While precision agriculture or plant phenotyping are very actively moving toward numerical protocols for objective and fast automated measurements, plant variety testing is still very largely guided by manual practice...
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This study builds an online detection device for quality assessment of high-speed moving filter rods based on machinevision. This detection device effectively addresses the problem of high defect rate associated with...
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Shadow removal is an important task in computer vision, as shadows in real-world scenes affect image color and brightness, making perception and texture recognition more challenging. Many existing methods may not adeq...
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In view of the current situation of low efficiency, poor accuracy and inability to realize real-time detection of the clearance between the safety mechanism of the Three Gorges ship lift and the thread pair of the nut...
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This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of...
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In this paper, the 3D space imaging model of machinevision is constructed. Starting from the traditional machinevisionimageprocessing algorithm flow, the image denoising process and target tracking process are opt...
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The proposal aims to develop a prototype system to detect possible bladder tumors. The system Will use imageprocessing techniques, computer vision, segmentation, pattern recognition and machine learning. The prototyp...
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ISBN:
(纸本)9798350359374
The proposal aims to develop a prototype system to detect possible bladder tumors. The system Will use imageprocessing techniques, computer vision, segmentation, pattern recognition and machine learning. The prototype Will load images of the urinary bladder (hollow, distensible muscular organ) and obtain a classification of possible bladder tumors.
Single image Super-Resolution (SISR), which aims to recover a high-resolution (HR) image from a low-resolution (LR) one, is an ill-posed problem. Convolutional Neural Networks (CNNs) have been used in low-level vision...
Single image Super-Resolution (SISR), which aims to recover a high-resolution (HR) image from a low-resolution (LR) one, is an ill-posed problem. Convolutional Neural Networks (CNNs) have been used in low-level vision tasks such as Super-Resolution (SR), and inspired by impressive results in high-level tasks. By choosing the proper structure, the methods can be improved significantly. In this case, selecting an appropriate loss function is essential for any deep learning task, especially in SISR. The exploited loss function impacts the quality of the images produced by the SISR algorithms. Some loss functions can make the output image look blurred or unnatural, which goes against the purpose of SR. To ensure that the output image retains the content of the original photo while also improving the structure and texture, it is essential to choose a loss that is well suited for the task. In this paper, various loss functions for SISR are reviewed. Then, we present an overall analysis of loss functions for SISR based on our exploration.
This paper focuses on the detection and identification of defects on the end faces of small motor bearings. Bearing defects significantly impact the performance and lifespan of motors, and traditional manual inspectio...
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Remote sensing image fusion is the integration of multimodal information to facilitate the comprehensive acquisition of target characteristics for image interpretation and analysis. Recently, the diffusion model and i...
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