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

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 the braille convex dots. The… More >

Usman Khan¹, Muhammad Khalid Khan¹, Muhammad Ayub Latif¹, Muhammad Naveed^1,2,*, Muhammad Mansoor Alam^2,3,4, Salman A. Khan¹, Mazliham Mohd Su’ud^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101

Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their… More >

Yongcan Zhu^1,2, Jiawen Wang¹, Ye Zhang^1,2, Long Zhao¹, Botao Jiang¹, Xinbo Huang^1,*

Energy Engineering, Vol.121, No.4, pp. 895-911, 2024, DOI:10.32604/ee.2023.041002

Abstract The accumulation of snow and ice on PV modules can have a detrimental impact on power generation, leading to reduced efficiency for prolonged periods. Thus, it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules. To address this issue, the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images. Furthermore, the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules, allowing for the establishment of a residual ice and… More >

Mustafa AlRifaee¹, Sally Almanasra^2,*, Adnan Hnaif³, Ahmad Althunibat³, Mohammad Abdallah³, Thamer Alrawashdeh³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1591-1609, 2024, DOI:10.32604/cmc.2023.043520

Abstract In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect the sclera region. The image… More >

Xiaozhong Yu^1,2,*, Jinhua Zheng^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 197-225, 2024, DOI:10.32604/cmc.2023.043943

Abstract In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to the network structure differences; and… More >

Huanhua Liu, Wei Wang^*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

Zhiqiang Wu^*, Bing Xie

Computer Systems Science and Engineering, Vol.47, No.1, pp. 299-316, 2023, DOI:10.32604/csse.2023.038586

Abstract Image has become an essential medium for expressing meaning and disseminating information. Many images are uploaded to the Internet, among which some are pornographic, causing adverse effects on public psychological health. To create a clean and positive Internet environment, network enforcement agencies need an automatic and efficient pornographic image recognition tool. Previous studies on pornographic images mainly rely on convolutional neural networks (CNN). Because of CNN’s many parameters, they must rely on a large labeled training dataset, which takes work to build. To reduce the effect of the database on the recognition performance of pornographic images, many researchers view pornographic… More >

Muhammad Aamir¹, Ziaur Rahman^1,*, Waheed Ahmed Abro², Uzair Aslam Bhatti³, Zaheer Ahmed Dayo¹, Muhammad Ishfaq¹

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6351-6373, 2023, DOI:10.32604/cmc.2023.038173

Abstract Object detection in images has been identified as a critical area of research in computer vision image processing. Research has developed several novel methods for determining an object’s location and category from an image. However, there is still room for improvement in terms of detection efficiency. This study aims to develop a technique for detecting objects in images. To enhance overall detection performance, we considered object detection a two-fold problem, including localization and classification. The proposed method generates class-independent, high-quality, and precise proposals using an agglomerative clustering technique. We then combine these proposals with the relevant input image to train… More >

Xiuli Si¹, Biao Hong¹, Yuanhui Hu¹, Lidong Chu^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829

Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA) module. The module follows the… More >

Wen-Tsai Sung¹, Chin-Hsuan Lin¹, Sung-Jung Hsiao^2,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 505-520, 2023, DOI:10.32604/csse.2023.034781

Abstract As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency… More >