Super-resolution has advanced significantly in the last 20 years, particularly with the application of deep learning methods. One of the most important imageprocessing methods for boosting an image's resolut...
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The proceedings contain 52 papers. The topics discussed include: improvement of remote sensing image target detection algorithm based on YOLO v5;A Study of Chan-vese model with the introduction of edge information;rea...
The proceedings contain 52 papers. The topics discussed include: improvement of remote sensing image target detection algorithm based on YOLO v5;A Study of Chan-vese model with the introduction of edge information;real-time monitoring algorithm of muscle state based on sEMG signal;lane detection network with direction context;anomaly pixel detection via dual-branch uncertainty metrics;high precision license plate recognition algorithm in open scene;implementation and design of metro process quality inspection system based on imageprocessing technology;the research on remote sensing image change detection based on deep learning;research on aircraft wheel hub pose detection method based on machinevision;lunar dome detection method based on few-shot object detection;and image enhancement algorithm of foggy sky with sky based on sky segmentation.
Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology ...
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Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging -based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery. Level of evidence: Level v;Review Article (c) 2024 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved.
Face detection applications using digital photos are critical in the face recognition process. This application is used in biometric recognition systems, search systems, and security systems. Artificial intelligence a...
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vision-language models like CLIP, utilizing class proxies derived from class name text features, have shown a notable capability in zero-shot medical image diagnosis which is vital in scenarios with limited disease da...
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In recent years, there has been a sharp increase in transmission of images to remote servers specifically for the purpose of computer vision. In many applications, such as surveillance, images are mostly transmitted f...
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ISBN:
(纸本)9781665492577
In recent years, there has been a sharp increase in transmission of images to remote servers specifically for the purpose of computer vision. In many applications, such as surveillance, images are mostly transmitted for automated analysis, and rarely seen by humans. Using traditional compression for this scenario has been shown to be inefficient in terms of bit-rate, likely due to the focus on human based distortion metrics. Thus, it is important to create specific image coding methods for joint use by humans and machines. One way to create the machine side of such a codec is to perform feature matching of some intermediate layer in a Deep Neural Network performing the machine task. In this work, we explore the effects of the layer choice used in training a learnable codec for humans and machines. We prove, using the data processing inequality, that matching features from deeper layers is preferable in the sense of rate-distortion. Next, we confirm our findings empirically by re-training an existing model for scalable human-machine coding. In our experiments we show the trade-off between the human and machine sides of such a scalable model, and discuss the benefit of using deeper layers for training in that regard.
The task of image style transfer is to automatically redraw (using neural networks) an image with some content (for example, a family photo) in the style set by another image (for example, a van Gogh painting), which ...
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Edge detection can benefit many different industries and domains, including computer vision, machine learning, image analysis, remote sensing, thermal imaging, pattern recognition, and medical imaging. The technique o...
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The advancement of assistive technologies for visually impaired individuals has seen significant strides, leveraging the power of computer vision-based object recognition and detection. This literature review explores...
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imageprocessing has become extremely important, with the consequences of real-time imageprocessing failures being severe;thus, research and study in real-time imageprocessing methods are extremely important. Some i...
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