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Computational Intelligence in Image Processing and Pattern Recognition

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 6736

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Special Issue Editors

Prof. Dr. Xianye Ben

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Guest Editor

School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Interests: image processing; pattern recognition; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Peng Zhang

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Guest Editor

College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: computer vision; deep learning; pattern recognition

Prof. Dr. Tao Lei

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School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China
Interests: image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Chen

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Guest Editor

School of Information Science and Engineering, Shandong University, Ji’nan 250100, China
Interests: image processing; computer vision; deep learning

Special Issue Information

Dear Colleagues,

The past decades have witnessed the great success of image processing and pattern recognition in many fields such as image denoising, image synthetization and translation, biometric recognition, etc. Automated image analysis and information extraction from mass real-world data have significantly increased productivity in practical engineering applications. Especially, deep learning and other advanced methods are accelerating the revolution. Computational intelligence will play a key role in the revolution due to its potential power in information processing, decision making and knowledge management.

This Special Issue will gather recent advances in both theoretical and practical studies of computational intelligence, emphasizing image processing and pattern recognition. Potential topics include, but are not limited to, the use of computational intelligence techniques such as neural networks, fuzzy logic, metaheuristics and expert systems in the following topics:

Image processing, including morphology, filtering and enhancement, etc.;
Supervised/semi-supervised/unsupervised learning;
Reinforcement learning;
Deep learning theory and applications;
Pattern recognition;
Computer vision;
Natural language processing;
Time-series analysis;
Data mining.

Prof. Dr. Xianye Ben
Dr. Peng Zhang
Prof. Dr. Tao Lei
Prof. Dr. Lei Chen
Guest Editors

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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. Mathematics 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

image processing
pattern recognition
machine learning
deep learning
computer vision
natural language processing
data mining

Published Papers (4 papers)

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Research

17 pages, 9119 KiB

Open AccessArticle

Learning Wasserstein Contrastive Color Histogram Representation for Low-Light Image Enhancement

by Zixuan Sun, Shenglong Hu, Huihui Song and Peng Liang

Mathematics 2023, 11(19), 4194; https://doi.org/10.3390/math11194194 - 8 Oct 2023

Cited by 1 | Viewed by 884

Abstract

The goal of low-light image enhancement (LLIE) is to enhance perception to restore normal-light images. The primary emphasis of earlier LLIE methods was on enhancing the illumination while paying less attention to the color distortions and noise in the dark. In comparison to the ground truth, the restored images frequently exhibit inconsistent color and residual noise. To this end, this paper introduces a Wasserstein contrastive regularization method (WCR) for LLIE. The WCR regularizes the color histogram (CH) representation of the restored image to keep its color consistency while removing noise. Specifically, the WCR contains two novel designs including a differentiable CH module (DCHM) and a WCR loss. The DCHM serves as a modular component that can be easily integrated into the network to enable end-to-end learning of the image CH. Afterwards, to ensure color consistency, we utilize the Wasserstein distance (WD) to quantify the resemblance of the learnable CHs between the restored image and the normal-light image. Then, the regularized WD is used to construct the WCR loss, which is a triplet loss and takes the normal-light images as positive samples, the low-light images as negative samples, and the restored images as anchor samples. The WCR loss pulls the anchor samples closer to the positive samples and simultaneously pushes them away from the negative samples so as to help the anchors remove the noise in the dark. Notably, the proposed WCR method was only used for training, and was shown to achieve high performance and high speed inference using lightweight networks. Therefore, it is valuable for real-time applications such as night automatic driving and night reversing image enhancement. Extensive evaluations on benchmark datasets such as LOL, FiveK, and UIEB showed that the proposed WCR method achieves superior performance, outperforming existing state-of-the-art methods. Full article

(This article belongs to the Special Issue Computational Intelligence in Image Processing and Pattern Recognition)

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15 pages, 5694 KiB

Open AccessArticle

Full-Reference Image Quality Assessment with Transformer and DISTS

by Pei-Fen Tsai, Huai-Nan Peng, Chia-Hung Liao and Shyan-Ming Yuan

Mathematics 2023, 11(7), 1599; https://doi.org/10.3390/math11071599 - 26 Mar 2023

Cited by 1 | Viewed by 1804

Abstract

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images. Full article

(This article belongs to the Special Issue Computational Intelligence in Image Processing and Pattern Recognition)

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11 pages, 1484 KiB

Open AccessArticle

Developing a Spine Internal Rotation Angle Measurement System Based Machine Learning Using CT Reconstructed X-ray Anteroposterior Image

by Tae-Seok Kang and Seung-Man Yu

Mathematics 2022, 10(24), 4781; https://doi.org/10.3390/math10244781 - 15 Dec 2022

Cited by 1 | Viewed by 1425

Abstract

The purpose of this study was to develop a predictive model for estimating the rotation angle of the vertebral body on X-ray anteroposterior projection (AP) image by applying machine learning. This study is intended to replace internal/external rotation of the thoracic spine (T-spine), which can only be observed through computed tomography (CT), with an X-ray AP image. 3-dimension (3D) T-spine CT images were used to acquired reference spine axial angle and various internal rotation T-spine reconstructed X-ray AP image. Distance from the pedicle to the outside of the spine and change in distance between the periphery of the pedicle according to the rotation of the spine were designated as main variables using reconstructed X-ray AP image. The number of measured spines was 453 and the number of variables for each spine was 13, creating a total of 5889 data. We applied a total of 24 regression machine learning methods using MATLAB software, performed learning with the acquired data, and finally, the Gaussian regression method showed the lowest RMSE value. X-rays obtained with the phantom of the human body tilted by 16 degrees showed results with reproducibility within the RMSE range. Full article

(This article belongs to the Special Issue Computational Intelligence in Image Processing and Pattern Recognition)

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22 pages, 3895 KiB

Open AccessArticle

Research on a Decision Prediction Method Based on Causal Inference and a Multi-Expert FTOPJUDGE Mechanism

by Qiang Zhao, Rundong Guo, Xiaowei Feng, Weifeng Hu, Siwen Zhao, Zihan Wang, Yujun Li and Yewen Cao

Mathematics 2022, 10(13), 2281; https://doi.org/10.3390/math10132281 - 29 Jun 2022

Viewed by 1394

Abstract

Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal inference and a multi-expert FTOPJUDGE mechanism. First, a causal inference algorithm was adopted to process unstructured text. This process did not require very much manual intervention to better mine the information in the text. Then, a neural network dedicated to each task was set up, and a neural network that simultaneously served multiple tasks was also set up. Finally, the pre-trained language model Lawformer was used to provide knowledge for downstream tasks. By using the public data set CAIL2018 and comparing it with current mainstream decision prediction models, it was shown that the model significantly improved the performance of downstream tasks and achieved great improvements in multiple indicators. Through ablation experiments, the effectiveness and rationality of each module of the proposed model were verified. The method proposed in this study achieved reasonably good performance in legal judgment prediction, which provides a promising solution for legal judgment prediction. Full article

(This article belongs to the Special Issue Computational Intelligence in Image Processing and Pattern Recognition)

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