As computer vision technologies become increasingly integrated into various industries, the demand for more accurate, efficient, and innovative techniques is growing. This special issue focuses on novel methodologies that push the boundaries of image classification, object detection, and segmentation. It covers a wide range of topics, including but not limited to, deep learning architectures, data augmentation strategies, unsupervised and semi-supervised learning techniques, transfer learning, and explainable AI in visual recognition tasks.

Researchers and practitioners are invited to submit original contributions that present new models, algorithms, or systems that enhance the performance, scalability, and generalizability of image-based tasks. The issue also welcomes studies that address challenges such as handling noisy or imbalanced data, real-time processing, and applications across diverse domains like medical imaging, autonomous vehicles, and remote sensing. Through this special issue, we aim to provide a platform for sharing groundbreaking work that will shape the future of image analysis and drive forward the capabilities of computer vision systems.

Potential topics include, but are not limited to:

· Applications in Medical Imaging, Autonomous Vehicles, and Surveillance

· Novel Evaluation Metrics and Benchmarking

· Optimization Techniques for Improved Accuracy and Efficiency

· Emerging applications in robotics, agriculture, and environmental monitoring

Keywords

Deep Learning; Convolutional Neural Networks (CNNs); Unsupervised Learning; Semi-Supervised Learning; Explainable AI; Real-Time Processing; Remote Sensing; Visual Recognition

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ARTICLE

CPEWS: Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation
Xiaoyan Shao, Jiaqi Han, Lingling Li, Xuezhuan Zhao, Jingjing Yan
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 595-617, 2025, DOI:10.32604/cmc.2025.060295
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods. End-to-end model designs have gained significant attention for improving training efficiency. Most current algorithms rely on Convolutional Neural Networks (CNNs) for feature extraction. Although CNNs are proficient at capturing local features, they often struggle with global context, leading to incomplete and false Class Activation Mapping (CAM). To address these limitations, this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation (CPEWS) model, which improves feature extraction by utilizing the Vision Transformer… More >

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ARTICLE

An Efficient Instance Segmentation Based on Layer Aggregation and Lightweight Convolution
Hui Jin, Shuaiqi Xu, Chengyi Duan, Ruixue He, Ji Zhang
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1041-1055, 2025, DOI:10.32604/cmc.2025.060304
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Instance segmentation is crucial in various domains, such as autonomous driving and robotics. However, there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices. Therefore, it is essential to enhance detection speed while maintaining high accuracy. In this study, we propose you only look once-layer fusion (YOLO-LF), a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications. Based on the You Only Look Once version 8 nano (YOLOv8n) framework, we introduce a lightweight convolutional module and design a lightweight layer aggregation module… More >

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ARTICLE

Bilateral Dual-Residual Real-Time Semantic Segmentation Network
Shijie Xiang, Dong Zhou, Dan Tian, Zihao Wang
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 497-515, 2025, DOI:10.32604/cmc.2025.060244
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Real-time semantic segmentation tasks place stringent demands on network inference speed, often requiring a reduction in network depth to decrease computational load. However, shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy. Therefore, balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation. To address these challenges, this paper proposes a lightweight bilateral dual-residual network. By introducing a novel residual structure combined with feature extraction and fusion modules, the proposed network significantly enhances representational capacity while reducing computational costs. Specifically, an improved compound… More >

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ARTICLE

Target Detection-Oriented RGCN Inference Enhancement Method
Lijuan Zhang, Xiaoyu Wang, Songtao Zhang, Yutong Jiang, Dongming Li, Weichen Sun
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1219-1237, 2025, DOI:10.32604/cmc.2025.059856
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract In this paper, a reasoning enhancement method based on RGCN (Relational Graph Convolutional Network) is proposed to improve the detection capability of UAV (Unmanned Aerial Vehicle) on fast-moving military targets in urban battlefield environments. By combining military images with the publicly available VisDrone2019 dataset, a new dataset called VisMilitary was built and multiple YOLO (You Only Look Once) models were tested on it. Due to the low confidence problem caused by fuzzy targets, the performance of traditional YOLO models on real battlefield images decreases significantly. Therefore, we propose an improved RGCN inference model, which improves More >

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REVIEW

A Comprehensive Review of Pill Image Recognition
Linh Nguyen Thi My, Viet-Tuan Le, Tham Vo, Vinh Truong Hoang
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3693-3740, 2025, DOI:10.32604/cmc.2025.060793
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
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 >

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ARTICLE

Semi-Supervised Medical Image Classification Based on Sample Intrinsic Similarity Using Canonical Correlation Analysis
Kun Liu, Chen Bao, Sidong Liu
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4451-4468, 2025, DOI:10.32604/cmc.2024.059053
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC),… More >

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ARTICLE

A Weakly Supervised Semantic Segmentation Method Based on Improved Conformer
Xueli Shen, Meng Wang
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4631-4647, 2025, DOI:10.32604/cmc.2025.059149
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract In the field of Weakly Supervised Semantic Segmentation (WSSS), methods based on image-level annotation face challenges in accurately capturing objects of varying sizes, lacking sensitivity to image details, and having high computational costs. To address these issues, we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs, proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer. In the Convolution Neural Network (CNN) branch, a cross-scale feature integration convolution module is designed, incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range… More >

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ARTICLE

ProNet: Underwater Forward-Looking Sonar Images Target Detection Network Based on Progressive Sensitivity Capture
Kaiqiao Wang, Peng Liu, Chun Zhang
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4931-4948, 2025, DOI:10.32604/cmc.2025.060547
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Underwater target detection in forward-looking sonar (FLS) images is a challenging but promising endeavor. The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments. Considering the problems of low imaging resolution, complex background environment, and large changes in target imaging of underwater sonar images, this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture, named ProNet. It progressively captures the sensitive regions in the current image where potential effective targets may exist. Guided by this basic idea, the… More >

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ARTICLE

CAMSNet: Few-Shot Semantic Segmentation via Class Activation Map and Self-Cross Attention Block
Jingjing Yan, Xuyang Zhuang, Xuezhuan Zhao, Xiaoyan Shao, Jiaqi Han
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5363-5386, 2025, DOI:10.32604/cmc.2025.059709
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract The key to the success of few-shot semantic segmentation (FSS) depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set. Due to the few samples in the support set, FSS faces challenges such as intra-class differences, background (BG) mismatches between query and support sets, and ambiguous segmentation between the foreground (FG) and BG in the query set. To address these issues, The paper propose a multi-module network called CAMSNet, which includes four modules: the General Information Module (GIM), the Class Activation Map Aggregation (CAMA) module, the… More >

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ARTICLE

Research on Multimodal Brain Tumor Segmentation Algorithm Based on Feature Decoupling and Information Bottleneck Theory
Xuemei Yang, Yuting Zhou, Shiqi Liu, Junping Yin
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3281-3307, 2025, DOI:10.32604/cmc.2024.057991
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Aiming at the problems of information loss and the relationship between features and target tasks in multimodal medical image segmentation, a multimodal medical image segmentation algorithm based on feature decoupling and information bottleneck theory is proposed in this paper. Based on the reversible network, the bottom-up learning method for different modal information is constructed, which enhances the features’ expression ability and the network’s learning ability. The feature fusion module is designed to balance multi-directional information flow. To retain the information relevant to the target task to the maximum extent and suppress the information irrelevant to… More >

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Novel Methods for Image Classification, Object Detection, and Segmentation

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