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.
In traditional conveyor belt edge detection methods, contact detection methods have a high cost. At the same time noncontact detection methods have low precision, and the methods based on the convolutional neural netw...
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In traditional conveyor belt edge detection methods, contact detection methods have a high cost. At the same time noncontact detection methods have low precision, and the methods based on the convolutional neural network are limited by the local operation features of the convolution operation itself, causing problems such as insufficient perception of long-distance and global information. In order to solve the above problems, a dual flow transformer network (DFTNet) integrating global and local information is proposed for belt edge detection. DFTNet could improve belt edge detection accuracy and suppress the interference of belt image noise. In this paper, the authors have merged the advantages of the traditional convolutional neural network's ability to extract local features and the transformer structure's ability to perceive global and long-distance information. Here, the fusion block is designed as a dual flow encoder-decoder structure, which could better integrate global context information and avoid the disadvantages of a transformer structure pretrained on large datasets. Besides, the structure of the fusion block is designed to be flexible and adjustable. After sufficient experiments on the conveyor belt dataset, the comparative results show that DFTNet can effectively balance accuracy and efficiency and has the best overall performance on belt edge detection tasks, outperforming full convolution methods. The processingimage frame rate reaches 53.07 fps, which can meet the real-time requirements of the industry. At the same time, DFTNet can deal with belt edge detection problems in various scenarios, which gives it great practical value.
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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Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learn...
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Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presence of unseen videos;and usually, deep learning models require large amounts of data to avoid overfitting. Recently, graph learning has attracted significant attention in many computer visionapplications since they provide tools to exploit the geometrical structure of data. In this work, concepts of graph signal processing are introduced for MOS. First, we propose a new algorithm that is composed of segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the theory of recovery of graph signals. Second, theoretical developments are introduced, showing one bound for the sample complexity in semi-supervised learning, and two bounds for the condition number of the Sobolev norm. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both static and moving camera videos. Our algorithm is also adapted for Video Object Segmentation (VOS) tasks and is evaluated on six publicly available datasets outperforming several state-of-the-art methods in challenging conditions.
The paper proposes a prototype of an algorithm based on the use of machinevision methods, which allows automatic identification and selection of fields sown with agricultural crops on images. The algorithm works with...
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ISBN:
(纸本)9783030869601;9783030869595
The paper proposes a prototype of an algorithm based on the use of machinevision methods, which allows automatic identification and selection of fields sown with agricultural crops on images. The algorithm works with satellite images and consists of two stages. At the first stage, the image undergoes initial processing, after which edge detection and contour finding algorithms are applied to it. At the second stage, the obtained image areas enclosed within the contours are represented as a set of numerical and logical parameters which are used for filtering and classification of the areas.
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