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MULTIPLICATION-AVOIDING VARIANT OF POWER ITERATION WITH applications 47

MULTIPLICATION-AVOIDING VARIANT OF POWER ITERATION WITH APPL...

引用

47th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Pan, Hongyi Badawi, Diaa Miao, Runxuan Koyuncu, Erdem Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9781665405409

Power iteration is a fundamental algorithm in data analysis. It extracts the eigenvector corresponding to the largest eigenvalue of a given matrix. applications include ranking algorithms, principal component analysis (PCA), among many others. Certain use cases may benefit from alternate, non-linear power methods with low complexity. In this paper, we introduce multiplication-avoiding power iteration (MAPI). MAPI replaces the standard l(2) inner products that appear at the regular power iteration (RPI) with multiplication-free vector products, which are Mercer-type kernels that induce the l(1) norm. For an n x n matrix, MAPI requires n multiplications, while RPI needs n(2) multiplications per iteration. Therefore, MAPI provides a significant reduction of the number of multiplication operations, which are known to be costly in terms of energy consumption. We provide applications of MAPI to PCA-based image reconstruction as well as to graph-based ranking algorithms. When compared to RPI, MAPI not only typically converges much faster, but also provides superior performance.

关键词： Power iteration multiplication-free algorithms principal component analysis PageRank algorithm

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ON THE RELEVANCE OF MULTI-GRAPH MATCHING FOR SULCAL GRAPHS 29

ON THE RELEVANCE OF MULTI-GRAPH MATCHING FOR SULCAL GRAPHS

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IEEE International conference on image processing (ICIP)

作者： Yadav, R. Dupe, F. x. Takerkart, S. Auzias, G. Aix Marseille Univ CNRS Inst Neurosci Timone UMR 7289 Marseille France Aix Marseille Univ Inst Marseille Imaging Marseille France Aix Marseille Univ CNRS Lab Informat & Syst UMR 7020 Marseille France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Fine-scale characterization of the geometry of the folding patterns of the brain is a key processing step in neuroscience, with high impact applications such as for uncovering biomarkers indicative of a neurological pathology. Sulcal graphs constitute relevant representations of the complex and variable geometry of the cortex of individual brains. Comparing sulcal graphs is challenging due to variations across subjects in the number of nodes, graph topology and attributes (on both nodes and edges). Graph matching experiments on real data are limited by the absence of ground truth. In this paper we propose to generate synthetic graphs to benchmark graph matching methods and assess their robustness to noise on attributes and to the presence of un-matchable nodes. Three multi-graph matching methods are compared to one pairwise approach in various simulation settings, showing that good matching performances can be achieved even with highly perturbed sulcal graphs. An experiment on real data from a population of 134 subjects further unveil large performance differences across matching methods.

关键词： Geometry Pathology Neuroscience Scalability image processing Sociology Benchmark testing

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Design and Implementation of 4-bit High Speed Array Multiplier for image Coding 1st

Design and Implementation of 4-bit High Speed Array Multipli...

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1st EAI International conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Aylapogu, Pramod Kumar Badita, Kalivaraprasad Geetha, P. Sama, Namratha Department of ECE Vardhaman College of Engineering Hyderabad India Guntur India School of Engineering and Technology Mohan Babu University Tirupati India

ISBN: (纸本)9783031350771

Multipliers are the utmost commonly used elements in today’s digital electronics. In digital signal processing systems, hardware multiplication is critical for obtaining high data throughput. Based on the increasing applications of electronic devices, various types of multipliers have emerged. Array Multiplier is the most fundamental multiplier of all. The main goal of our project is to create an optimized and fast 4-bit array multiplier. Parallel Array Multipliers are used in DSP applications to do multiplication at high speeds while meeting performance criteria. The simulations are run on the xilinx 14.7 ISE Design Suite. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image coding

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OPTIMAL TRANSPORT-BASED GRAPH MATCHING FOR 3D RETINAL OCT image REGISTRATION 29

OPTIMAL TRANSPORT-BASED GRAPH MATCHING FOR 3D RETINAL OCT IM...

引用

IEEE International conference on image processing (ICIP)

作者： Tian, xin Anantrasirichai, Nantheera Nicholson, Lindsay Achim, Alin Univ Bristol Bristol England

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Registration of longitudinal optical coherence tomography (OCT) images assists disease monitoring and is essential in image fusion applications. Mouse retinal OCT images are often collected for longitudinal study of eye disease models such as uveitis, but their quality is often poor compared with human imaging. This paper presents a novel but efficient framework involving an optimal transport based graph matching (OT-GM) method for 3D mouse OCT image registration. We first perform registration of fundus-like images obtained by projecting all b-scans of a volume on a plane orthogonal to them, hereafter referred to as the x-y plane. We introduce Adaptive Weighted Vessel Graph Descriptors (AWVGD) and 3D Cube Descriptors (CD) to identify the correspondence between nodes of graphs extracted from segmented vessels within the OCT projection images. The AWVGD comprises scaling, translation and rotation, which are computationally efficient, whereas CD exploits 3D spatial and frequency domain information. The OT-GM method subsequently performs the correct alignment in the x-y plane. Finally, registration along the direction orthogonal to the x-y plane (the z-direction) is guided by the segmentation of two important anatomical features peculiar to mouse b-scans, the Internal Limiting Membrane (ILM) and the hyaloid remnant (HR). Both subjective and objective evaluation results demonstrate that our framework outperforms other well-established methods on mouse OCT images within a reasonable execution time.

关键词： Retinal image registration optimal transport graph matching mouse OCT

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Industrial Talk - Computer Vision & AI applications for Research & Education: An Introduction of an Open Platform and the RoboCup Community

Industrial Talk - Computer Vision & AI Applications for Rese...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Jerry Tan Lattel

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Computer vision is a fascinating area for the AI research community. Coupled with hardware and/or other AI applications like speech interactions, the possible topic areas become even wider. An open platform will be introduced with functionalities demonstration. Further, to foster AI research collaborations, the session will touch briefly on the RoboCup@Home Education initiative.

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image style transferring method based on VGG-16 Network

Image style transferring method based on VGG-16 Network

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2022 International conference on image, signal processing, and Pattern Recognition, ISPP 2022

作者： Ni, Huiyan Su, Zhiqing College of Arts and Science Ohio State University OSU Ohio United States College of Computer science and technology Nanjing University NJU Nanjing China

ISBN: (纸本)9781510654846

With the development of techniques and society, people gradually have more and more serious requirements for artistic pleasure. People want to make artistic works by themselves, and thus, many photo applications presented image style transfer features to cater to consumers’ tendencies. In order to improve the present algorithm about the feature and get a more effective solution. We presented the VGG-16 structure and improved it when solving the image style transfer problem. After comparing different networks, weight ratio, and activation functions, we find the VGG-16 is the best choice according to its least iteration and shortest running time among the four networks. In addition, ReLu and LeakyReLu are better than Tanh and Sigmoid when choosing activation functions for their better handling texture ability. LBFGS is better than Adam, SGD and RMSprop when choosing optimizer for its smaller number of iterations. Finally, we obtain an optimized image style transfer model based on VGG-16 network. © 2022 SPIE.

关键词： Textures

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The Development of Mobile Application for Assisting COVID-19 Antigen Test Kit Results Reading

The Development of Mobile Application for Assisting COVID-19...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Waranusast, Rattapoom Pattanathaburt, Pattanawadee Naresuan Univ Fac Engn Phitsanulok Thailand Naresuan Univ Fac Publ Hlth Phitsanulok Thailand

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

The antigen test kits or ATKs have been widely used for screening COVID-19 infections because they can detect and give the results quickly and can be done easily by untrained patients. However, reading ATK test results could be difficult for some people and may lead to misinterpretations of the test results. This paper presents a preliminary study for developing a mobile application for helping in reading the results of the COVID-19 ATKs from an image using algorithms based on the YOLO object detection. The results are classified into 3 classes, negative, positive, and invalid. The negative and the invalid results are further refined by using the distances between the visible line and the letters on the test cassette. Experiments were conducted to test the efficiency and accuracy of the developed model with a mean of average precision or mAP of 0.986 and an F1 score of 0.970. The model was developed and put into a prototype mobile application using tools that support cross-platform technology.

关键词： COVID-19 Antigens Prototypes Object detection Information processing Mobile applications Classification algorithms

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StGAN: A Novel Symbolic signal Decomposition Base on GANs and Swin Transformer

StGAN: A Novel Symbolic Signal Decomposition Base on GANs an...

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Sun, Ming Guo, Li Chen, Long The College of Computer Science & Technology Qingdao University Qingdao China University of Macau Faculty of Science and Technology Macau China

ISBN: (纸本)9798350337020

The symbolic imagery signal decomposition is a common problem in digital signal processing. Its main purpose is to divide the symbolic imagery signal into different parts. However, in real-world applications, symbolic imagery signal captured by the camera is usually influenced by complex negative lighting environments such as highlights, reflections, drop shadows, or information loss. To overcome those problems, a generative adversarial network with Swin Transformer (StGAN) is proposed and applied to symbolic imagery signal decomposition and semantic segmentation tasks. In addition, a realistic image dataset taken in complex lighting conditions is proposed for symbolic imagery signal decomposition, which have complex lighting environments and edge information loss, we name it the MLT dataset. We demonstrate StGAN brings significant improvements in performance than some existing methods in the accuracy of symbolic imagery signal decomposition on MLT datasets. Further experiments on Sky and Facades datasets prove that StGAN works well in other tasks, especially when fewer data is involved in training, but it can still ensure good accuracy. © 2023 IEEE.

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Dense view interpolation of 4D light fields for real-time augmented reality applications

Dense view interpolation of 4D light fields for real-time au...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Yoshino, Hidemichi Kodama, Kazuya Hamamoto, Takayuki Tokyo Univ Sci Tokyo Japan Res Org Informat & Syst Natl Inst Informat Tokyo Japan

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Dense multi-view 3D displays appropriately combined with camera arrays may provide consistently augmented reality, where we enjoy a simple solution of occlusion caused by visual obstructions in the real world as if they were transparent for many users at the same time. In this paper, toward such multi-view systems electrically transmitting light fields beyond visual obstructions, we propose a novel method for real-time dense interpolation of entire 4D light fields acquired by sparse viewpoints. First, we discuss appearances of our proposed multi-view system, while examining how dense 4D light fields should be. Then, we propose a novel method of real-time 4D light field interpolation to develop an inexpensive system by reducing the number of image sensors to acquire 4D light fields. By introducing simple depth estimation from equally-spaced multi-view images without severe increase of computational cost, the novel method significantly improves reconstruction quality of the previously proposed one, that can only work for almost constant depths. Finally, experimental results show that our practical implementation on a GPGPU achieves real-time interpolation of entire 4D light fields while obtaining sufficient quality of reconstructed images. It is even comparable to that of various conventional methods for view interpolation, which depend on high computational cost not suitable for our real-time applications.

关键词： image sensors Interpolation Visualization Three-dimensional displays Information processing Light fields Real-time systems

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OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETECTION USING CHEST x-RAY imageS 29

OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETEC...

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IEEE International conference on image processing (ICIP)

作者： Degerli, Aysen Kiranyaz, Serkan Chowdhury, Muhammad E. H. Gabbouj, Moncef Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland Qatar Univ Dept Elect Engn Doha Qatar

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest x-ray (CxR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CxRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CxR dataset: QaTa-COV19 with 121,378 CxRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65% among the state-of-the-art deep models with 98.09% precision.

关键词： SARS-CoV-2 COVID-19 Machine Learning Deep Learning

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