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检索条件"任意字段=18th Image Processing: Algorithms and Systems Conference, IPAS 2020"

共 258 条记录，以下是221-230 订阅

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conference on Digital Avionics systems (DASC)

作者： M. Rosemblum B. Gothard J. Jaczkowski Center for Intelligent Syst. Sci. Applications Int. Corp. Englewood CO USA Center for Intelligent Systems Science Applications International Corporation Englewood CO USA U.S. Army Tank-Automotive and Armaments Command Warren MI USA

the authors have applied a systems philosophy to the computer vision problem, and they have designed a system called O-NAV (Object NAVigation) that can harness all of the computer vision technology to date, and combine these approaches into one integrated system. In O-NAV, no one sub-component bears the burden of the problem. In other words, it is not expected that algorithms alone will solve the computer vision problem. If we choose effective sensing that inherently performs some level of scene discrimination at the phenomenology level, algorithms will be handed a partially analyzed scene before they ever encounter the raw image data. the algorithms have been designed to exploit an optimized processing hardware infrastructure, to maximize computation for the "real-time" application of autonomous robot navigation.

关键词： Layout Navigation Computer vision Vehicles Humans Biology computing Robotics and automation Machine vision Eyes Algorithm design and analysis

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Performance Analysis of GAN Approaches in the Portable Chest X-Ray Synthetic image Generation for COVID-19 Screening 18th

Performance Analysis of GAN Approaches in the Portable Chest...

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18th International conference on Computer Aided systems theory, EUROCAST 2022

作者： Morís, Daniel Iglesias Gende, Mateo de Moura, Joaquim Novo, Jorge Ortega, Marcos Centro de Investigación CITIC Universidade da Coruña Campus de Elviña s/n A Coruña15071 Spain Universidade da Coruña Xubias de Arriba 84 A Coruña15006 Spain

ISBN: (纸本)9783031253119

COVID-19 mainly affects lung tissues, aspect that makes chest X-ray imaging useful to visualize this damage. In the context of the global pandemic, portable devices are advantageous for the daily practice. Furthermore, Computer-aided Diagnosis systems developed with Deep Learning algorithms can support the clinicians while making decisions. However, data scarcity is an issue that hinders this process. thus, in this work, we propose the performance analysis of 3 different state-of-the-art Generative Adversarial Networks (GAN) approaches that are used for synthetic image generation to improve the task of automatic COVID-19 screening using chest X-ray images provided by portable devices. Particularly, the results demonstrate a significant improvement in terms of accuracy, that raises 5.28% using the images generated by the best image translation model. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Improving parallel performance of ensemble learners for streaming data through data locality with mini-batching

Improving parallel performance of ensemble learners for stre...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Guilherme Cassales Heitor Gomes Albert Bifet Bernhard Pfahringer Hermes Senger Federal University of São Carlos Brazil University of Waikato New Zealand

ISBN: (纸本)9781728176505

Machine Learning techniques have been employed in virtually all domains in the past few years. New applications demand the ability to cope with dynamic environments like data streams with transient behavior. Such environments present new requirements like incrementally process incoming data instances in a single pass, under both memory and time constraints. Furthermore, prediction models often need to adapt to concept drifts observed in non-stationary data streams. Ensemble learning comprises a class of stream mining algorithms that achieved remarkable prediction performance in this scenario. Implemented as a set of (several) individual component classifiers whose predictions are combined to predict new incoming instances, ensembles are naturally amendable for task parallelism. Despite its relevance, an efficient implementation of ensemble algorithms is still challenging. For example, dynamic data structures used to model non-stationary data behavior and detect concept drifts cause inefficient memory usage patterns and poor cache memory performance in multi-core environments. In this paper, we propose a minibatching strategy which can significantly reduce cache misses and improve the performance of several ensemble algorithms for stream mining in multi-core environments. We assess our strategy on four different state-of-art ensemble algorithms applying four widely used machine learning benchmark datasets with varied characteristics. Results from two different hardware show speedups of up to 5X on 8-core processors with ensembles of 100 and 150 learners. the benefits come at the cost of changes in predictive performances.

关键词： Adaptation models Machine learning algorithms Multicore processing Heuristic algorithms Memory management Parallel processing Prediction algorithms

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Solar Powered Auto Irrigation Monitoring System with Plant Health Indication Using image processing

Solar Powered Auto Irrigation Monitoring System with Plant H...

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International conference on Open Source systems and Technologies (ICOSST)

作者： Maida Saleem Khadija Nawaz Shahroz Ali Hira Ali Jamal Dept. of Electrical Engineering University of Engineering and Technology Lahore RCET Campus Gujranwala Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

this project aims to develop a solar powered auto irrigation system that incorporates image processing techniques to monitor and maintain the health of the plants. the system utilizes solar energy to power the irrigation process, making it environmentally friendly and cost-effective. image processing algorithms are employed to analyze the visual characteristics of the plants and detect any signs of distress or disease. this information is then used to automatically adjust the irrigation schedule and provide necessary nutrients to ensure optimal plant health. Furthermore, the system integrates image processing techniques to analyze the health of the plants. images of the plants are captured using a camera module and processed to detect any signs of diseases or abnormalities. the system then provides real-time feedback on the plant health status. through the implementation of this project, the efficiency and effectiveness of irrigation systems can be improved, leading to better crop yields and reduced water usage.

关键词： Temperature sensors Training Irrigation Visualization image processing Valves Sensor systems Real-time systems Sensors Monitoring

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Handling Large-Scale SAR image Data on Network-based Compute systems using Divisible load paradigm

Handling Large-Scale SAR Image Data on Network-based Compute...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Gokul Madathupalyam Chinnappan Bharadwaj Veeravalli National University of Singapore

ISBN: (纸本)9781728176505

processing Synthetic Aperture Radar (SAR) image data on compute clusters is a challenging problem. One of the ways to handle such computationally intensive tasks is to design efficient load distribution strategies for a given compute infrastructure. In this paper, we adopt the Divisible Load paradigm to handle SAR image processing by using Multi-instalment scheduling (MIS) strategies that have conclusively proven to minimize the idle times. In this paper, we design, analyse, and evaluate a practically viable load distribution strategy, referred to as Multi-Instalment Scheduling with Results Retrieval (MIS-RR), by considering the communication and computational overheads. In addition, we consider the results retrieval phase as a part of our solution procedure. this makes the formulation complete as it takes into account of all real-world influencing parameters in modelling and this is a first-work-of-its-kind contribution in employing a periodic MIS strategy that includes results retrieval phase together with system overhead parameters. We present a detailed theoretical analysis followed by a rigorous performance evaluation that captures the behaviour of the strategy. We evaluate our strategy based on a number of influencing parameters such as network scalability, number of instalments, overheads and then attempt to identify the maximal number of processors to use for a given size of the load. this latter result is of practical importance for system administrators in optimizing the resources to be deployed for a given load.

关键词： Performance evaluation Program processors Distribution strategy Processor scheduling Scalability Computational modeling Radar polarimetry

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Number Recognition of Parts Book Schematics using Convolutional Recurrent Neural Network

Number Recognition of Parts Book Schematics using Convolutio...

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International conference on Control, Automation and systems

作者： Erdal Genc Hee Ran Shin Jang Sik Park Department of Electrical and Electronic Engineering Kyunsung University 48434. 309 Suyeong-ro Nam-gu Busan Republic of Korea

ISBN: (纸本)9781538670804;9788993215168

OCR (Optical Character Recognition) has been becoming a vital method to recognize digits, letters, symbols and so on. the main idea is basically the conversion of data files which consists of handwritten or machine-written digits or characters into a type to let the machine make edits and read. this way, it lets computers read articles or books. they can also read images and make the conversion to a text file by using OCR. there are two important benefits of OCR. First, is the enhancement of the device to operate more productively even if the number of employees is decreased. Secondly, is the increase in the efficiency of the storage. this paper compares two state-of-the-art OCR algorithms in a simulated environment by using modified dataset. Simulation results are shown in part 4.

关键词： OCR MLP Deep learning Computer vision RNN LSTM image processing

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Deep Learning based Face Recognition for Security Robot

Deep Learning based Face Recognition for Security Robot

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IEEE/ASME International conference on Mechtronic and Embedded systems and Applications, MESA

作者： Min-Fan Ricky Lee Yun-Min Huang Jia-Yang Sun Xue-Qin Chen Ting-Fu Huang National Taiwan University of Science and Technology Taipei Taiwan Center for Cyber-Physical System Innovation Taipei Taiwan

ISBN: (纸本)9781665455718

For indoor security robots, face recognition is an important ability. However, face recognition is suffered from the limitation by environment uncertainties, the factors including perceptual aliasing, occlusion, illumination changes and significant viewpoint changes. these uncertainties will affect the recognition accuracy and processing time, which will cause the security concerns. this paper proposes a convolutional neural networks-based face recognition system for the mobile robots to perform visual perception and control tasks. the trained model proposed in this paper (i.e., FaceNet) is compared and tested against two different algorithms, VGGNet and AlexNet. With image streaming, images are transferred to the cloud for GPU computing. In addition, the Cartographer SLAM algorithms is used for the indoor simultaneous localization and mapping. the experimental results show that the accuracy of proposed face recognition system under the conditions of four different illumination is 88%, which proves the feasibility of the method. through the cloud GPU, the local computation and processing time can be reduced. the established mobile robot system can perform the indoor navigating and simultaneous localization and mapping.

关键词： Simultaneous localization and mapping Uncertainty Navigation Face recognition Computational modeling Graphics processing units Lighting

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An Omnidirectional Stereo Vision System Using a Single Camera

An Omnidirectional Stereo Vision System Using a Single Camer...

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International conference on Pattern Recognition

作者： Sooyeong Yi N. Ahuja Division of Electronics and Information Engineering Chonbuk National University of Korea South Korea Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA

ISBN: (纸本)0769525210

We describe a new omnidirectional stereo imaging system that uses a concave lens and a convex mirror to produce a stereo pair of images on the sensor of a conventional camera. the light incident from a scene point is split and directed to the camera in two parts. One part reaches camera directly after reflection from the convex mirror and forms a single-viewpoint omnidirectional image. the second part is formed by passing a subbeam of the reflected light from the mirror through a concave lens and forms a displaced single viewpoint image where the disparity depends on the depth of the scene point. A closed-form expression for depth is derived. Since the optical components used are simple and commercially available, the resulting system is compact and inexpensive. this, and the simplicity of the required image processing algorithms, make the proposed system attractive for real-time applications, such as autonomous navigation and object manipulation. the experimental prototype we have built is described

关键词： Stereo vision Cameras Mirrors Lenses Layout Optical imaging image sensors Sensor phenomena and characterization Sensor systems Optical reflection

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Accelerating Cost Aggregation for Real-Time Stereo Matching

Accelerating Cost Aggregation for Real-Time Stereo Matching

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International conference on Parallel and Distributed systems (ICPADS)

作者： Jianbin Fang Ana Lucia Varbanescu Jie Shen Henk Sips Gorkem Saygili Laurens Van Der Maaten Parallel and Distributed Systems Group Pattern Recognition and Bioinformatics Group Delft University of Technology Delft The Netherlands

Real-time stereo matching, which is important in many applications like self-driving cars and 3-D scene reconstruction, requires large computation capability and high memory bandwidth. the most time-consuming part of stereo-matching algorithms is the aggregation of information (i.e. costs) over local image regions. In this paper, we present a generic representation and suitable implementations for three commonly used cost aggregators on many-core processors. We perform typical optimizations on the kernels, which leads to significant performance improvement (up to two orders of magnitude). Finally, we present a performance model for the three aggregators to predict the aggregation speed for a given pair of input images on a given architecture. Experimental results validate our model with an acceptable error margin (an average of 10.4%). We conclude that GPU-like many-cores are excellent platforms for accelerating stereo matching.

关键词： Kernel Optimization Memory management Acceleration Venus Graphics processing units image color analysis

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Structure Preserved Graph Reordering for Fast Graph processing Without the Pain

Structure Preserved Graph Reordering for Fast Graph Processi...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Baofu Huang Zhidan Liu Kaishun Wu College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781728176505

By optimizing the data layout ahead-of-time, graph reordering can effectively improve the memory access locality in graph processing. the reordered graphs derived by sophisticated graph reordering approaches can greatly speedup the executions of most graph algorithms, while they incur huge computation overheads. Although lightweight approaches indeed reduce the reordering costs, they cannot achieve the best speedup performances. this is because they merely operate vertices of high-degrees and inadvertently destroy the community structures hidden in the graph. In this paper, we thus propose Sorder to balance the speedup performance and the reordering overhead. Sorder achieves better locality by preserving structural properties of graphs. Specifically, it mainly exploits neighborhood relations to renumber vertices and preferentially reorders vertices of high-degree ahead of the other vertices. We further enhance the design with the hypernode concept, which gathers neighboring vertices of low-degree to form a virtual vertex. therefore, Sorder can consecutively rearrange more neighboring vertices, such that protecting the community structures. Extensive experiments with 5 representative graph algorithms and 7 real-world graphs demonstrate that Sorder can achieve comparable speedup performance as Gorder, the state-of-the-art approach, while significantly reducing the reordering overhead.

关键词： Pain High performance computing conferences Layout

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