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检索条件"任意字段=7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021"

共 272 条记录，以下是41-50 订阅

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SwinT-Unet: Hybrid architecture for Medical image Segmentation Based on Swin transformer block and Dual-Scale Information 7

SwinT-Unet: Hybrid architecture for Medical Image Segmentati...

引用

7th international conference on image and signal processing and their applications, ISPA 2022

作者： Atek, Sara Mehidi, Imane Jabri, Dalel Belkhiat, Djamel E.C. Ferhat Abbas University Dac Hr Laboratory Setif Algeria

ISBN: (纸本)9781665480420

the fast development of Convolution Neural Networks (CNN) based on U-shaped architecture has shown innovative improvements in the fields of image segmentation. However, these approaches cannot learn global information in images due to the local aspect of the convolution operation. this paper deals with designing a hybrid method of medical image segmentation. Taking advantage of Shifted windows (Swin) transformer block to extract fine-grained features and the Transformer Interactive Fusion (TIF) module to create a fusion between features of different scales, the proposed approach consists of a dual-Scale encoder-Swin transformer U-shaped architecture (SwinT-Unet). the effectiveness of this method has been evaluated on the Synapse multi-organ CT dataset. the suggested segmentation demonstrated more efficiency than the results of some other current methods. © 2022 ieee.

关键词： image segmentation

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QHash: An efficient hashing algorithm for low-variance image deduplication 23

QHash: An efficient hashing algorithm for low-variance image...

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23rd ieee international conference on High Performance Computing and Communications, 7th ieee international conference on Data Science and Systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Xuan Chang, Liqiong Liu, Xue School of Computer Science McGill University MontrealQCH3A0G4 Canada

ISBN: (纸本)9781665494571

Attributed to the widespread use of general artificial intelligence (AI), large-scale datasets have become a critical component for the success of AI-powered applications. While collecting a larger dataset is desirable in general, studies have revealed that many databases include duplicated images. the accumulation of redundant images will result in excessive resource usage and inefficient cloud storage utilization. To get rid of the duplicates, hashing-based methods have been developed for the problem of image deduplication. However, as demonstrated in our experiments, current approaches fail in dataset with small visual difference, such as medical images. To this end, we propose QHash, which achieves effective image deduplication on the low-variance dataset. QHash leverages Vector Quantized Variational AutoEncoder (VQ-VAE) to learn the data distribution in an unsupervised manner. In addition, hash sequences are implemented using integer-based tensor, enabling distance calculation and deduplication being processed in parallel on either CPU or GPU. Extensive experiments show that QHash outperforms other baseline approaches by at least 50%, while is about 23% memory efficient and 18% deduplication time speedup. © 2021 ieee.

关键词： Visualization Tensors Smart cities Memory management Graphics processing units Parallel processing Lesions

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A real-time compressing method for complex SAR images

A real-time compressing method for complex SAR images

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7th international conference on signal processing

作者： Xia, WP Wang, ZS Zhai, JF Han, JZ Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China

ISBN: (纸本)7121002159

Complex image compression is widely used in diverse remote sensing applications in order to reduce the costs of data storage and release the burden of data transmission on limited data transmission channels. However. the huge amount of data makes it difficult to compress in real-time. A novel real-time compressing method for complex SAR images is presented in this paper. the system uses a fixed-point DSP of TI SM320c6416 with IGB SDRAM (Synchronous Dynamic Random Access Memory). After removing the redundancy of complex image in frequency domain, arithmetic codec is applied for statistical redundancy further. With the least data regress times, good performance is achieved. the results show that compressing a complex image of 8192*2048 needs only 4315ms in an acceptable distortion with compression ratio of 16.214:1. So the method is highly suitable for real-time (or low delay) applications.

关键词： complex SAR image DRT (data regress time) real-time compression

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Efficient feature matching algorithms applied to robust image processing 7

Efficient feature matching algorithms applied to robust imag...

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7th international conference on Intelligent Computing and signal processing, ICSP 2022

作者： Chen, Kaige Ye, Ning Xie, Jianhong Taiyuan University of Technology School of Mechanical and Transportation Engineering Taiyuan China Dalian Polytechnic University School of Art and Design Liaoning Dalian China Shenyang University of Technology School of Artificial Intelligence Liaoning Shenyang China

ISBN: (纸本)9781665478571

image feature matching is an essential problem in computer vision. Also, feature matching is an important task in many computer vision applications, such as in VR technology, drones, and autonomous driving. In this paper, four algorithms for matching are presented first. We perform outdoor scene feature matching experiments on four algorithms: Surf, Sift, Orb, Brisk. the original image is rotated, the sharpness is continuously blurred, the scene is transformed, specific regions are scaled, and the resolution is changed. the number of feature points that correctly matched the transformed image to the original image was compared. In this paper, the stability is determined by comparing the operation time of the four algorithms in the case of unified processing. then, the RANSAC algorithm detects the proportion of internal and external points and compares the robustness. It is concluded that the ORB algorithm is the fastest, and the SURF algorithm is the most robust. © 2022 ieee.

关键词： Computer vision

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Enhancement of a compact support time-frequency distribution derived from a polynomial kernel using image processing 7

Enhancement of a compact support time-frequency distribution...

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7th international conference on image and signal processing and their applications, ISPA 2022

作者： Adoul, Mohammed Amin Abed, Mansour Belouchrani, Adel University Abdel Hamid Ibn Badis of Mostaganem Lss Lab. Electrical Engineering Department Mostaganem Algeria Ecole Nationale Polytechnique Ldccp Lab. Electrical Engineering Department El Harrach Algiers Algeria

ISBN: (纸本)9781665480420

the polynomial Cheriet-Belouchrani distribution (PCBD) is considered to be one of the best time-frequency distributins (TFDs) available for the analysis of time varying signals. As for all quadratic distributions, due to the unavoidable smoothing effects of the kernel, the recently proposed PCBD suffers from degradation of time-frequency localization. the aim of this paper is to enhance the readability and improve the concentration of this distribution. the obtained time-frequency map is enhanced using a specific method based on two-dimensional mean filter, automatic binarization and morphological image processing techniques. the enhanced PCBD is compared to the original distribution using several tests on real-life and multi-component signals with linear and nonlinear frequency modulation (FM) components including the noise effects. Also, comparisons are made by considering a selection of the best-known time-frequency representations (TFRs). © 2022 ieee.

关键词： image enhancement

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Multi-scale image Decomposition Using a Local Statistical Edge Model 7

Multi-scale Image Decomposition Using a Local Statistical Ed...

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7th ieee international conference on Virtual Reality (ICVR

作者： Wong, Kin-Ming City Univ Hong Kong Sch Creat Media Hong Kong Peoples R China

ISBN: (纸本)9781665423090

We present a progressive image decomposition method based on a novel non-linear filter named Sub-window Variance filter. Our method is specifically designed for image detail enhancement purpose;this application requires extraction of image details which are small in terms of both spatial and variation scales. We propose a local statistical edge model which develops its edge awareness using spatially defined image statistics. Our decomposition method is controlled by two intuitive parameters which allow the users to define what image details to suppress or enhance. By using the summed-area table acceleration method, our decomposition pipeline is highly parallel. the proposed filter is gradient preserving and this allows our enhancement results free from the gradient-reversal artefact. In our evaluations, we compare our method in various multi-scale image detail manipulation applications with other mainstream solutions.

关键词： multi-scale image decomposition image processing edge-preserving filter

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DeepLoc: A Deep Neural Network-based Indoor Positioning Framework 23

DeepLoc: A Deep Neural Network-based Indoor Positioning Fram...

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作者： Liu, Saining Ren, Qianqian Li, Jinbao Xu, Hui Heilongjiang University Department of Computer Science and Technology Harbin150080 China Shandong Artificial Intelligence Institute Qilu University of Technology Jinan250014 China

ISBN: (纸本)9781665494571

the growing attention on location-based services has promoted the development of indoor localization studies. Existing techniques mainly use Received signal Strength Indicator (RSSI) of wireless signals as location fingerprint. Inspired by deep learning techniques for signal processing, we propose a deep neural network-based framework (DeepLoc) to implement Wi-Fi fingerprint positioning. In order to improve localization performance, we further design a network division based optimization algorithm. We first adopt greedy algorithm to locate the user in a sub-area, and then reconstruct a smaller fingerprint database, which is fed into the training model. Finally, we evaluate the proposed framework. Experimental results show that DeepLoc can improve the localization accuracy efficiently and obtain better performance. © 2021 ieee.

关键词： Location awareness Training Greedy algorithms Deep learning signal processing algorithms Fingerprint recognition Received signal strength indicator

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Research on Remote Sensing image Classification Algorithm Based on EfficientNet 7

Research on Remote Sensing Image Classification Algorithm Ba...

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7th international conference on Intelligent Computing and signal processing, ICSP 2022

作者： Yin, Hang Yang, Chengyi Lu, Jiayi Xi'an University of Science and Technology School of Computer Science and Technology Xi'an China Harbin University of Science and Technology School of Computer Science and Technology Harbin China Beijing University of Chemical Technology School of Mechanical and Electrical Engineering Beijing China

ISBN: (纸本)9781665478571

Accurate classification of remote sensing images is important in remote sensing applications. In order to verify the efficiency and accuracy of efficientnet algorithm in remote sensing image classification, this paper classifies the UCMerced LandUse dataset based on EfficientNet. the experimental results show that compared with VGGNet, ResNet and MobileNet, the EfficientNet network introduces composite parameters and scales depth, width and resolution at the same time. the accuracy of EfficientV2-s in the verification set is 16.5%, 5.2%, 1.8% and 1.7% higher than that of VGG, MobileNetV2, ResNet34 and EfficientNet-b0, which shows the efficiency and accuracy of EfficientNet network in remote sensing image classification data set. © 2022 ieee.

关键词： Efficiency

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On an adaptive ICA method with application to biomedical image analysis

On an adaptive ICA method with application to biomedical ima...

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7th international conference on signal processing

作者： Hong, BM Calhoun, VD Inst Living Olin Neuropsychiat Res Ctr Hartford CT 06106 USA

ISBN: (纸本)7121002159

Conventional ICA algorithms typically model the probability density functions of the underlying sources as highly kurtotic or symmetric. However, when source data violate the assumptions (e.g., low kurtosis), the conventional ICA methods might not work well. Adaptive modeling of the underlying sources thus becomes an important issue for ICA applications. this paper proposes the Log Weibull model to represent skewed distributed sources within the Infomax framework and further introduces an adaptive ICA method. the central idea is to use a two-stage separation process: 1) Conventional ICA used for all channel sources to obtain initial independent source estimates;2) source density estimate-based nonlinearities adaptively used for the "refitting" separation to all channel sources. the ICA algorithm is based on flexible nonlinearities of density matched candidates, Our simulations demonstrate the effectiveness of this approach.

关键词： image analysis

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Non-Sliding Window Decoding of Braided Convolutional Codes 7

Non-Sliding Window Decoding of Braided Convolutional Codes

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7th international conference on image and signal processing and their applications, ISPA 2022

作者： Amamra, Imed Derouiche, Nadir Laouar, Oulfa 20th August 1955 University of Skikda Lab. Res Ict Team Dept. of Electrical Engineering Skikda Algeria

ISBN: (纸本)9781665480420

this paper proposes a new approach for window decoding of Braided Convolutional Codes (BCC) using the iterative LogMAP algorithm. the approach is based on a window of component decoders which operate in parallel as they exploit the tail-biting termination of the component convolutional encoders, to obtain decoupled circular trellises. Using a non-sliding parallel window scheme for decoding Blockwise BBC, instead of the progressive sliding window technique, achieves a significant reduction in computational complexity with less decoding iterations for a particular error correction performance. Simulation results, over AWGN channel, show that the performance of the proposed approach exceeds those obtained by other algorithms presented in the existing literature with much less decoding iterations and an improved gain of up to 0.4dB. Additionally, the proposed algorithm has much less implementation requirements and complexity compared to the sliding window scheme. © 2022 ieee.

关键词： Convolutional codes

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