Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy h...
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Serial shelf images are frequently used in product availability research in the retail industry. However, in order to accurately count the products and determine the shelf arrangement in these serial images, these ima...
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ISBN:
(数字)9798350388961
ISBN:
(纸本)9798350388978
Serial shelf images are frequently used in product availability research in the retail industry. However, in order to accurately count the products and determine the shelf arrangement in these serial images, these images must be stitched with imageprocessingalgorithms. Appropriate metrics are also needed to measure the quantitative quality of the stitched images. In this study, we developed a new method to measure the performance of algorithms used to stitch multiple images that have intersections. In this method, object detection was performed separately on individual images and on the stitched image. image quality is calculated by looking at the overlap of the projection coordinates of the objects detected in the individual images and the stitched image. For this purpose, we prepared a dataset
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consisting of retail shelf scenes and tested our proposed method by comparing state-of-the-art stitched algorithms on this dataset. As a result of the tests, we showed numerically that deep learning-based methods achieve higher performance than classical methods.
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https://***/rempeople/remretail100
The Coronavirus disease (COVID-19) infection has become a pandemic, and this is the most critical problem that has occurred in Thailand and also expanded all over the world. As such, it is not astonishing to know that...
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ISBN:
(纸本)9781665489034
The Coronavirus disease (COVID-19) infection has become a pandemic, and this is the most critical problem that has occurred in Thailand and also expanded all over the world. As such, it is not astonishing to know that this virus has had a direct effect on hospitals with the delayed screening of patients because of the increasing number of daily cases and the shortage of medical personnel and restricted treatment space. Due to such restrictions, in this study, we used a clinical decision-making system with predictive algorithms. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. Moreover, image classification is one interesting aspect of imageprocessing. Convolutional neural network (CNN) is a widely used algorithm for image classification by separating the images of the COVID-19 disease, images with a lung infection, and normal images. To evaluate the predictive performance of our models, precision, F1-score, recall, receiver operating characteristic (ROC) curve (area under the ROC curve), and accuracy scores were used. It was observed that the predictive models trained on the laboratory findings could be used to predict the COVID-19 infection as well and could be helpful for medical experts to appropriately prioritize the resources. This could be employed to assist medical experts in validating their initial laboratory findings and could also be used for clinical prediction studies.
The efficiency of diagnostic processes is paramount in healthcare, particularly for breast cancer detection and treatment. This research explores the queuing dynamics within the breast cancer imaging process, emphasiz...
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ISBN:
(数字)9798331540821
ISBN:
(纸本)9798331540838
The efficiency of diagnostic processes is paramount in healthcare, particularly for breast cancer detection and treatment. This research explores the queuing dynamics within the breast cancer imaging process, emphasizing the role of advanced imageprocessing techniques alongside computing, communication, and intelligent systems to streamline patient flow and reduce wait times. By employing sophisticated imageprocessingalgorithms and intelligent queuing models, we scrutinize various strategies through data obtained from a premier medical facility. Our methodology integrates statistical analysis and simulation techniques to evaluate the effectiveness of different queuing approaches on operational efficiency and patient outcomes. Results indicate that intelligent imageprocessingsystems significantly enhance service efficiency, alleviate patient anxiety, and elevate overall healthcare delivery.
Lymphatic Filariasis commonly known as ‘Elephantiasis’ is a vector congenital disease that creates disfiguring as well as disabling in the forbearer. This gruesome disease is acquired in the childhood phase, but its...
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The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical imageprocessing methods performed at the edge, such as featu...
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ISBN:
(纸本)9781450395595
The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical imageprocessing methods performed at the edge, such as feature extraction or edge detection, use convolutional filters that are energy, computation, and memory hungry algorithms. But edge devices and cameras have scarce computational resources, bandwidth, and power and are limited due to privacy constraints to send data over to the cloud. Thus, there is a need to process image data at the edge. Over the years, this need has incited a lot of interest in implementing neuromorphic computing at the edge. Neuromorphic systems aim to emulate the biological neural functions to achieve energy-efficient computing. Recently, Oscillatory Neural Networks (ONNs) present a novel brain-inspired computing approach by emulating brain oscillations to perform auto-associative memory types of applications. To speed up image edge detection and reduce its power consumption, we perform an in-depth investigation with ONNs. We propose a novel imageprocessing method by using ONNs as a Heterogeneous Associative Memory (HAM) for image edge detection. We simulate our ONN-HAM solution using first, a Matlab emulator, and then a fully digital ONN design. We show results on gray scale square evaluation maps, also on black and white and gray scale 28x28 MNIST images and finally on black and white 512x512 standard test images. We compare our solution with standard edge detection filters such as Sobel and Canny. Finally, using the fully digital design simulation results, we report on timing and resource characteristics, and evaluate its feasibility for real-time imageprocessing applications. Our digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz system frequency) respecting real-time camera constraints. This work is the first to explore ONNs as hetero-associative memory for imageprocessing applications.
There has been a rise in the frequency of fire-related calamities all over the globe, which leads to the need for an efficient fire detection system to avoid high losses or fatalities. This paper focuses on real-time ...
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ISBN:
(数字)9798350378092
ISBN:
(纸本)9798350378108
There has been a rise in the frequency of fire-related calamities all over the globe, which leads to the need for an efficient fire detection system to avoid high losses or fatalities. This paper focuses on real-time fire detection techniques through image and video processing. In particular, this paper is aimed at a color detection approach that uses HSV and YCbCr color models for detecting only fire pixels along with the implementation of fire movement detection approach by comparing consecutive frames from the live feed. Overall, the study contributes to advancing fire detection methodology, highlighting the potential of imageprocessing methods in real-time fire detection systems.
Inverse halftoning is a technique used to recover realistic images from ancient prints (e.g., photographs, newspapers, books). The rise of deep learning has led to the gradual incorporation of neural network designs i...
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ISBN:
(纸本)9781713871088
Inverse halftoning is a technique used to recover realistic images from ancient prints (e.g., photographs, newspapers, books). The rise of deep learning has led to the gradual incorporation of neural network designs into inverse halftoning methods. Most of existing inverse halftoning approaches adopt the U-net architecture, which uses an encoder to encode halftone prints, followed by a decoder for image reconstruction. However, the mainstream supervised learning paradigm with element-wise regression commonly adopted in U-net based methods has poor generalization ability in practical applications. Specifically, when there is a large gap between the dithering patterns of the training and testing halftones, the reconstructed continuous-tone images have obvious artifacts. This is an important issue in practical applications, since the algorithms for generating halftones are ever-evolving. Even for the same algorithm, different parameter choices will result in different halftone dithering patterns. In this paper, we propose the first generative halftoning method in the literature, which regards the black pixels in halftones as physically moving particles, and makes the randomly distributed particles move under some certain guidance through reverse diffusion process, so as to obtain desired halftone patterns. In particular, we propose a Conditional Diffusion model for image Halftoning (CDH), which consists of a halftone dithering process and an inverse halftoning process. By changing the initial state of the diffusion model, our method can generate visually plausible halftones with different dithering patterns under the condition of image gray level and Laplacian prior. To avoid introducing redundant patterns and undesired artifacts, we propose a meta-halftone guided network to incorporate blue noise guidance in the diffusion process. In this way, halftone images subject to more diverse distributions are fed into the inverse halftoning model, which helps the model to lear
Parkinson's disease, a neurological disorder which affects the nervous system, manifests as unintentional and uncontrollable movements in the body. With over 6 million individuals globally affected, early detectio...
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Condition monitoring and predictive maintenance of induction motors have great relevance in industrial applications. Nowadays, there are different techniques to analyze electronic signals from different types of senso...
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