Dennies Tsietso^1,*, Abid Yahya¹, Ravi Samikannu¹, Basit Qureshi², Muhammad Babar^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4749-4765, 2024, DOI:10.32604/cmc.2024.052793 - 12 September 2024

Abstract Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide. Various Computer-Aided Diagnosis (CAD) tools, based on breast thermograms, have been developed for early detection of this disease. However, accurately segmenting the Region of Interest (ROI) from thermograms remains challenging. This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottom boundary using a second-degree polynomial. The proposed method demonstrated high efficacy, achieving an impressive Jaccard coefficient of 86% and a Dice… More >

Xinkun Tang^1,2, Feng Ouyang^1,2, Ying Xu^2,*, Ligu Zhu¹, Bo Peng¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3955-3975, 2024, DOI:10.32604/cmc.2024.049057 - 20 June 2024

Abstract High-resolution video transmission requires a substantial amount of bandwidth. In this paper, we present a novel video processing methodology that innovatively integrates region of interest (ROI) identification and super-resolution enhancement. Our method commences with the accurate detection of ROIs within video sequences, followed by the application of advanced super-resolution techniques to these areas, thereby preserving visual quality while economizing on data transmission. To validate and benchmark our approach, we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications. The proposed model architecture leverages the transformer network framework,… More >

Nizheen A. Ali¹, Ramadhan J. Mstafa^2,3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1451-1469, 2023, DOI:10.32604/csse.2023.039957 - 28 July 2023

Abstract With the widespread use of the internet, there is an increasing need to ensure the security and privacy of transmitted data. This has led to an intensified focus on the study of video steganography, which is a technique that hides data within a video cover to avoid detection. The effectiveness of any steganography method depends on its ability to embed data without altering the original video’s quality while maintaining high efficiency. This paper proposes a new method to video steganography, which involves utilizing a Genetic Algorithm (GA) for identifying the Region of Interest (ROI) in… 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 - 29 April 2023

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… More >

Gamil Ahmed¹, Tarek Sheltami^1,*, Ashraf Mahmoud¹, Ansar Yasar²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3239-3263, 2023, DOI:10.32604/cmes.2023.022860 - 09 March 2023

Abstract Unmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage path planning (CPP) which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest (ROI). However, the flight time of the UAV is limited due to a battery limitation and may not cover the whole region, especially in large region. Therefore, energy consumption is one of the most… More >

K. Nithya^1,*, V. Rajamani²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2655-2666, 2023, DOI:10.32604/csse.2023.027275 - 01 August 2022

Abstract Recent days, Image retrieval has become a tedious process as the image database has grown very larger. The introduction of Machine Learning (ML) and Deep Learning (DL) made this process more comfortable. In these, the pair-wise label similarity is used to find the matching images from the database. But this method lacks of limited propose code and weak execution of misclassified images. In order to get-rid of the above problem, a novel triplet based label that incorporates context-spatial similarity measure is proposed. A Point Attention Based Triplet Network (PABTN) is introduced to study propose code More >

Jingcheng Chen¹, Zhili Zhou^1,2,*, Zhaoqing Pan¹, Ching-nung Yang³

Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582

Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of More >

Zhe Liu¹, Charlie Maere^1,*, Yuqing Song¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 669-686, 2019, DOI:10.32604/cmc.2019.04590

Abstract Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data . Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’ area and automatically detect a seed point. 3) We use a novel approach to More >