The classification of an eye disease may be useful in the clinical setting for describing the current state of the eye, evaluating the effectiveness of treatment, and selecting the best course of action. Every classif...
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Benefiting from vigorous development of the deep learning, many CNN-based image super-resolution methods have been appearing and achieve better results than traditional algorithms. However, it is difficult for most al...
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Benefiting from vigorous development of the deep learning, many CNN-based image super-resolution methods have been appearing and achieve better results than traditional algorithms. However, it is difficult for most algorithms to adaptively adjust spatial region and channel features at the same time, let alone the information exchange between them. In addition, the exchange of information between attention modules is even less visible to researchers. To solve these problems, we put forward a lightweight spatial-channel adaptive coordination of multilevel refinement enhancement network(MREN). Specifically, we construct a space-channel adaptive coordination block, which enables the network to learn the spatial region and channel feature information of interest under different receptive fields. Furthermore, the information of corresponding feature processing level in the spatial part and the channel part is exchanged by jumping connection to achieve the coordination between the two. We built a bridge of communication between attention modules through a simple linear combination operation, so as to more accurately and continuously guide the network to pay attention to the information of interest. Extensive experiments on several standard test sets have shown that our MREN achieves superior performance over other advanced algorithms with a very small number of parameters and very low computational complexity.(c) 2022 Elsevier B.v. All rights reserved.
Nowadays, Machine learning (ML) plays a significant role in processing multimedia based on user search queries. To develop an efficient classification model that processes the high dimensional data, an ensemble model ...
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image classification is an essential technology that is widely used in all aspects of human life. This work combined multiple image feature sources using deep learning algorithms to identify photos from the publicly a...
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The global prevalence of skin cancer has seen a concerning increase over recent decades, now accounting for one in every three cancer cases worldwide. The unchecked growth of aberrant cells in the skin can lead to ski...
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An expert system tailored for foot pressure analysis and imprint detection, with a focus on enhancing podiatric treatments and ergonomics. The system comprises three key components: a foot pressure analyser, an image ...
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The secure Wireless Sensor Network (WSN) architecture was designed in such a way that the tradeoffs among efficiency, scalability and security were balanced. It was consisted of several sensor nodes, cluster heads, ba...
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The significance of high-speed machine vision in scientific and technological fields is growing, especially with the era of Industry 4.0 technologies. There are several pattern-matching algorithms that have various in...
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The significance of high-speed machine vision in scientific and technological fields is growing, especially with the era of Industry 4.0 technologies. There are several pattern-matching algorithms that have various intriguing applications in ultralow-latency machine vision processing. However, the low frame rate of image sensors-which usually operate at tens of hertz-fundamentally limits the processing rate. The paper will conceptualize and develop the computerized pattern recognition technique that can be applied to investigate light beam profiles and extract the desired information according to the purpose required in this case study. In the current work, the automatic detection and inspection of laser spots were designed to perform analysis and alignment for laser beam in comparison with the electron spot beam using the LabvIEW graphical programming environment, especially when the laser and electron beams overlap. This is one of the important steps for realizing the fundamental aim of test-FEL to produce short wavelengths with the second, third, and fifth harmonics at 131.5, 88, and 53 nm, respectively. The tentative version of the program achieved the elementary purpose, which fulfilled the accurate transversal alignment of the ultrashort laser pulses with the electron beam in the system of the FEL test facility at MAX-Lab, in addition to studying the beam's stability and jittering range. Copyright (C) 2024 The Authors.
This article discusses the process of identifying roadway defects used in automated mobile laboratories by searching for the edges of an object. The introduction of automated systems using machine vision will signific...
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With the advancements in modern medical imaging systems, the diagnostic image data has increased exponentially. The future medical applications seek medical imaging devices to be portable. image quality and real-time ...
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With the advancements in modern medical imaging systems, the diagnostic image data has increased exponentially. The future medical applications seek medical imaging devices to be portable. image quality and real-time processing are of prime importance in medical image compression. Therefore the volumetric medical images need to be compressed in perceptually lossless manner. Also the time taken to compress the images before transmitting (or storing), should be small. In this paper, an algorithm for lossless and perceptually-lossless medical image compression is proposed. The proposed algorithm uses two small lists and two small state-tables to encode an image. The compression efficiency is comparable to the state-of-the-art lossless compression techniques. Also the computational complexity and memory requirement are realistic for portable medical imaging devices. Combining all the three features, it is obvious that the proposed algorithm is a better candidate for image compression in comparison to all the state-of-art compression algorithms that we know of, for volumetric-medical imaging systems. (C) 2020 The Author. Published by Elsevier B.v. on behalf of King Saud University.
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