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A Raster Data Framework Based on distributed Heterogeneous Cluster

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第4期47卷 715-723页

作者： Bhushan, R. Phani Somayajulu, D. V. L. N. Venkatraman, S. Subramanyam, R. B. V. ADRIN Dept Space Hyderabad Telangana India NIT Dept Comp Sci & Engn Warangal Andhra Pradesh India

Advancements in satellite imaging and sensor technologies result in capturing of large amount of spatial data. Many parallel processing techniques based on data or control parallelism have been attempted during the past 2 decades to provide performance improvement in image processing applications such as urban sprawl, weather prediction and crop estimation. These techniques have used block-based distributed file processing or the more modern MapReduce-based programming for implementation which still have gaps between optimal and best processing in terms of resource scheduling, data distribution and ease of programming. In this paper, we present a layered framework for parallel data processing to improve storage, retrieval and processing performance of spatial data on an underlying distributed file system. The paper presents a data placement strategy across a distributed HDFS cluster in a way to optimize spatial data retrieval and processing. The presence of neighborhood pixels local to the processing node in a distributed environment reduces network latencies and improves the efficiency of applications such as object recognition, change detection and site selection. We evaluate the data placement strategy on a four-node HDFS cluster and show that it can deliver good performance benefits by way of reading blocks of data at almost 10-12 times the default, which contributes to the improvement in efficiency of the various applications that use region growing methods.

关键词： Big data Remote sensing Spatial MapReduce HDFS RecordReader Raster data Vector data Resource scheduler Retrieval Engine

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Seamless image mosaic for multi-viewpoint overlapping pictures

Seamless image mosaic for multi-viewpoint overlapping pictur...

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International Conference on parallel and distributed processing Techniques and Applications

作者： Liu, D He, XJ Dalian Maritime Univ Inst Naut Sci & Technol Dalian 116026 Peoples R China

ISBN: (纸本)1932415262

The mosaic method of the multi-viewpoint pictures provides a quick;practical means to obtain the multi-viewpoint panoramic image. It is a kind of image-based rendering methods, which joints the texture of real world images. In this paper an image geometrical mosaic model of the overlapping images is constructed, a smooth mosaic method is proposed, and an experimental result is given.

关键词： mosaic of multi-viewpoint image seamless image mosaic virtual environment image-based rendering

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parallel and distributed methods for image retrieval with dynamic feature extraction on cluster architectures

Parallel and distributed methods for image retrieval with dy...

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International Workshop on Database and Expert Systems Applications

作者： O. Kao Department of Computer Science Technical University of Clausthal-Zellerfeld Clausthal-Zellerfeld Germany

This paper presents techniques for parallel multimedia retrieval by considering an image database as an example. The discussed cluster architecture depicts one possible solution for the performance problem. The distribution of the image data over a large number of nodes enables a parallel processing of the compute intensive operations for dynamic image retrieval. Thus, the partitioning of the data and the applied strategies for workload balancing have a decisive impact on the performance, efficiency, and the usability of such image databases.

关键词： image retrieval Feature extraction image databases Information retrieval Multimedia databases Computer architecture Data mining Multimedia systems Art image processing

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A Fast Search Algorithm for SqueeSAR distributed Scatterers in the Problem of Calculating Displacement Velocities

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PROGRAMMING AND COMPUTER SOFTWARE 2021年第6期47卷 426-438页

作者： Popov, S. E. Potapov, V. P. Fed Res Ctr Informat & Computat Technol Pr Akad Lavrenteva 6 Novosibirsk 630090 Russia

This paper describes a software implementation of a fast distributed scatterer search algorithm for the problem of displacement velocity calculation based on the Apache Spark platform. A complete scheme for calculating displacement velocities by the persistent scatterer method is considered. The proposed algorithm is integrated into the scheme after the stage of subpixel-accuracy alignment of a stack of time-series images. The search for distributed scatterers is carried out independently in shift windows over the entire area of the image. The presence of distributed scatterers is determined based on the assumption that pairs of samples in the window, which are composed of vectors of complex pixel values in each of the N images, are homogeneous. This assumption stems from the fulfillment of the Kolmogorov-Smirnov criterion for each pair. Toestimate phases of homeogenic pixels, the maximization problem is solved. It is shown that the proposed algorithm is not iterative and can be implemented in the framework of the parallel computing paradigm. Toenable distributed in-memory processing of radar data arrays (from 60 images) across many physical nodes in a network environment, we use the Apache Spark parallel processing platform. In this case, the time it takes to find distributed scatterers is reduced by a factor of 10 on average as compared to a single-processor implementation of the algorithm. The comparative results of testing the computing system on a demo cluster are presented. The algorithm is implemented in Python with a detailed description of the objects and methods of the algorithm.

关键词： Computer Science general Software Engineering/Programming and Operating Systems Operating Systems Software Engineering Artificial Intelligence

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New accelerated graph-based method of image segmentation applying minimum spanning tree

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IET image processing 2014年第4期8卷 239-251页

作者： Fabijanska, Anna Goclawski, Jaroslaw Lodz Univ Technol Inst Appl Comp Sci Lodz Poland

In this paper problem of graph based image segmentation is considered. In particular, attention is paid to minimal spanning tree based algorithm proposed by Felzenszwalb and Huttenlocher (FH). Although the method yields high quality results for various classes of images, its application is limited mainly to off-line processing. Its due to the very long execution time of the FH method, which in the case of high resolution images, requires processing of millions of vertices and edges contained within the resulting graph. Therefore, some improvements to the FH method are proposed in this paper. The modifications aim at the reduction of algorithm execution time and the usage of computer host memory. These goals are achieved both by reducing the size of input image graph and by applying the methods of GPU parallel computing at initial stages of the algorithm. As the reduction of graph size is obtained by processing meta-pixels representing homogenous regions, the new method is most suitable for the segmentation of images including rare, structurally complex objects distributed over nonuniform background. Results obtained by the introduced approach are compared with the results of the original FH method and other popular graph-based approaches to image segmentation. The comparison includes both the accuracy of image segmentation and the execution time. Analysis of the results clearly shows, that the proposed approach in many cases can significantly accelerate segmentation process without a noticeable loss of image segmentation quality.

关键词： graph theory image segmentation trees (mathematics) image segmentation minimum tree accelerated graph-based method FH algorithm Felzenszwalb and Huttenlocher algorithm

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A new parallel vision environment in heterogeneous networked computing

A new parallel vision environment in heterogeneous networked...

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SPIE Conference on parallel and distributed methods for image processing II

作者： Kim, KN Choi, TS Ramakrishna, RS KJIST Dept Informat & Commun Kwangju South Korea

ISBN: (纸本)0819429074

Many vision tasks are very complex and computationally intensive. Rear time requirements further aggravate the situation. They usually involve both structured (low-level vision) and unstructured (high-level vision) computations. parallel approaches offer hope in this context. parallel approaches to vision tasks and scheduling schemes for their implementation receive special emphasis in this paper. Architectural issues are also addressed. The aim is to design algorithms which can be implemented on low cost heterogeneous networks running PVM. Issues connected with general purpose architectures also receive attention. The proposed ideas have been illustrated through a practical example (of eye location from an image sequence). Next generation multimedia environments are expected to routinely employ such high performance computing platforms.

关键词： computer vision software environment parallel computing heterogeneous networked computing PVM

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Organization of the computer means controlled by the image's parameters

Organization of the computer means controlled by the image's...

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parallel and distributed methods for image processing

作者： Perju, Veacheslav L. Perju, Veacheslav V. Department of Computer Science Technical University of Moldova Stefan Mare Av.168 Kishinau MD-2012 Moldova

A new concept of working out computer means, with the architecture controlled by the parameters of the images (CPI) is being suggested. The methods and processor structures of image parameters determination are presented. The architectures of general purpose multiprocessor computer systems CPI with fixed and reconfigurable structures were elaborated and investigated. The analytical estimation of the image processing time depending on the image parameters, characteristics of the processor conveyor and the image memory modules are presented. The methods of the system organization, which optimize the number of processors, are proposed. ©2005 Copyright SPIE - The International Society for Optical Engineering.

关键词： image processing

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Multiagent boundary detection system: a way to parallel image processing

Multiagent boundary detection system: a way to parallel imag...

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Conference on parallel and distributed methods for image processing

作者： Ballet, P Rodin, V Tisseau, J Ecole Nationale d'Ingenieurs de Brest (France)

ISBN: (纸本)0819425885

In this article, we would like to detect boundaries of objects with the help of a multiagent system made up of reactive agents. The reactivity being very important, the agents' behavior is very simple (perception-action) : they have the capacity, nevertheless, to adapt locally to what they consider their environment, that is to say the image. An agent can move and has its own position in its environment. The basic behavior for an agent consists of following the highest brightness gradient and inscribing its path, if estimating to be on an edge, in all the agents' shared memory. Its path thus corresponds to edges which are found in the image. Please note that, in order to be noise resistant, the path is actually stored in the shared memory only if it is long enough. The notion of shared memory is very important because it allows the interaction among agents and the coordination of their actions. The agents actually use already found edges for finding new ones or complete those already detected. We have tested this system on different grey scale images scenes, but as well on synthetic scenes allowing analysis of thus obtained results. The results are promising and especially fast. Our multiagent system has been tested on a single-processor computer, and it has been note that the number of agents in a simulation neither affects the quality of the result nor CPU time necessary for segmentation of a given scene. We think that this approach is original in its use of agents and may be used to implement parallel image processing by assigning, for instance, an agent to each processor.

关键词： Sensors Edge detection image processing image sensors Actuators Computer simulations Computing systems Environmental sensing image analysis image segmentation

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A Fast Robustness Quantification Method for Evaluating Typical Deep Learning Models by Generally image processing 18

A Fast Robustness Quantification Method for Evaluating Typic...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Li, Haocong Cheng, Yunjia Ren, Wei Zhu, Tianqing China Univ Geosci Sch Comp Sci Wuhan Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Chinese Acad Sci CAS Inst Informat Engn Key Lab Network Assessment Technol Beijing 100093 Peoples R China

ISBN: (纸本)9781665414852

Deep learning especially image recognition techniques have been extensively used in various applications such as unmanned driving. The robustness of deep learning models is of critical importance since fault image recognition results may result in serious incidents. In this paper, we propose a fast quantifying method by general image processing to evaluate the robustness of five typical deep learning models under the Keras framework (i.e., VGG16, InceptionV3, ResNet50, DenseNet, and MobileNet). We analyze six metrics in terms of accuracy, precision, recall, F1, recognition time, and impact factor. The evaluation data is publicly accessible image data sets from Kaggle. In our evaluation, the adversary samples are generated by generally image processing methods such as gray-scaling, color-reversing, and image-flipping, which is ordinary operations and easily launched. The different models over various image processing methods are evaluated and compared comprehensively. The evaluation results show that DenseNet performs best over three conditions such as baseline, gray-scaling and horizontal flipping. MobileNet costs the shortest delay in decision over all image processing methods. F1 score varies with different attack intensity. InceptionV3 presents overall robustness in most conditions.

关键词： robustness model evaluation deep learning image recognition

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Implementation of a parallel registration algorithm for registration of InSAR complex images

Implementation of a parallel registration algorithm for regi...

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Conference on image and Signal processing for Remote Sensing X

作者： Lu, LJ Liao, MS Zhang, L Wang, Y Lin, H Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)0819455202

Registration of two or more images of the same scene is an important procedure in InSAR image processing that seeks to extract differential phase information exactly between two images. Meanwhile, the efficiency for large volume data processing is also a key point in the operational InSAR data processing chain. In this paper, some conventional registration methods are analyzed in detail and the parallel algorithm for registration is investigated. Combining parallel computing model with the intrinsic properties of InSAR data, the authors puts forward an image parallel registration scheme over distributed cluster of PCs. The preliminary experiment will be implemented and the result demonstrates feasibility and effectiveness of the proposed scheme.

关键词： parallel processing parallel computing InSAR image registration image matching

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