The goal of image enhancement is to improve specific features or details of an image and enhance its overall visual quality. We introduce a novel image enhancement algorithm based on block-rooting processing combined ...
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Diabetic retinopathy (DR), a severe complication arising from diabetes, make a significant threat to vision due to the deterioration of retinal blood vessels. This research work proposes a comprehensive methodology fo...
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ISBN:
(纸本)9798350373301;9798350373295
Diabetic retinopathy (DR), a severe complication arising from diabetes, make a significant threat to vision due to the deterioration of retinal blood vessels. This research work proposes a comprehensive methodology for the automated detection, grading, and segmentation of DR, leveraging advanced imageprocessing, deep learning techniques and machine learning. The study utilizes the Indian Diabetic Retinopathy image dataset (IDRID), comprising 81 fundus images and labels, to rigorously evaluates the proposed methodology. Key steps include detailed image preprocessing, VGG16-based feature extraction, Random Forest classifier-based grading, and innovative segmentation techniques for lesion localization. The evaluation demonstrates exceptional performance, with both VGG16 and ResNet50 architectures achieving over 99% accuracy. The process of semantic segmentation enhances interpretability, supporting clinical decision-making in retinopathy diagnosis. While the results are promising, future validation on diverse datasets and careful consideration of ethical implications are essential for responsible deployment in clinical settings. The proposed methodology signifies a significant step toward precise diagnostics and improved patient outcomes in diabetic retinopathy and holds potential for broader applications in retinal disease diagnosis.
The verification of IP core with imageprocessing algorithm is important for SoC and FPGA application in the field of machinevision. This paper proposes a verification framework with general purpose, real-time perfor...
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In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources,...
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In recent years there has been an increased interest towards edge computing, i.e., computing performed on distributed devices as opposed to centralized high-power hubs. Examples of edge computing would be the local im...
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In recent years there has been an increased interest towards edge computing, i.e., computing performed on distributed devices as opposed to centralized high-power hubs. Examples of edge computing would be the local imageprocessing performed on Unmanned Autonomous Vehicles (UAV's) or the specialized machinevision systems on drones. These edge computing applications require schemes that are efficient with power and memory and typically must operate real-time. Many state-of-the-art imageprocessing solutions that employ advanced optimization and deep neural networks (NNs) achieve impressive benchmark results, but are computationally demanding and thus on many occasions, impractical. The additional requirement for a range of applications is noise robustness or the ability to work in (extreme) low-light conditions; reasonable quality image or accurate object classification may be critical when there is low light flux or when the environment is over-saturated with other signals. Here, we approach edge computing with a combination of optical preprocessing and shallow NN and we show that this hybrid approach greatly reduces the computational requirements. For low-SNR imaging, we develop a technique that reconstructs objects and scenes from their Fourier-plane images. The optical preprocessing is performed via encoded diffraction with optical vortex singularities. The optical vortex encoder achieves differentiation of the already-compressed Fourier-plane patterns and enables facile inverse inference of the original object scene. We demonstrate that our method is robust to noise. And for a simple NN architecture (one or two layers), leads to generalization, i.e., reconstruction of objects from classes that are greatly different from the ones the NN was trained on. Our research identifies strong potential for swift hybrid imaging systems with edge computing applications and highlights the valuable function of the vortex encoder for spectral differentiation.
The fashion industry’s traditional price-setting methods, based on historical sales and Fashion Week trends, are inadequate in the digital era. Rapid changes in collections and consumer preferences necessitate advanc...
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Convolution is a fundamental operation in imageprocessing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, however, can introduce undesirable boundar...
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With the continuous development of furniture design, the machining accuracy and surface quality of die steel have been paid more and more attention. The traditional grinding process has problems such as low efficiency...
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With the continuous development of furniture design, the machining accuracy and surface quality of die steel have been paid more and more attention. The traditional grinding process has problems such as low efficiency and unstable quality, so it is urgent to introduce advanced technical means to improve the intelligent level of the processing process. This study aims to explore the application of the die steel grinding process based on machinevision and wireless sensor network equipment in furniture design, and improve the efficiency and quality of the grinding process through real-time monitoring and data analysis. A grinding monitoring platform integrating machinevision system and wireless sensor network was developed. A machinevision system is used to capture critical image data during the grinding process in real time, while a wireless sensor network is used to collect and transmit grinding parameters, including temperature, vibration and acoustic emission signals. By analyzing the acquired data, the optimized grinding parameters and control strategy are worked out. The experimental results show that the grinding process using machinevision and wireless sensor network has improved the relevant parameters compared with the traditional methods. The real-time monitoring capability of the system significantly reduces the failure rate during grinding and provides a more stable and reliable die steel processing solution for furniture design.
In real-world applications, images and videos used in computer vision algorithms are often distorted due, e.g., to compression and transmission. As a result, they may lose relevant information content, or they may dev...
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ISBN:
(纸本)9789082797091
In real-world applications, images and videos used in computer vision algorithms are often distorted due, e.g., to compression and transmission. As a result, they may lose relevant information content, or they may deviate significantly from the original data distribution used to train the machine task, rendering the visual content practically useless with respect to its initial purpose. Evaluating the utility of an image for machine tasks has received little attention so far in the literature. This concept of utility is substantially different from the visual quality typically used in image/video compression, as the latter is related to the perception of the human visual system. In this paper, we propose a definition of utility as the degree of confidence by which a machine task is able to take a decision. In this context, we propose a full-reference utility loss measure: we assume that the decision on the pristine image is correct (reference), and we measure the utility loss as the confidence reduction in the decision due to a noisy input with respect to this reference. We apply this general definition on two specific tasks, classification and object detection, and we study practical solutions to predict utility, as well as the ability of our utility measure to generalize across tasks.
Gradient computing is a low-level technology widely used in imageprocessing. For large gradient magnitude, the pixel value in the field changes a lot, and for small gradient magnitude the pixel in the domain changes ...
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ISBN:
(纸本)9781665464680
Gradient computing is a low-level technology widely used in imageprocessing. For large gradient magnitude, the pixel value in the field changes a lot, and for small gradient magnitude the pixel in the domain changes little. This is the basis of classical edge extraction algorithms, but it is often necessary to manually set thresholds to differentiate. This paper innovatively brings out the concept of omnidirectional gradient, which uses flexible convolution kernel radius and special law to calculate, and omnidirectional gradient pays more attention to gradient direction and analyzes the relationship and change of the gradient direction with different kernel radius. We present here an algorithm for stylized edge extraction based on omnidirectional gradient, overcoming the drawback of classical edge extraction algorithms that require manual thresholding. Experimental results show that the proposed method outperforms the classical edge extraction methods in terms of adaptive, consistent, and visually friendlier features for infrared imaging. In addition, the algorithm is fast and efficient, its result can be used as real-time input for subsequent applications.
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