Mingming Huang¹, Yunfan Ye^1,*, Zhiping Cai²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.072399 - 09 December 2025

Abstract As a fundamental component in computer vision, edges can be categorized into four types based on discontinuities in reflectance, illumination, surface normal, or depth. While deep CNNs have significantly advanced generic edge detection, real-time multi-class semantic edge detection under resource constraints remains challenging. To address this, we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection. Our model simultaneously predicts background and four edge categories from full-resolution inputs, balancing accuracy and efficiency. Key contributions include: a multi-channel output structure expanding binary edge prediction to five classes, supported by a deep supervision More >

Mohammed Albekairi¹, Radhia Khdhir^2,*, Amina Magdich³, Somia Asklany^4,*, Ghulam Abbas⁵, Amr Yousef ^6,7

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3607-3644, 2025, DOI:10.32604/cmes.2025.069681 - 30 September 2025

Abstract License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation, which lead to pixel displacements. This article introduces a Displacement Region Recognition Method (DR2M) to address such a problem. This method operates on displaced features compared to the training input observed throughout definite time frames. The technique focuses on detecting features that remain relatively stable under haze, using a frame-based analysis to isolate edges minimally affected by visual noise. The edge detection failures are identified using a bilateral neural network through displaced feature training. The training converges bilaterally… More >

Thaer Thaher^1,*, Muhammed Saffarini², Majdi Mafarja³, Abdulaziz Alashbi⁴, Abdul Hakim Mohamed⁵, Ayman A. El-Saleh⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2359-2394, 2025, DOI:10.32604/cmes.2025.065388 - 31 August 2025

Abstract Face detection is a critical component in modern security, surveillance, and human-computer interaction systems, with widespread applications in smartphones, biometric access control, and public monitoring. However, detecting faces with high levels of occlusion, such as those covered by masks, veils, or scarves, remains a significant challenge, as traditional models often fail to generalize under such conditions. This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients (HOG) and Canny edge detection with modern deep learning models. The goal is to improve face detection accuracy under occlusions. The… More >

Hanan Hardan^1,*, Osama A. Khashan^2,*, Mohammad Alshinwan¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1681-1710, 2025, DOI:10.32604/cmc.2025.061659 - 09 June 2025

Abstract This study introduces an Edge-Based Data Hiding and Extraction Algorithm (EBDHEA) to address the problem of data embedding in images while preserving robust security and high image quality. The algorithm produces three classes of pixels from the pixels in the cover image: edges found by the Canny edge detection method, pixels arising from the expansion of neighboring edge pixels, and pixels that are neither edges nor components of the neighboring edge pixels. The number of Least Significant Bits (LSBs) that are used to hide data depends on these classifications. Furthermore, the lossless compression method, Huffman… More >

Xin Wen^*, Xiao Zheng, Yu He

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025

Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >

Haemin Jung¹, Heesung Park², Hae Sun Jung³, Kwangyon Lee^4,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2261-2279, 2024, DOI:10.32604/cmc.2024.057118 - 18 November 2024

Abstract The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades, as buildings contribute significantly to energy consumption in urban environments. However, conventional image segmentation methods often struggle to capture fine details such as edges and contours, limiting their effectiveness in identifying areas prone to energy loss. To address this challenge, we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model, Cascade U-Net. Object-wise processing isolates components of the facade, such as walls and windows, for independent analysis, while Cascade U-Net incorporates contour information to… More >

Zongbao Jiang, Minqing Zhang^*, Weina Dong, Chao Jiang, Fuqiang Di

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1123-1155, 2024, DOI:10.32604/cmc.2024.055120 - 15 October 2024

Abstract With the rapid advancement of cloud computing technology, reversible data hiding algorithms in encrypted images (RDH-EI) have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments. However, existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios. To address these challenges, this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection (AMED) and ciphertext-policy attribute-based encryption (CP-ABE). This proposed algorithm enhances the conventional median edge detection (MED) by incorporating dynamic variables to improve pixel prediction… More >

Yousef Alhwaiti^1,*, Muhammad Hameed Siddiqi¹, Irshad Ahmad²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110 - 09 November 2023

Abstract Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments.… More >

Yongning Yuan¹, Dong Zhang², Usama Sayed³, Hao Zhu¹, Jun Wang⁴, Xiaojun Yang², Zheng Wang^2,*

Journal of Renewable Materials, Vol.11, No.11, pp. 3917-3932, 2023, DOI:10.32604/jrm.2023.028764 - 31 October 2023

Abstract In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood, this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method. The detection and visualization analysis of internal log defects were realized through log specimen test. The main conclusions show that the accuracy, reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified. The system can make the edge of the detected image smooth by interpolation algorithm, More >

Kali Gurkahraman^1,*, Rukiye Karakis², Hidayet Takci¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 55-77, 2023, DOI:10.32604/csse.2023.037798 - 26 May 2023

Abstract This study aimed to deal with three challenges: robustness, imperceptibility, and capacity in the image watermarking field. To reach a high capacity, a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional techniques. The colored watermark image was created by inserting a randomly generated message on the edge points detected by this algorithm. To ensure robustness and imperceptibility, watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value Decomposition. In the watermarking stage, the watermark image was weighted by the adaptive scaling factor More >