Signal processing has become central to many fields, from coherent optical telecommunications, where it is used to compensate signal impairments, to video imageprocessing. imageprocessing is particularly important f...
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Signal processing has become central to many fields, from coherent optical telecommunications, where it is used to compensate signal impairments, to video imageprocessing. imageprocessing is particularly important for observational astronomy, medical diagnosis, autonomous driving, big data and artificial intelligence. For these applications, signal processing traditionally has mainly been performed electronically. However these, as well as new applications, particularly those involving real time video imageprocessing, are creating unprecedented demand for ultrahigh performance, including high bandwidth and reduced energy consumption. Here, we demonstrate a photonic signal processor operating at 17 Terabits/s and use it to process video image signals in real-time. The system processes 400,000 video signals concurrently, performing 34 functions simultaneously that are key to object edge detection, edge enhancement and motion blur. As compared with spatial-light devices used for imageprocessing, our system is not only ultra-high speed but highly reconfigurable and programable, able to perform many different functions without any change to the physical hardware. Our approach is based on an integrated Kerr soliton crystal microcomb, and opens up new avenues for ultrafast robotic vision and machine learning.
In the image super-resolution algorithm model, a large receptive field can provide more valuable features, so the Transformer with strong information interaction ability has achieved excellent results in image super-r...
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The article proposes a fusion technique and an algorithm for combining images recorded in the IR and visible spectrum in relation to the problem of processing products by robotic complexes in dust and fog. Primary dat...
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ISBN:
(纸本)9781510655461
The article proposes a fusion technique and an algorithm for combining images recorded in the IR and visible spectrum in relation to the problem of processing products by robotic complexes in dust and fog. Primary data processing is based on the use of a multi-criteria processing with complex data analysis and cross-change of the filtration coefficient for different types of data. The search for base points is based on the application of the technique of reducing the range of clusters (image simplification) and searching for transition boundaries using the approach of determining the slope of the function in local areas. As test data used to evaluate the effectiveness, pairs of test images obtained by sensors with a resolution of 1024x768 (8 bit, color image, visible range) and 640x480 (8 bit, color, IR image) are used. images of simple shapes are used as analyzed objects.
Special section editors Johan Debayle, Wolfgang Osten, and Dmitry Nikolaev introduce the Special Section on machinevision: Systems, Methods, and applications.
Special section editors Johan Debayle, Wolfgang Osten, and Dmitry Nikolaev introduce the Special Section on machinevision: Systems, Methods, and applications.
Retinopathy is a common complication of diabetes that can cause severe vision loss if not detected and managed promptly. In this study, we propose a comprehensive approach that leverages imageprocessing techniques to...
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ISBN:
(纸本)9781510665651;9781510665644
Retinopathy is a common complication of diabetes that can cause severe vision loss if not detected and managed promptly. In this study, we propose a comprehensive approach that leverages imageprocessing techniques to analyze fundus images of patients with diabetic retinopathy. Our primary focus is on vein extraction and hemorrhage detection, with exudate detection being performed only on specific images to showcase advancements in the current prototype algorithm. The dataset used in this project consists of images obtained from Mexican ophthalmology institutes, ensuring its relevance and applicability to the local population. By extracting veins and hemorrhages, we aim to capture crucial features indicative of the severity of retinopathy. These generated images, along with the original dataset, are utilized to train convolutional neural network (CNN) models, enabling accurate classification of the disease's degree into three categories. The significance of this project lies in its potential to serve as an auxiliary tool in diagnosing diabetic retinopathy. By automating the analysis of fundus images and providing objective classification results, our algorithm aims to assist healthcare professionals in making informed decisions regarding treatment and management options. The proposed method can potentially enhance the efficiency and precision of diabetic retinopathy (DR) diagnosis, improving Mexican health outcomes.
The evolution of low-cost embedded systems is growing exponentially;likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algori...
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The evolution of low-cost embedded systems is growing exponentially;likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algorithms. We review the state-of-the-art strategies available for Tiny machine Learning (TinyML) implementation to provide a complete overview using various existing embedded vision and control systems. Our discussion divides the article into four critical aspects that high-cost and low-cost embedded systems must include to execute real-time control and imageprocessing tasks, applying TinyML techniques: Hardware Architecture, vision System, Power Consumption, and Embedded Software Platform development environment. The advantages and disadvantages of the reviewed systems are presented. Subsequently, the perspectives of them for the next ten years are present. A basic TinyML implementation for embedded vision application using three low-cost embedded systems, Raspberry Pi Pico, ESP32, and Arduino Nano 33 BLE Sense, is presented for performance analysis.
In the production of power cables, the performance test of the cable insulation sheath is an important part. Compared with traditional testing methods, machinevision has the advantages of stable operation, high preci...
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With the rapid development of artificial intelligence, machine learning applications in visual design have become increasingly widespread, particularly in the field of imageprocessing. This study presents an intellig...
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ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
With the rapid development of artificial intelligence, machine learning applications in visual design have become increasingly widespread, particularly in the field of imageprocessing. This study presents an intelligent image generation platform based on Generative Adversarial Networks (GANs) aimed at improving design efficiency and creative expression. The platform integrates Deep Convolutional GANs (DCGANs) with style transfer techniques to efficiently generate high-quality images with artistic and visual appeal. Experimental results demonstrate that the model outperforms traditional methods in terms of clarity (PSNR of 28.4 dB), realism (Inception Score of 7.5), and style consistency (Style Loss of 0.0025). User experience evaluations indicate that designers rate the platform highly for ease of use and image generation quality. This model not only enhances design efficiency but also serves as a powerful tool for creative visual tasks, advancing the automation and intelligence of the design process.
Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhan...
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Although image captioning has a vast array of applications, it has not reached its full potential in languages other than English. Arabic, for instance, although the native language of more than 400 million people, re...
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