Sonar image segmentation technique is crucial for underwater target tracking, among other things. Due to the undersea environment's influence, noise is easily absorbed, which leads to a poor tracking performance. ...
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machinevision-based applications have witnessed widespread adoption in diverse fields. Efficiently processing and compressing the vast amounts of video data collected by machines is crucial for these applications. To...
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machinevision is a technology and method used to provide automated image-driven analysis in applications such as inspection, process control, and guidance, and is very popular in industries nowadays. Computer/machine...
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machinevision is a technology and method used to provide automated image-driven analysis in applications such as inspection, process control, and guidance, and is very popular in industries nowadays. Computer/machinevision has been extensively developed and used in production to achieve precise automatic control. This paper presented an imageprocessing approach, a subset of machinevision, for the visual inspection system of the Clutch Friction Disc (CFD) produced for 2 wheelers. imageprocessing is used to inspect different parts of the CFD. After previous operations of production, a part enters the inspection system, where the geometry and size of the part are inspected, and then imageprocessing technology is used to decide to accept or reject the product. This paper presented the work constructed using a python program with OpenCV which aims to identify the major defects in clutch friction plates, by using different imageprocessing techniques. With the proposed approach decision can be made automatically that whether the processed part will be accepted or rejected and then will be identified as "Ok tested" and "Faulty" pieces. Copyright (C) 2022 Elsevier Ltd. All rights reserved.
The cultivation of mangoes contributes significantly to the economy and food security of many tropical and subtropical regions. However, mango trees are susceptible to various leaf diseases that can significantly affe...
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image classification is a core task in the field of computer vision, with significant implications for applications such as medical imaging and autonomous driving. Classical neural network architectures face numerous ...
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ISBN:
(纸本)9798331507800;9798331507794
image classification is a core task in the field of computer vision, with significant implications for applications such as medical imaging and autonomous driving. Classical neural network architectures face numerous challenges when dealing with high-dimensional and complex data, particularly in terms of feature extraction and data processing capabilities, limiting their performance in certain applications. Quantum computing offers promising new approaches to processing high dimensional complex data, most notably through Parameterized Quantum Circuits (PQC) as quantum hybrid neural networks for image classification. In this paper, we propose a hybrid neural network featuring a specific PQC structure designed to generate entanglement in accordance with sample images. This PQC structure utilizes the adjacency matrix of the corresponding graph of the sample image, embedding adjacency relationships among nodes into the entanglement module, thereby generating entanglement aligned with the features of specific images. Numerical results validate the effectiveness of our PQC model across various image classification tasks, enhancing the efficiency of feature learning and representation. Our model demonstrates significant advantages in classification accuracy and learning efficiency on classical datasets. Our research not only advances the theoretical understanding of hybrid quantum networks but also paves the way for practical implementations in real-world scenarios.
Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the be...
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ISBN:
(纸本)9798350350470;9798350350487
Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the best option to replace manual crack detection because of the advancement of methods for deep learning. machine learning algorithms known as artificial neural networks (ANNs) imitate how the human brain functions. These Neural Networks can be implemented in software. However, these neural networks require large computations. Hardware implementation of these neural networks has higher processing speeds than their software implementations. CNN is a particular kind of artificial neural network that is used to interpret pixel data and is utilised in image detection and processing. Computer visionapplications including object identification, image segmentation, and image classification work well with convolutional neural networks. employed for categorization The proposed method uses a configurable convolution neural network system for crack detection. An accuracy of 97.5% is achieved over 200 images. By detecting the crack effectively using the method, the quality of the concrete structures will be ensured using dedicated hardware shortly.
machinevision is a field of computer vision that focuses on developing and implementing automated visual inspection systems. It involves using cameras, sensors, and imageprocessing algorithms to capture and analyze ...
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The proceedings contain 39 papers. The topics discussed include: performance analysis of several CNN based models for brain MRI in tumor classification;MRI-based lumbar sagittal alignment classification system;3D mapp...
ISBN:
(纸本)9798350352368
The proceedings contain 39 papers. The topics discussed include: performance analysis of several CNN based models for brain MRI in tumor classification;MRI-based lumbar sagittal alignment classification system;3D mapping and landing zone identification in complex terrains using DSM and photogrammetry;vision language models for oil palm fresh fruit bunch ripeness classification;towards no shadow: region-based shadow compensation on low-altitude urban aerial images;comparative analysis of deep learning architectures for blood cancer classification;exploration of group and shuffle module for semantic segmentation of sea ice concentration;on handcrafted machine learning features for art authentication;and acoustic signature modelling of marine vessels in various environmental and operational conditions.
machinevisionapplications for intelligent vision systems in manufacturing industries were reported based on imageprocessing and artificial intelligence technology. We propose the imaging and vision development plat...
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In this paper, deformation correction, feature extraction, image filtering, particle manipulation and other steps are used to achieve the relative positioning between the objects. The visually-assisted image processin...
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