INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, machine learning (ML) is becoming a hot topic due to the dire...
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INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification. OBJECTIVES: In Computer vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation. CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and imageprocessing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process. METHODS, RESULTS, AND CONCLUSION: In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of imageprocessing and the results based on the impact;neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV.
Automatic detection of the healthy and unhealthy maize plant leaf is a prevalent machinevision learning task and has significant applications in the Food Industry. In this paper, effective machine learning technique ...
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This research work aims to develop an AI-based plant growth monitoring system using computer vision. By leveraging computer vision algorithms and artificial intelligence techniques, the system will enable real-time an...
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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in imageprocessing, computer graphics, and computer vision. In recent years, there has been a si...
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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in imageprocessing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.
Failures of tailings dams have been happening lately. Due to the lack of laws on particular design criteria and stability requirements related monitoring during construction and maintenance, they are thought to be mor...
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Convolutional neural networks (CNNs) are a widely researched neural network architecture that has demonstrated exemplary performance in imageprocessing tasks and applications compared to other popular deep learning a...
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Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconst...
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Classification and retrieval of medical images (MedIR) are emerging applications of computer vision for enabling intelligent medical diagnostics. Medical images are multi-dimensional and require specialised processing...
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Classification and retrieval of medical images (MedIR) are emerging applications of computer vision for enabling intelligent medical diagnostics. Medical images are multi-dimensional and require specialised processing for the extraction of features from their manifold underlying content. Existing models often fail to consider the inherent characteristics of data and have thus often fallen short when applied to medical images. In this paper, we present a MedIR approach based on the bag of visual words (BoVW) model for content-based medical image retrieval. When it comes to any medical approach models, an imbalance in the dataset is one of the issues. Hence the perspective is also considering a balanced set of categories from an imbalanced dataset. The proposed work on BoVW model extracts features from each image are used to train supervised machine learning classifier for X-ray medical image classification and retrieval. During the experimental validation, the proposed model performed well with the classification accuracy of 89.73% and a good retrieval result using our filter-based approach.
image segmentation models are often evaluated using measures of overlap and boundary deviation between a ground truth and a prediction. These measures do not indicate whether a prediction is an overestimation or under...
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In the current era, machinevision systems are being implemented widely in varied fields due to its key features, such as rapid processing, non-contact-based technology and in-situ measurements. This technology also p...
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In the current era, machinevision systems are being implemented widely in varied fields due to its key features, such as rapid processing, non-contact-based technology and in-situ measurements. This technology also possesses wide applications in the manufacturing sector. The surface texture properties of any machined component vary based on the manufacturing process, machining parameters, tool and machine conditions etc. As the surface texture of the machined components greatly influences the functional performance, it is vital to examine the surface characteristics. The surface texture of the machine component can be assessed by implementing a series of imageprocessing techniques on its speckle images. Speckle image refers to the randomly distributed granular pattern which is obtained when a rough or textured surface is illuminated using a laser beam. This paper focuses on estimating the orientation of the workpiece and examining the surface characteristics based on the post-processing of the speckle images. The hardened steel workpieces used in this investigation were ground by varying the process parameters and speckle images were obtained at 0°, 30°, 60° and 90° orientations. The shifted power spectral density of the ground sample images contains high-energy coefficients which mimic a line and its orientation varies based on the sample orientation. The Hough transform technique was applied to the binary image of shifted PSD to efficiently determine the orientation. Furthermore, correlations have been established between several surface texture characteristics and GLCM parameters with the surface roughness of ground samples.
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