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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 2023) | Viewed by 27592

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Special Issue Editors

Dr. Wen-Yu Chung

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Guest Editor

Department of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
Interests: bioinformatics; computational biology; graph theory; machine learning; neural networks
Special Issues, Collections and Topics in MDPI journals

Dr. Sebastian Iwaszenko

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Guest Editor

Department of Acoustic, Electronic and IT Solutions, GIG National Research Institute, Plac Gwarków 1, 40-166 Katowice, Poland
Interests: computer science; computer vision; image processing; image analysis; machine learning; artificial intelligence; software engineering
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Special Issue Information

Dear Colleagues,

We live in a society where digital technology is everywhere and in every aspect of our lives, with new and advanced usages being constantly proposed. This has set off a growing need to develop novel algorithms and integrated approaches to not only efficiently, but also accurately retrieve information from the quickly accumulated data.

Pattern recognition has generated research interests since the early years of computer science and plays an important role in understanding the patterns and regularities in data. Image analysis is an exciting research area with roots in machine learning and artificial intelligence. Recent developments in neural networks show that some models can perform well in image classification and recognition.

To address the challenges brought up by digital data, more research needs to be conducted in pattern recognition and image analysis. Therefore, in this Special Issue, we aim to include original and recent work or reviews in methodologies, technologies or applications. We welcome manuscripts relating, but not limited, to the following areas: signal processing, object detection, computer vision, motion detection, machine learning and artificial intelligence.

Prof. Dr. Wen-Yu Chung
Dr. Sebastian Iwaszenko
Guest Editors

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Keywords

signal processing
information retrieval
artificial intelligence
object detection and recognition
computer vision
machine learning
few or zero-shot learning
image segmentation
motion detection
3D pose detection or estimation

Published Papers (12 papers)

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Research

16 pages, 5507 KiB

Open AccessArticle

Application of Fractional Differential Model in Image Enhancement of Strong Reflection Surface

by Tang Ruiyin and Liu Bo

Mathematics 2023, 11(2), 444; https://0-doi-org.brum.beds.ac.uk/10.3390/math11020444 - 13 Jan 2023

Viewed by 1105

Abstract

Combined with advanced fractional differential mask operation, this paper used a fractional differential to normalize the 5 × 5 mask and conducted experiments to select fractional v = 0.7 to determine the equation. The position of the center of the light band was obtained by the gray centroid method, and the center of the light band was enhanced by the fractional differential algorithm. Three samples of hard disk substrate, roller, and printed circuit board were selected. The traditional processing was compared to the fractional differential algorithm in this paper, and several advanced algorithms were compared with the algorithm in this paper. Experimental data showed that fractional differential enhancement can effectively improve the accuracy of extracting the center of light fringes. It can be found that the average error of extracting the center by fractional differential processing was relatively small, and the image outline was clearer. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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15 pages, 7055 KiB

Open AccessArticle

Sea–Land Segmentation Using HED-UNET for Monitoring Kaohsiung Port

by Shih-Huan Tseng and Wei-Hao Sun

Mathematics 2022, 10(22), 4202; https://0-doi-org.brum.beds.ac.uk/10.3390/math10224202 - 10 Nov 2022

Cited by 1 | Viewed by 1355

Abstract

In recent years, it has become a trend to analyze shoreline changes through satellite images in coastal engineering research. The results of sea–land segmentation are very important for shoreline detection. CoastSat is a time-series shoreline detection system that uses an artificial neural network (ANN) on sea–land segmentation. However, the method of CoastSat only uses the spectral features of a single pixel and ignores the local relationships of adjacent pixels. This impedes optimal category prediction, particularly considering interference by climate features such as clouds, shadows, and waves. It is easy to cause the classifier to be disturbed in the classification results, resulting in classification errors. To solve the problem of misclassification of sea–land segmentation caused by climate interference, this paper applies HED-UNet to the image dataset obtained from CoastSat and learns the relationship between adjacent pixels by training the deep network architecture, thereby improving the results of erroneous sea–land segmentation due to climate disturbances. By using different optimizers and loss functions in the HED-Unet model, the experiment verifies that Adam + Focal loss has the best performance. The results also show that the deep learning model, HED-Unet, can effectively improve the accuracy of the sea–land segmentation to 97% in a situation with interference from atmospheric factors such as clouds and waves. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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16 pages, 5473 KiB

Open AccessArticle

Infusion-Net: Inter- and Intra-Weighted Cross-Fusion Network for Multispectral Object Detection

by Jun-Seok Yun, Seon-Hoo Park and Seok Bong Yoo

Mathematics 2022, 10(21), 3966; https://doi.org/10.3390/math10213966 - 25 Oct 2022

Cited by 8 | Viewed by 2000

Abstract

Object recognition is conducted using red, green, and blue (RGB) images in object recognition studies. However, RGB images in low-light environments or environments where other objects occlude the target objects cause poor object recognition performance. In contrast, infrared (IR) images provide acceptable object recognition performance in these environments because they detect IR waves rather than visible illumination. In this paper, we propose an inter- and intra-weighted cross-fusion network (Infusion-Net), which improves object recognition performance by combining the strengths of the RGB-IR image pairs. Infusion-Net connects dual object detection models using a high-frequency (HF) assistant (HFA) to combine the advantages of RGB-IR images. To extract HF components, the HFA transforms input images into a discrete cosine transform domain. The extracted HF components are weighted via pretrained inter- and intra-weights for feature-domain cross-fusion. The inter-weighted fused features are transmitted to each other’s networks to complement the limitations of each modality. The intra-weighted features are also used to enhance any insufficient HF components of the target objects. Thus, the experimental results present the superiority of the proposed network and present improved performance of the multispectral object recognition task. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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24 pages, 1861 KiB

Open AccessArticle

Bayesian Linear Regression and Natural Logarithmic Correction for Digital Image-Based Extraction of Linear and Tridimensional Zoometrics in Dromedary Camels

by Carlos Iglesias Pastrana, Francisco Javier Navas González, Elena Ciani, María Esperanza Camacho Vallejo and Juan Vicente Delgado Bermejo

Mathematics 2022, 10(19), 3453; https://0-doi-org.brum.beds.ac.uk/10.3390/math10193453 - 22 Sep 2022

Cited by 2 | Viewed by 2103

Abstract

This study evaluates a method to accurately, repeatably, and reliably extract camel zoo-metric data (linear and tridimensional) from 2D digital images. Thirty zoometric measures, including linear and tridimensional (perimeters and girths) variables, were collected on-field with a non-elastic measuring tape. A scaled reference was used to extract measurement from images. For girths and perimeters, semimajor and semiminor axes were mathematically estimated with the function of the perimeter of an ellipse. On-field measurements’ direct translation was determined when Cronbach’s alpha (Cα) > 0.600 was met (first round). If not, Bayesian regression corrections were applied using live body weight and the particular digital zoometric measurement as regressors (except for foot perimeter) (second round). Last, if a certain zoometric trait still did not meet such a criterion, its natural logarithm was added (third round). Acceptable method translation consistency was reached for all the measurements after three correction rounds (Cα = 0.654 to 0.997, p < 0.0001). Afterwards, Bayesian regression corrected equations were issued. This research helps to evaluate individual conformation in a reliable contactless manner through the extraction of linear and tridimensional measures from images in dromedary camels. This is the first study to develop and correct the routinely ignored evaluation of tridimensional zoometrics from digital images in animals. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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18 pages, 5569 KiB

Open AccessArticle

Rain Rendering and Construction of Rain Vehicle Color-24 Dataset

by Mingdi Hu, Chenrui Wang, Jingbing Yang, Yi Wu, Jiulun Fan and Bingyi Jing

Mathematics 2022, 10(17), 3210; https://0-doi-org.brum.beds.ac.uk/10.3390/math10173210 - 05 Sep 2022

Cited by 9 | Viewed by 1851

Abstract

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for [...] Read more.

R a i n V e h i c l e C o l o r

-24 dataset by rain-image rendering using

P S

technology and a

S y R a G A N

algorithm based on the

V e h i c l e C o l o r

-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset

R a i n V e h i c l e C o l o r

-24 improve accuracy to around

72 %

and

90 %

on rainy and sunny days, respectively. The code is available at [email protected]. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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28 pages, 3543 KiB

Open AccessArticle

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

by Sadiq H. Abdulhussain, Basheera M. Mahmmod, Amer AlGhadhban and Jan Flusser

Mathematics 2022, 10(15), 2721; https://0-doi-org.brum.beds.ac.uk/10.3390/math10152721 - 01 Aug 2022

Cited by 15 | Viewed by 2242

Abstract

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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17 pages, 2666 KiB

Open AccessArticle

Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing

by Xiaohan Yang, Zhen Wang, Nannan Wu, Guokun Li, Chuang Feng and Pingping Liu

Mathematics 2022, 10(15), 2644; https://0-doi-org.brum.beds.ac.uk/10.3390/math10152644 - 28 Jul 2022

Cited by 1 | Viewed by 1150

Abstract

The image-text cross-modal retrieval task, which aims to retrieve the relevant image from text and vice versa, is now attracting widespread attention. To quickly respond to the large-scale task, we propose an Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing (DRNPH) to achieve cross-modal retrieval in the common Hamming space, which has the advantages of storage and efficiency. To fulfill the nearest neighbor search in the Hamming space, we demand to reconstruct both the original intra- and inter-modal neighbor matrix according to the binary feature vectors. Thus, we can compute the neighbor relationship among different modal samples directly based on the Hamming distances. Furthermore, the cross-modal pair-wise similarity preserving constraint requires the similar sample pair have an identical Hamming distance to the anchor. Therefore, the similar sample pairs own the same binary code, and they have minimal Hamming distances. Unfortunately, the pair-wise similarity preserving constraint may lead to an imbalanced code problem. Therefore, we propose the cross-modal triplet relative similarity preserving constraint, which demands the Hamming distances of similar pairs should be less than those of dissimilar pairs to distinguish the samples’ ranking orders in the retrieval results. Moreover, a large similarity marginal can boost the algorithm’s noise robustness. We conduct the cross-modal retrieval comparative experiments and ablation study on two public datasets, MIRFlickr and NUS-WIDE, respectively. The experimental results show that DRNPH outperforms the state-of-the-art approaches in various image-text retrieval scenarios, and all three proposed constraints are necessary and effective for boosting cross-modal retrieval performance. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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16 pages, 2443 KiB

Open AccessArticle

Analysis of Different Image Enhancement and Feature Extraction Methods

by Lucero Verónica Lozano-Vázquez, Jun Miura, Alberto Jorge Rosales-Silva, Alberto Luviano-Juárez and Dante Mújica-Vargas

Mathematics 2022, 10(14), 2407; https://0-doi-org.brum.beds.ac.uk/10.3390/math10142407 - 09 Jul 2022

Cited by 5 | Viewed by 2879

Abstract

This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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24 pages, 8999 KiB

Open AccessArticle

Artificial Intelligence-Based Tissue Phenotyping in Colorectal Cancer Histopathology Using Visual and Semantic Features Aggregation

by Tahir Mahmood, Seung Gu Kim, Ja Hyung Koo and Kang Ryoung Park

Mathematics 2022, 10(11), 1909; https://0-doi-org.brum.beds.ac.uk/10.3390/math10111909 - 02 Jun 2022

Cited by 4 | Viewed by 2048

Abstract

Tissue phenotyping of the tumor microenvironment has a decisive role in digital profiling of intra-tumor heterogeneity, epigenetics, and progression of cancer. Most of the existing methods for tissue phenotyping often rely on time-consuming and error-prone manual procedures. Recently, with the advent of advanced technologies, these procedures have been automated using artificial intelligence techniques. In this paper, a novel deep histology heterogeneous feature aggregation network (HHFA-Net) is proposed based on visual and semantic information fusion for the detection of tissue phenotypes in colorectal cancer (CRC). We adopted and tested various data augmentation techniques to avoid computationally expensive stain normalization procedures and handle limited and imbalanced data problems. Three publicly available datasets are used in the experiments: CRC tissue phenotyping (CRC-TP), CRC histology (CRCH), and colon cancer histology (CCH). The proposed HHFA-Net achieves higher accuracies than the state-of-the-art methods for tissue phenotyping in CRC histopathology images. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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27 pages, 12341 KiB

Open AccessArticle

Deep Learning-Based Detection of Fake Multinational Banknotes in a Cross-Dataset Environment Utilizing Smartphone Cameras for Assisting Visually Impaired Individuals

by Tuyen Danh Pham, Young Won Lee, Chanhum Park and Kang Ryoung Park

Mathematics 2022, 10(9), 1616; https://0-doi-org.brum.beds.ac.uk/10.3390/math10091616 - 09 May 2022

Cited by 5 | Viewed by 4000

Abstract

The automatic handling of banknotes can be conducted not only by specialized facilities, such as vending machines, teller machines, and banknote counters, but also by handheld devices, such as smartphones, with the utilization of built-in cameras and detection algorithms. As smartphones are becoming increasingly popular, they can be used to assist visually impaired individuals in daily tasks, including banknote handling. Although previous studies regarding banknote detection by smartphone cameras for visually impaired individuals have been conducted, these studies are limited, even when conducted in a cross-dataset environment. Therefore, we propose a deep learning-based method for detecting fake multinational banknotes using smartphone cameras in a cross-dataset environment. Experimental results of the self-collected genuine and fake multinational datasets for US dollar, Euro, Korean won, and Jordanian dinar banknotes confirm that our method demonstrates a higher detection accuracy than conventional “you only look once, version 3” (YOLOv3) methods and the combined method of YOLOv3 and the state-of-the-art convolutional neural network (CNN). Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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19 pages, 8171 KiB

Open AccessArticle

Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition

by Tongyuan Huang, Jia Xu, Yuling Yang and Baoru Han

Mathematics 2022, 10(7), 1154; https://0-doi-org.brum.beds.ac.uk/10.3390/math10071154 - 02 Apr 2022

Cited by 15 | Viewed by 2022

Abstract

With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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14 pages, 543 KiB

Open AccessArticle

Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks

by Wei-Cheng Lin and Yi-Ren Yeh

Mathematics 2022, 10(4), 608; https://0-doi-org.brum.beds.ac.uk/10.3390/math10040608 - 16 Feb 2022

Cited by 17 | Viewed by 3596

Abstract

The rapid increase of malware attacks has become one of the main threats to computer security. Finding the best way to detect malware has become a critical task in cybersecurity. Previous work shows that machine learning approaches could be a solution to address this problem. Many proposed methods convert malware executables into grayscale images and apply convolutional neural networks (CNNs) for malware classification. However, converting malware executables into images could twist the one-dimensional structure of binary codes. To address this problem, we explore the bit and byte-level sequences from malware executables and propose efficient one-dimensional (1D) CNNs for the malware classification. Our experiments evaluate our proposed 1D CNN models with two benchmark datasets. Our proposed 1D CNN models achieve better performance from the experimental results than the existing 2D CNNs malware classification models by providing smaller resizing bit/byte-level sequences with less computational cost. Full article

(This article belongs to the Special Issue Advances in Pattern Recognition and Image Analysis)

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