuentes
Dr. Oleg Sergiyenko
Dr. Julio Cesar Rodriguez-Quinonez
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 2000 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

machine vision
cyber-physical systems
navigation
3D spatial coordinates
information theory applications
data interchange
instrumentation
measurements
artificial intelligence
signal and image processing

Published Papers (7 papers)

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Research

Open AccessArticle

Structured Cluster Detection from Local Feature Learning for Text Region Extraction

Huei-Yung Lin

and

Chin-Yu Hsu

Entropy 2023, 25(4), 658; https://doi.org/10.3390/e25040658 - 14 Apr 2023

Viewed by 425

Abstract

The detection of regions of interest is commonly considered as an early stage of information extraction from images. It is used to provide the contents meaningful to human perception for machine vision applications. In this work, a new technique for structured region detection based on the distillation of local image features with clustering analysis is proposed. Different from the existing methods, our approach takes the application-specific reference images for feature learning and extraction. It is able to identify text clusters under the sparsity of feature points derived from the characters. For the localization of structured regions, the cluster with high feature density is calculated and serves as a candidate for region expansion. An iterative adjustment is then performed to enlarge the ROI for complete text coverage. The experiments carried out for text region detection of invoice and banknote demonstrate the effectiveness of the proposed technique. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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Open AccessArticle

Hybrid Multi-Dimensional Attention U-Net for Hyperspectral Snapshot Compressive Imaging Reconstruction

Siming Zheng

Mingyu Zhu

and

Mingliang Chen

Entropy 2023, 25(4), 649; https://doi.org/10.3390/e25040649 - 12 Apr 2023

Viewed by 510

Abstract

In order to capture the spatial-spectral

(x, y, λ)

In order to capture the spatial-spectral

(x, y, λ)

information of the scene, various techniques have been proposed. Different from the widely used scanning-based methods, spectral snapshot compressive imaging (SCI) utilizes the idea of compressive sensing to compressively capture the 3D spatial-spectral data-cube in a single-shot 2D measurement and thus it is efficient, enjoying the advantages of high-speed and low bandwidth. However, the reconstruction process, i.e., to retrieve the 3D cube from the 2D measurement, is an ill-posed problem and it is challenging to reconstruct high quality images. Previous works usually use 2D convolutions and preliminary attention to address this challenge. However, these networks and attention do not exactly extract spectral features. On the other hand, 3D convolutions can extract more features in a 3D cube, but increase computational cost significantly. To balance this trade-off, in this paper, we propose a hybrid multi-dimensional attention U-Net (HMDAU-Net) to reconstruct hyperspectral images from the 2D measurement in an end-to-end manner. HMDAU-Net integrates 3D and 2D convolutions in an encoder–decoder structure to fully utilize the abundant spectral information of hyperspectral images with a trade-off between performance and computational cost. Furthermore, attention gates are employed to highlight salient features and suppress the noise carried by the skip connections. Our proposed HMDAU-Net achieves superior performance over previous state-of-the-art reconstruction algorithms. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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Open AccessArticle

Multi-Receptive Field Soft Attention Part Learning for Vehicle Re-Identification

Xiyu Pang

Yilong Yin

and

Yanli Zheng

Entropy 2023, 25(4), 594; https://doi.org/10.3390/e25040594 - 31 Mar 2023

Viewed by 404

Abstract

Vehicle re-identification across multiple cameras is one of the main problems of intelligent transportation systems (ITSs). Since the differences in the appearance between different vehicles of the same model are small and the appearance of the same vehicle changes drastically from different viewpoints, vehicle re-identification is a challenging task. In this paper, we propose a model called multi-receptive field soft attention part learning (MRF-SAPL). The MRF-SAPL model learns semantically diverse vehicle part-level features under different receptive fields through multiple local branches, alleviating the problem of small differences in vehicle appearance. To align vehicle parts from different images, this study uses soft attention to adaptively locate the positions of the parts on the final feature map generated by a local branch and maintain the continuity of the internal semantics of the parts. In addition, to obtain parts with different semantic patterns, we propose a new loss function that punishes overlapping regions, forcing the positions of different parts on the same feature map to not overlap each other as much as possible. Extensive ablation experiments demonstrate the effectiveness of our part-level feature learning method MRF-SAPL, and our model achieves state-of-the-art performance on two benchmark datasets. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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Open AccessArticle

Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes

and

Entropy 2023, 25(2), 353; https://doi.org/10.3390/e25020353 - 14 Feb 2023

Viewed by 659

Abstract

This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029%, and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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Open AccessArticle

An Infusion Containers Detection Method Based on YOLOv4 with Enhanced Image Feature Fusion

and

Entropy 2023, 25(2), 275; https://doi.org/10.3390/e25020275 - 02 Feb 2023

Viewed by 644

Abstract

The detection of infusion containers is highly conducive to reducing the workload of medical staff. However, when applied in complex environments, the current detection solutions cannot satisfy the high demands for clinical requirements. In this paper, we address this problem by proposing a novel method for the detection of infusion containers that is based on the conventional method, You Only Look Once version 4 (YOLOv4). First, the coordinate attention module is added after the backbone to improve the perception of direction and location information by the network. Then, we build the cross stage partial–spatial pyramid pooling (CSP-SPP) module to replace the spatial pyramid pooling (SPP) module, which allows the input information features to be reused. In addition, the adaptively spatial feature fusion (ASFF) module is added after the original feature fusion module, path aggregation network (PANet), to facilitate the fusion of feature maps at different scales for more complete feature information. Finally, EIoU is used as a loss function to solve the anchor frame aspect ratio problem, and this improvement allows for more stable and accurate information of the anchor aspect when calculating losses. The experimental results demonstrate the advantages of our method in terms of recall, timeliness, and mean average precision (mAP). Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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Open AccessArticle

Image Registration for Visualizing Magnetic Flux Leakage Testing under Different Orientations of Magnetization

and

Entropy 2023, 25(1), 167; https://doi.org/10.3390/e25010167 - 13 Jan 2023

Viewed by 650

Abstract

The Magnetic Flux Leakage (MFL) visualization technique is widely used in the surface defect inspection of ferromagnetic materials. However, the information of the images detected through the MFL method is incomplete when the defect (especially for the cracks) is complex, and some information would be lost when magnetized unidirectionally. Then, the multidirectional magnetization method is proposed to fuse the images detected under different magnetization orientations. It causes a critical problem: the existing image registration methods cannot be applied to align the images because the images are different when detected under different magnetization orientations. This study presents a novel image registration method for MFL visualization to solve this problem. In order to evaluate the registration, and to fuse the information detected in different directions, the mutual information between the reference image and the MFL image calculated by the forward model is designed as a measure. Furthermore, Particle Swarm Optimization (PSO) is used to optimize the registration process. The comparative experimental results demonstrate that this method has a higher registration accuracy for the MFL images of complex cracks than the existing methods. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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Open AccessArticle

Scale Enhancement Pyramid Network for Small Object Detection from UAV Images

and

Entropy 2022, 24(11), 1699; https://doi.org/10.3390/e24111699 - 21 Nov 2022

Viewed by 952

Abstract

Object detection is challenging in large-scale images captured by unmanned aerial vehicles (UAVs), especially when detecting small objects with significant scale variation. Most solutions employ the fusion of different scale features by building multi-scale feature pyramids to ensure that the detail and semantic information are abundant. Although feature fusion benefits object detection, it still requires the long-range dependencies information necessary for small objects with significant scale variation detection. We propose a simple yet effective scale enhancement pyramid network (SEPNet) to address these problems. A SEPNet consists of a context enhancement module (CEM) and feature alignment module (FAM). Technically, the CEM combines multi-scale atrous convolution and multi-branch grouped convolution to model global relationships. Additionally, it enhances object feature representation, preventing features with lost spatial information from flowing into the feature pyramid network (FPN). The FAM adaptively learns offsets of pixels to preserve feature consistency. The FAM aims to adjust the location of sampling points in the convolutional kernel, effectively alleviating information conflict caused by the fusion of adjacent features. Results indicate that the SEPNet achieves an AP score of 18.9% on VisDrone, which is 7.1% higher than the AP score of state-of-the-art detectors RetinaNet achieves an AP score of 81.5% on PASCAL VOC. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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