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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是181-190 订阅

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A novel iris recognition system based on active contour

A novel iris recognition system based on active contour

引用

Iranian conference of Biomedical Engineering (ICBME)

作者： Seyyed Mohammad Sadegh Moosavi Seyed Mohammad Seyedzade Ahmad Ayatollahi Department of Electrical Engineering Iran University of Science and Technology Tehran Iran

image segmentation is a method which is widely used in image processing application, and it is one of the most difficult steps of the process. this matter becomes more significant when we try to use the algorithms presented in complicated systems, such as the biometric systems. Different methods of iris segmentation have been presented so far. In this article, a new algorithm has been proposed for iris segmentation. We tried to use the greedy snake algorithm for the iris segmentation process. the proposed algorithm was tested on the CASIA Iris image Database 1.0 and the obtained results showed that the proposed method has an acceptable accuracy.

关键词： Iris recognition Active contours image segmentation image edge detection Pixel Accuracy Feature extraction

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A Method for Quality Evaluation of Multi-exposure Fusion images with Multi-scale Gradient Magnitude 17th

A Method for Quality Evaluation of Multi-exposure Fusion Ima...

引用

17th Chinese Intelligent systems conference, CISC 2021

作者： Zhu, Zhiqin Liang, Huahua Li, Yuanyuan Qi, Guanqiu Automation College Chongqing University of Posts and Telecommunications Chongqing400065 China Computer Information Systems Department Buffalo State College BuffaloNY14222 United States

ISBN: (纸本)9789811663239

Multi-exposure fusion (MEF) combines multiple images with different exposures taken in the same scene into a more detailed image. Aiming at the problems of messy structure, brightness and color distortion of MEF images obtained by different fusion algorithms. the gradient is more sensitive to the distortion of the image structure, so we propose a full-reference quality evaluation method called the multi-scale gradient magnitude similarity quality evaluation method (MGMS). In a single scale, from the perspective of reference image fusion, a quality evaluation algorithm for multi-exposure fusion images based on gradient amplitude is obtained. the quality evaluation values of each scale are weighted and combined to obtain the final multi-exposure fusion image quality evaluation result. the experimental results show that the Pearson linear correlation coefficient (PLCC) and Spearman rank-order correlation coefficient (SROCC) of this method are about 0.8782 and 0.8670, respectively, which are better than the existing MEF image quality evaluation method, more in line with reality, and human visual perception. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image fusion

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the crane control systems: A survey

The crane control systems: A survey

引用

International conference on Methods and Models in Automation and Robotics (MMAR)

作者： Paweł Hyla Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland

the paper presents a survey of proposed in the scientific works, as well as applications implemented in industrial practice of anti-sway crane control systems based on the intelligent solution (fuzzy logic, neural network and genetic algorithm and hybrid combine solution. the other part of paper contain review of solution concern crane workspace visualization techniques with obstacle identification for non-collision payload trajectory path planning. Presented solution was base on the image processing techniques, especially base on the stereovision technique.

关键词： Cranes Control systems Payloads Genetic algorithms Fuzzy logic Machine vision Cameras

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P-AFLC: a parallel scalable fuzzy clustering algorithm

P-AFLC: a parallel scalable fuzzy clustering algorithm

引用

International conference on Pattern Recognition

作者： A. Petrosino M. Verde ICAR Italian National Research Council (CNR) Complesso University of Monte S. Angelo Napoli Italy

Clustering is the unsupervised classification of data items into homogeneous groups called clusters. Clustering algorithms are computationally intensive, particularly when they are used to analyze large amounts of data and this is the case in many pattern recognition, image analysis applications. A possible approach to reduce the processing time is based on the implementation of clustering algorithms on scalable parallel computers. this paper describes the design and implementation of P-AFLC, a parallel version of the adaptive fuzzy leader clustering system based upon the competitive learning model for determining optimal classes in large data sets. the system architecture, its implementation, and experimental performance results are reported, together with theoretical performance evaluation.

关键词： Clustering algorithms image analysis Pattern analysis Algorithm design and analysis Pattern recognition Application software Concurrent computing Fuzzy sets Fuzzy systems Computer architecture

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High-Dimensional Similarity Query processing for Data Science 21

High-Dimensional Similarity Query Processing for Data Scienc...

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27th ACM SIGKDD International conference on Knowledge Discovery and Data Mining (KDD)

作者： Qin, Jianbin Wang, Wei Xiao, Chuan Zhang, Ying Wang, Yaoshu Shenzhen Univ Shenzhen Guangdong Peoples R China Shenzhen Inst Comp Sci Shenzhen Guangdong Peoples R China Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China Osaka Univ Suita Osaka Japan Nagoya Univ Suita Osaka Japan Univ Technol Sydney Ultimo NSW Australia

ISBN: (纸本)9781450383325

Similarity query (a.k.a. nearest neighbor query) processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications (e.g., classification & regression, deduplication, image retrieval, and recommender systems). Recently, representation learning and auto-encoding methods as well as pre-trained models have gained popularity. they basically deal with dense high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. this tutorial aims to provide a comprehensive review of high-dimensional similarity query processing for data science. It introduces solutions from a variety of research communities, including data mining (DM), database (DB), machine learning (ML), computer vision (CV), natural language processing (NLP), and theoretical computer science (TCS), thereby highlighting the interplay between modern computer science and artificial intelligence technologies. We first discuss the importance of high-dimensional similarity query processing in data science applications, and then review query processing algorithms such as cover tree, locality sensitive hashing, product quantization, proximity graphs, as well as recent advancements such as learned indexes. We analyze their strengths and weaknesses and discuss the selection of algorithms in various application scenarios. Moreover, we consider the selectivity estimation of high-dimensional similarity queries, and show how researchers are bringing in state-of-the-art ML techniques to address this problem. We expect that this tutorial will provide an impetus towards new technologies for data science.

关键词： similarity query processing nearest neighbor similarity search high-dimensional data

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Scene-and-process-dependent spatial image quality metrics 17

Scene-and-process-dependent spatial image quality metrics

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17th image Quality and System Performance conference, IQSP 2020

作者： Fry, Edward W. S. Triantaphillidou, Sophie Jenkin, Robin B. Jacobson, Ralph E. Jarvis, John R. Faculty of Science and Technology University of Westminster United Kingdom NVIDIA Corporation 2788 San Tomas Expressway Santa ClaraCA95050 United States

Spatial image quality metrics designed for camera systems generally employ the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS) and a visual contrast detection model. Prior art indicates that scene-dependent characteristics of non-linear, content-aware image processing are unaccounted for by MTFs and NPSs measured by traditional methods. the authors present two novel metrics: the log Noise Equivalent Quanta (log NEQ) and Visual log NEQ. they both employ Scene-and-Process-Dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures, which account for signal transfer and noise scene dependency, respectively. the authors also investigate implementing contrast detection and discrimination models that account for scene-dependent visual masking. Also, three leading camera metrics are revised to use the above scene-dependent measures. All metrics are validated by examining correlations with the perceived quality of images produced by simulated camera pipelines. Metric accuracy improved consistently when the SPD-MTFs and SPD-NPSs were implemented. the novel metrics outperformed existing metrics of the same genre. © 2019 Society for Imaging Science and Technology.

关键词： Optical transfer function

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Optimized Multilevel threshold Selection Using Evolutionary Computing

Optimized Multilevel Threshold Selection Using Evolutionary ...

引用

International conference on Network-Based Information systems (NBIS)

作者： M. Sridevi C. Mala E. Sivasankar Ilsun You National Institute of Technology Calicut Calicut Kerala IN Korean Bible University Seoul KR

thresholding is the method used for segmenting an image to isolate regions of interest from the image. the result of segmentation mainly depends on the selection of proper threshold values and number of classes. this paper proposes a method for optimal selection of threshold values using Evolutionary computing. the proposed method decomposes the given image to reduce its size so that it can be processed faster using Genetic Algorithm. the resultant image is finally mapped onto the original image space. the efficiency of the proposed method is compared with the other multilevel thresholding techniques namely GA-Otsu and GA-Kapur with and without wavelets. From the experimental results, it is inferred that the proposed method takes less time for processing and provides better results compared to existing methods.

关键词： image segmentation Histograms Discrete wavelet transforms Linear programming Genetic algorithms Sociology

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Real-time canny edge detection parallel implementation for FPGAs

Real-time canny edge detection parallel implementation for F...

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IEEE International conference on Electronics, Circuits and systems (ICECS)

作者： Christos Gentsos Calliope-Louisa Sotiropoulou Spiridon Nikolaidis Nikolaos Vassiliadis Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece New Technology Park NCSR Demokritos Microgen Limited Athens Greece

Edge detection is one of the most fundamental algorithms in digital image processing. the Canny edge detector is the most implemented edge detection algorithm because of its ability to detect edges even in images that are intensely contaminated by noise. However, this is a time consuming algorithm and therefore its implementations are difficult to reach real time response speeds. Especially nowadays where the demand for high resolution image processing is constantly increasing, the need for fast and efficient edge detector implementations is ever so present. A new parallel Canny edge detector FPGA implementation is proposed in this paper to answer this demand. this design takes advantage of 4-pixel parallel computations to achieve high throughput without increasing the on-chip memory demands. Synthesis and simulation results are presented to prove the design's efficiency and high frames per second rate.

关键词： image edge detection Field programmable gate arrays image resolution Pollution measurement Biological system modeling Computational modeling Hysteresis

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Minimum Cost for Channels and Registers in Processor Arrays by Avoiding Redundancy

Minimum Cost for Channels and Registers in Processor Arrays ...

引用

International conference on Application Specific systems (ASAP), Architectures and Processors

作者： Sebastian Siegel Renate Merker Institute of Circuits and Systems Dresden University of Technology Dresden Germany

In the process of mapping compute-intensive algorithms onto arrays of processing elements (PEs) an efficient usage of channels between PEs and registers within PEs is crucial for achieving a significant algorithm acceleration. In this paper this problem is solved for algorithms represented as systems of uniform recurrence equations. We address an optimization problem in order to realize the algorithmic data dependencies within the processor array (PA) with minimum cost for channels and registers. there, we use a new mapping approach which allows a direct mapping of the algorithm onto the PA by a partitioning method. In contrast to existing approaches, we consider the issue of avoiding redundant usage of channels and registers, which can appear if one instance of a variable has to be transferred from a source PE to several sink PEs. Further, a solution of the optimization problem determines the schedule for the transfer of the variable instances in the channels and their storage in registers as well as the inner schedule for the operations in the PEs. We illustrate our method on the edge detection algorithm.

关键词： Registers Partitioning algorithms Difference equations Cost function image edge detection Parallel processing Algorithm design and analysis Circuits and systems Acceleration Topology

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Hybrid CPU-GPU Solver for Gradient Domain processing of Massive images

Hybrid CPU-GPU Solver for Gradient Domain Processing of Mass...

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International conference on Parallel and Distributed systems (ICPADS)

作者： Sujin Philip Brian Summa Valerio Pascucci Peer-Timo Bremer Scientific Computing and Imaging Institute Salt Lake USA Lawrence Livermore National Laboratory Livermore USA

Gradient domain processing is a computationally expensive image processing technique. Its use for processing massive images, giga or terapixels in size, can take several hours with serial techniques. To address this challenge, parallel algorithms are being developed to make this class of techniques applicable to the largest images available with running times that are more acceptable to the users. To this end we target the most ubiquitous form of computing power available today, which is small or medium scale clusters of commodity hardware. Such clusters are continuously increasing in scale, not only in the number of nodes, but also in the amount of parallelism available within each node in the form of multicore CPUs and GPUs. In this paper we present a hybrid parallel implementation of gradient domain processing for seamless stitching of gigapixel panoramas that utilizes MPI, threading and a CUDA based GPU component. We demonstrate the performance and scalability of our implementation by presenting results from two GPU clusters processing two large data sets.

关键词： Tiles Kernel Graphics processing unit Instruction sets Random access memory image color analysis Scalability

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