Linh Nguyen Thi My^1,2,*, Viet-Tuan Le³, Tham Vo¹, Vinh Truong Hoang^3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3693-3740, 2025, DOI:10.32604/cmc.2025.060793 - 06 March 2025

Abstract Pill image recognition is an important field in computer vision. It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety. This survey examines the current state of pill image recognition, focusing on advancements, methodologies, and the challenges that remain unresolved. It provides a comprehensive overview of traditional image processing-based, machine learning-based, deep learning-based, and hybrid-based methods, and aims to explore the ongoing difficulties in the field. We summarize and classify the methods used in each article, compare the strengths and More >

Donghyeok Park¹, Sumin Yeon², Hyeon Seo², Seok-Jun Buu², Suwon Lee^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2725-2737, 2025, DOI:10.32604/cmes.2025.061732 - 03 March 2025

Abstract Recent research on adversarial attacks has primarily focused on white-box attack techniques, with limited exploration of black-box attack methods. Furthermore, in many black-box research scenarios, it is assumed that the output label and probability distribution can be observed without imposing any constraints on the number of attack attempts. Unfortunately, this disregard for the real-world practicality of attacks, particularly their potential for human detectability, has left a gap in the research landscape. Considering these limitations, our study focuses on using a similar color attack method, assuming access only to the output label, limiting the number of More >

Zaid Derea^1,2, Beiji Zou¹, Xiaoyan Kui^1,*, Alaa Thobhani¹, Amr Abdussalam³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2841-2867, 2025, DOI:10.32604/cmes.2025.059586 - 03 March 2025

Abstract Enhancing website security is crucial to combat malicious activities, and CAPTCHA (Completely Automated Public Turing tests to tell Computers and Humans Apart) has become a key method to distinguish humans from bots. While text-based CAPTCHAs are designed to challenge machines while remaining human-readable, recent advances in deep learning have enabled models to recognize them with remarkable efficiency. In this regard, we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention (GVA), which sharpens focus on relevant visual features. We have specifically adapted the… More >

Jianfeng Lu^1,2, Xiangye Huang¹, Caijin Li¹, Renlin Xin¹, Shanqing Zhang^1,2, Mahmoud Emam^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 405-423, 2025, DOI:10.32604/cmes.2024.058121 - 17 December 2024

Abstract Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting. Stamped seal inspection is commonly audited manually to ensure document authenticity. However, manual assessment of seal images is tedious and labor-intensive due to human errors, inconsistent placement, and completeness of the seal. Traditional image recognition systems are inadequate enough to identify seal types accurately, necessitating a neural network-based method for seal image recognition. However, neural network-based classification algorithms, such as Residual Networks (ResNet) and Visual Geometry Group with 16 layers… More >

Shunshun Ji, Jiajun Sun, Chao Zhang^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4017-4029, 2024, DOI:10.32604/cmc.2024.055038 - 12 September 2024

Abstract Asparagus stem blight, also known as “asparagus cancer”, is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus… More >

Xiaoyu Liu, Yong Hu^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4413-4432, 2024, DOI:10.32604/cmc.2024.053938 - 12 September 2024

Abstract Multi-label image classification is recognized as an important task within the field of computer vision, a discipline that has experienced a significant escalation in research endeavors in recent years. The widespread adoption of convolutional neural networks (CNNs) has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification. However, in multi-label image classification tasks, it is crucial to consider the correlation between labels. In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features, many existing studies use graph convolutional networks (GCN) for… More >

Gonghui Deng, Dunzhi Wu, Weizhen Chen^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1985-2003, 2024, DOI:10.32604/cmc.2024.052174 - 15 August 2024

Abstract The task of food image recognition, a nuanced subset of fine-grained image recognition, grapples with substantial intra-class variation and minimal inter-class differences. These challenges are compounded by the irregular and multi-scale nature of food images. Addressing these complexities, our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion, grounded in the ConvNeXt architecture. Our model employs hybrid attention (HA) mechanisms to pinpoint critical discriminative regions within images, substantially mitigating the influence of background noise. Furthermore, it introduces a multi-stage local fusion (MSLF) module, fostering long-distance dependencies between feature maps at… More >

Baoxia Li¹, Wenzhuo Chen¹, Xiaojiang Tang¹, Shaohuang Bian¹, Yang Liu², Junwei Guo², Dan Zhang², Feng Huang^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2221-2236, 2024, DOI:10.32604/cmc.2024.049862 - 15 August 2024

Abstract Complex plasma widely exists in thin film deposition, material surface modification, and waste gas treatment in industrial plasma processes. During complex plasma discharge, the configuration, distribution, and size of particles, as well as the discharge glow, strongly depend on discharge parameters. However, traditional manual diagnosis methods for recognizing discharge parameters from discharge images are complicated to operate with low accuracy, time-consuming and high requirement of instruments. To solve these problems, by combining the two mechanisms of attention mechanism (strengthening the extraction of the channel feature) and shortcut connection (enabling the input information to be directly… More >

Shu-Yin Chiang^*, Ting-Yu Lin

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1757-1773, 2024, DOI:10.32604/cmc.2024.049477 - 15 May 2024

Abstract This research focuses on addressing the challenges associated with image detection in low-light environments, particularly by applying artificial intelligence techniques to machine vision and object recognition systems. The primary goal is to tackle issues related to recognizing objects with low brightness levels. In this study, the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images. The detection scenarios are categorized into normal brightness and low brightness situations. When the system determines a normal brightness environment, normal brightness images are recognized using deep learning methods. In low-brightness situations,… More >

Zezheng Meng, Zefeng Cai, Jie Feng^*, Hanjie Ma, Haixiang Zhang, Shaohua Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1481-1496, 2024, DOI:10.32604/cmc.2024.048002 - 25 April 2024

Abstract Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of More >