Affiliation: Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea

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Research Interests: deep learning; object detection; NLP; pattern recognition; computer vision

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Dr. Tri-Hai Nguyen

Email: hai.nguyentri@vlu.edu.vn

Affiliation: Faculty of Information Technology, School of Technology, Van Lang University, Ho Chi Minh City70000, Vietnam

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Research Interests: Algorithms; Reinforcement Learning; Swarm Intelligence; UAV; Resource Allocation; Machine Learning

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Summary

In recent years, the landscape of image and video processing has undergone a transformative shift, driven by advancements in methodologies and technologies across several domains, including computer vision, 3D modeling, computer graphics, and multimedia. The rapid evolution of these fields has spurred the development of innovative applications and techniques, enhancing our ability to interpret and interact with visual data in unprecedented ways. Particularly, the rise of machine learning, and more specifically deep learning, has revolutionized image processing by enabling the efficient analysis of massive datasets, leading to the discovery of new patterns and the creation of sophisticated analytical procedures.

This Special Issue seeks to highlight the latest breakthroughs and applications in image and video processing, with a dual focus. Firstly, it explores novel uses of cutting-edge devices for data acquisition and visualization, such as CCTV cameras, 3D scanners, virtual reality (VR) glasses, and robotics, demonstrating how these technologies are reshaping the field. Secondly, it delves into new methodologies for processing large-scale datasets, leveraging modern pattern recognition and machine learning techniques, including deep learning and hypergraph learning, to advance the state of the art in image analysis.

Keywords

3D models processing; Augmented and virtual reality applications; Robotic applications; RGBD analysis; Advanced image enhancement; De-noising and low-light enhancement; Advanced image classification and retrieval; Semantic segmentation; Image processing.

Published Papers

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ARTICLE

SwinHCAD: A Robust Multi-Modality Segmentation Model for Brain Tumors Using Transformer and Channel-Wise Attention
Seyong Jin, Muhammad Fayaz, L. Minh Dang, Hyoung-Kyu Song, Hyeonjoon Moon
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

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ARTICLE

Enhanced Capacity Reversible Data Hiding Based on Pixel Value Ordering in Triple Stego Images
Kim Sao Nguyen, Ngoc Dung Bui
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069355
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Reversible data hiding (RDH) enables secret data embedding while preserving complete cover image recovery, making it crucial for applications requiring image integrity. The pixel value ordering (PVO) technique used in multi-stego images provides good image quality but often results in low embedding capability. To address these challenges, this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image. The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order. Four secret bits are embedded into each block’s maximum pixel value, while three additional More >

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ARTICLE

An Enhanced Image Classification Model Based on Graph Classification and Superpixel-Derived CNN Features for Agricultural Datasets
Thi Phuong Thao Nguyen, Tho Thong Nguyen, Huu Quynh Nguyen, Tien Duc Nguyen, Chu Kien Nguyen, Nguyen Giap Cu
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4899-4920, 2025, DOI:10.32604/cmc.2025.067707
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a… More >

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ARTICLE

Transformer-Based Fusion of Infrared and Visible Imagery for Smoke Recognition in Commercial Areas
Chongyang Wang, Qiongyan Li, Shu Liu, Pengle Cheng, Ying Huang
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5157-5176, 2025, DOI:10.32604/cmc.2025.067367
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract With rapid urbanization, fires pose significant challenges in urban governance. Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles. This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model. By capturing both thermal and visual cues, our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes. We first established a dual-view dataset comprising infrared and visible light videos, implemented an innovative image feature fusion strategy, and More >

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ARTICLE

An Improved YOLO-Based Waste Detection Model and Its Integration to Robotic Gripping Systems
Anjie Wang, Haining Jiao, Zhichao Chen, Jie Yang
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5773-5790, 2025, DOI:10.32604/cmc.2025.066852
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract With the rapid development of the Internet of Things (IoT), artificial intelligence, and big data, waste-sorting systems must balance high accuracy, low latency, and resource efficiency. This paper presents an edge-friendly intelligent waste-sorting system that integrates a lightweight visual neural network, a pentagonal-trajectory robotic arm, and IoT connectivity to meet the requirements of real-time response and high accuracy. A lightweight object detection model, YOLO-WasNet (You Only Look Once for Waste Sorting Network), is proposed to optimize performance on edge devices. YOLO-WasNet adopts a lightweight backbone, applies Spatial Pyramid Pooling-Fast (SPPF) and Convolutional Block Attention Module… More >

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ARTICLE

Multimodal Convolutional Mixer for Mild Cognitive Impairment Detection
Ovidijus Grigas, Robertas Damaševičius, Rytis Maskeliūnas
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1805-1838, 2025, DOI:10.32604/cmc.2025.064354
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Brain imaging is important in detecting Mild Cognitive Impairment (MCI) and related dementias. Magnetic Resonance Imaging (MRI) provides structural insights, while Positron Emission Tomography (PET) evaluates metabolic activity, aiding in the identification of dementia-related pathologies. This study integrates multiple data modalities—T1-weighted MRI, Pittsburgh Compound B (PiB) PET scans, cognitive assessments such as Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and Functional Activities Questionnaire (FAQ), blood pressure parameters, and demographic data—to improve MCI detection. The proposed improved Convolutional Mixer architecture, incorporating B-cos modules, multi-head self-attention, and a custom classifier, achieves a classification accuracy of 96.3% More >

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ARTICLE

A Mask-Guided Latent Low-Rank Representation Method for Infrared and Visible Image Fusion
Kezhen Xie, Syed Mohd Zahid Syed Zainal Ariffin, Muhammad Izzad Ramli
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 997-1011, 2025, DOI:10.32604/cmc.2025.063469
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images. However, existing methods often fail to distinguish salient objects from background regions, leading to detail suppression in salient regions due to global fusion strategies. This study presents a mask-guided latent low-rank representation fusion method to address this issue. First, the GrabCut algorithm is employed to extract a saliency mask, distinguishing salient regions from background regions. Then, latent low-rank representation (LatLRR) is applied to extract deep image features, enhancing More >

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ARTICLE

Robust Alzheimer’s Patient Detection and Tracking for Room Entry Monitoring Using YOLOv8 and Cross Product Analysis
Praveen Kumar Sekharamantry, Farid Melgani, Roberto Delfiore, Stefano Lusardi
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4215-4238, 2025, DOI:10.32604/cmc.2025.062686
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >

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ARTICLE

Local Content-Aware Enhancement for Low-Light Images with Non-Uniform Illumination
Qi Mu, Yuanjie Guo, Xiangfu Ge, Xinyue Wang, Zhanli Li
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4669-4690, 2025, DOI:10.32604/cmc.2025.058495
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract In low-light image enhancement, prevailing Retinex-based methods often struggle with precise illumination estimation and brightness modulation. This can result in issues such as halo artifacts, blurred edges, and diminished details in bright regions, particularly under non-uniform illumination conditions. We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image. By introducing multi-scale effective guided filtering, our method surpasses the limitations of traditional isotropic filters, such as Gaussian filters, in handling non-uniform illumination. It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates… More >

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ARTICLE

Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier
Yawar Abbas, Aisha Ahmed Alarfaj, Ebtisam Abdullah Alabdulqader, Asaad Algarni, Ahmad Jalal, Hui Liu
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >

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REVIEW

A Review on Vision-Language-Based Approaches: Challenges and Applications
Huu-Tuong Ho, Luong Vuong Nguyen, Minh-Tien Pham, Quang-Huy Pham, Quang-Duong Tran, Duong Nguyen Minh Huy, Tri-Hai Nguyen
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1733-1756, 2025, DOI:10.32604/cmc.2025.060363
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. More >

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New Trends in Image Processing

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