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135 articles for “image detection”
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Underwater Image Enhancement and Edge Detection
Abstract: A hybrid image enhancement and edge detection algorithm for underwater images is proposed in this paper. The proposed enhancement algorithm augments the image quality and suppresses noise in underwater images. The novel edge detection process is centered on the pixel phase information present in images, thereby leading to edge pixels that are illumination invariant. Since, illumination influences a major portion of information in underwater images, subjective analysis of the proposed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 1, Issue 1, 2015 · pp. 11–21 Read article
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SMS: An Improved Approach Specifically for Edge Detection of Gray-Scale Images
Abstract: Abstract Edges of an image can be represented by using various edge detectors with different approaches. Edge detection is a very fundamental part in image processing and computer vision. In this paper, an emerging edge detection approach which computes edges of different images using the programmatic combination of Sobel-Morphological-Sobel (SMS) edge detectors is presented. This approach can be used as framework for multi-scale edge detectors for a very wide range …
Published in International Journal of Distributed Computing and Technology · Vol. 1, Issue 2, 2015 · pp. 16–20 Read article
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A Review on Design and Analysis Model for Detectionand Classification of Lung Diseases Using Medical Images
Abstract: A key component of the human respiratory system, the lung regulates blood oxygen levels through gasexchange. Diseases linked to the lungs are becoming more common as pollution levels rise. In order topreserve human life, it is crucial to identify lung disorders early. In order to forecast lung disease, clinicalprofessionals analyze medical imaging, such as CT or X-rays. Clinicians identify lung disease by applyingtheir medical expertise and knowledge. Because expert knowledge …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 2, 2025 Read article
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Diabetic Retinopathy Detection from Retinal Images Using Convolutional Neural Network
Abstract: Diabetic retinopathy (DR) is a popular problem of diabetes and the major reasons of loss of sight in the active inhabitants. Many of the problems of DR can be avoided by blood glucose manage and regular remedy. Since the kinds and the difficulties of DR, it is actually hard for DR detection in the time-consuming regular analysis. This document is to effort towards discovering an automated method to sort out …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 1, 2023 · pp. 8–17 Read article
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CSPC: A New Approach for Edge Detection of Gray-Scale Images
Abstract: We can define an edge of an image as a set of connected pixels that creates boundaries of several objects in an image. Generally, Edge detection is a process of segmenting an image into regions of discontinuity. Edge detection takes a greater role in digital image processing as well as practical aspects of our life.Edges of an image can begenerated by implementingseveral edge detector mechanisms.In this paper, anew edge detectionmechanismthat …
Published in International Journal of Distributed Computing and Technology · Vol. 2, Issue 1, 2016 · pp. 1–6 Read article
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Deep Learning in Medical Image Classification and Object Detection: A Survey
Abstract: Deep learning methods have demonstrated superior performance in the area of computer vision, speech recognition, natural language processing, healthcare, and many more. Convolutional neural networks (CNNs) are a class of deep learning methods that have the ability to learn from raw data available as images, audio, or text. CNNs have become a powerful tool for variety of pattern recognition tasks due to the availability of abundant data and GPU-based training. …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 8, Issue 2, 2022 · pp. 1–15 Read article
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Detection of Leaf Diseases Using Image Processing Techniques
Abstract: Agricultural field plays a vital role in the earning to feed ever growing population. The productivity of the crop depends on pest attack, disease, weather, etc. The disease in plants will affect the production of crops. Detecting diseases in plants through visualization is a traditional method which is not relevant. We are focusing on reliable, fast and accurate detection method that can be done using image processing techniques to monitor …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 5, Issue 2, 2019 · pp. 14–19 Read article
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A Real Image and the Edges Detected Using Ramp and Ridge Profile
Abstract: Edge detection may be an elementary downside of laptop vision and has been wide investigated. I tend to propose a brand-new framework for edge detection supported edge profiles. Acceptable analysis functions area unit required for various kinds of edge profiles, like ramp edges, ridge edges, etc. Associate in Nursing analysis operate should meet the need that it'll manufacture native minima at the positions wherever edges of a given sort occur …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 3, Issue 1, 2017 · pp. 11–16 Read article
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Advancing Early Detection of Menkes Disease Through Image Analysis and Biosensor Integration
Abstract: Menkes disease, a rare X-linked recessive disorder of copper metabolism, poses significant challenges in early diagnosis due to its nonspecific initial symptoms. This paper presents a comprehensive approach to enhance the early detection of Menkes disease by leveraging image processing techniques, pattern recognition algorithms, and innovative biosensor technologies. A novel framework is proposed that combines analysis of hair microscopy images, facial feature recognition, and copper-sensitive biosensors to create a multi-modal …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 2, 2024 · pp. 24–34 Read article
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Image Enhancement and Lung Cancer Detection Using Anisotropic Diffusion Filter
Abstract: The leading cause of cancer-related deaths globally is lung cancer. According to the Global Cancer Statistics lung cancer is the leading cause of cancer-related fatalities with 1.8 million deaths and 2.1 million new cases. Early detection of a tiny tumor can stop cancer from spreading and greatly enhance prognosis and survival rates. Consequently, the creation of a sophisticated computer-aided diagnosis system (CADS) may be advantageous for the early therapy of …
Published in International Journal of Analytical and Applied Chemistry · Vol. 9, Issue 1, 2023 · pp. 21–29 Read article
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Comparison Analysis of Lumpy Skin Disease Detection System Using X-Ray Images of Animals with the Help of Deep Learning
Abstract: Lumpy Skin Disease (LSD), caused by the Capripoxvirus, is a highly contagious viral infection impacting cattle and other livestock, resulting in significant economic losses due to decreased productivity and mortality. Early detection is critical to mitigate its spread and reduce its impact on agriculture and dairy sectors. This study investigates the efficacy of deep learning methodologies for detecting LSD through X-ray images, offering a non-invasive diagnostic approach to identify internal …
Published in International Journal of Animal Biotechnology and Applications · Vol. 11, Issue 2, 2025 · pp. 1–5 Read article
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Advanced Classification of Diabetic Retinopathy Using GAN and Deep Residual CNN Model
Abstract: Diabetic retinopathy (DR) is a leading cause of blindness worldwide, impacting the largest number of individuals who have diabetes. Early identification of diabetic retinopathy may help to prevent severe effects; however, the very limited availability of labeled datasets, and the possibility of overfitting of the model, presents obstacles to obtaining an accurate diagnosis. In this research, a hybrid deep learning strategy is introduced, where Generative Adversarial Networks (GANs) are used …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 8–16 Read article
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Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods
Abstract: The presented work uses Deep learning methods to detect diseases in cabbage leaves. To avoid a reduction in agricultural product yield, disease diagnosis in plants is crucial. Manual plant disease monitoring is challenging and time-consuming at every stage. Five major types of diseases are considered. Initially, the input images are classified as healthy and diseased. Further, the diseased images are classified into five different varieties. Early and precise biotic stress …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 8, Issue 2, 2022 · pp. 23–32 Read article
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Detecting COVID_19 in X-ray images using artificial neural network
Abstract: In this paper, we propose a method for detecting COVID-19, which is most important for now days in lowering mortality rates. COVID-19 had a rapid impact on our daily lives, businesses, and global trade and transportation. Chest CT scans and CT x-rays reveal particular radiographic abnormalities in people with COVID-19 by means of faster and more efficient representation of the patient's lung that enables for the detection of viral phenomena. …
Published in International Journal of Broadband Cellular Communication · Vol. 8, Issue 1, 2022 · pp. 32–38 Read article
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AI-Driven Hybrid Neural Network Architecture for Medical Image Segmentation: A Novel Approach for Early Disease Detection
Abstract: One of the key factors in detecting early diseases is medical image segmentation. Medical image segmentation can be very complex due to the differences in image quality, noise, and specific anatomical structures of individual patients. A novel approach for a hybrid neural network structure that combines the power of convolutional neural networks, and the transformer-based models is described here for improved accuracy and robustness of medical image segmentation. Unlike traditional …
Published in International Journal of Microwave Engineering and Technology · Vol. 11, Issue 1, 2025 · pp. 1–7 Read article
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An approach for Intelligent detection of dirty PV panels using image processing with YOLOv8
Abstract: The efficiency of solar photovoltaic (PV) systems is greatly affected by the condition of panel surfaces, as dust buildup and other impurities can noticeably reduce power output. Traditional maintenance methods rely largely on manual inspection, which can be subjective, inconsistent, and impractical for large-scale installations. To address these challenges, this work investigates a deep learning-based computer vision approach for the automatic assessment of solar panel cleanliness. In this work, a …
Published in International Journal of Renewable Energy and its Commercialization · Vol. 12, Issue 2, 2026 Read article
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Using Image Feature Selection through Deep Salient Region Detection Using Canny Edge Detection Framework
Abstract: Salient object identification has received a lot of attention recently in computer vision because it offers quick solutions to several challenging procedures. It first identifies the scene's most prominent and attention-grabbing object, after which it segments the object's entire extent. The perception and processing of visual input is linked to visual saliency. This proposed work focuses on several convectional saliency detection methods that are useful in a variety of applications …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 9, Issue 1, 2023 · pp. 8–21 Read article
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Deep Learning to Detect Pneumonia from Chest X-Ray Image
Abstract: Pneumonia is an acute pulmonary infection that is caused by viruses or by bacteria and it causes inflammation in the lungs. In 2019, a total of 2.5 million people died because of pneumonia, out of which, one third were younger than the age of 5. This infection was diagnosed by taking a chest Xray. The prediction of diseases is mainly based on human knowledge. In this, the probability of human …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 8, Issue 1, 2022 · pp. 8–14 Read article
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Design and Prototype of an Autonomous Car using Image Processing
Abstract: The goal of the project is to construct a working model of an autonomous vehicle. The car will receive the essential information from the outside world through the use of an HD camera for image processing. The car can drive itself safely and intelligently to the specified location, reducing the possibility of human error. Numerous currently used methods, like object and lane identification, are combined to offer the car the …
Published in International Journal of Computer Aided Manufacturing · Vol. 9, Issue 1, 2023 · pp. 6–12 Read article
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Real-Time Traffic Management System Utilizing Machine Learning Techniques
Abstract: AbstractThe rapid increase in the number of vehicles on roads has created significant challenges forconventional traffic management systems, which struggle to efficiently regulate traffic flow undervarying conditions. Traditional traffic signals operate on fixed timing cycles, failing to adapt tofluctuating vehicle densities across different lanes. This inefficiency results in unnecessary delays,increased congestion, longer travel times, and excessive fuel consumption. Additionally, fixed-timingsignals do not account for dynamic traffic patterns, leading to bottlenecks …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 11, Issue 1, 2025 · pp. 24–31 Read article