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A Multi-granularity parallel Model for Unified Remote Sensing image processing WebServices

TRANSACTIONS IN GIS 2012年第6期16卷 845-866页

作者： Guo, Wei Zhu, Xinyan Hu, Tao Fan, LiWei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R LIESMARS Wuhan 430079 Peoples R China

The growth of the Web has resulted in the Web-based sharing of distributed geospatial data and computational resources. The Geospatial processing Web (GeoPW) described here is a set of services that provide a wide array of geo-processing utilities over the Web and make geo-processing functionalities easily accessible to users. High-performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high-performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi-granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI-oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub-models. In addition, interfaces, definitions, parallel task scheduling and fault-tolerance mechanisms are described in detail. Based on the model and methods, we propose an open-source online platform named OpenRS-Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi-granularity parallel model for unified remote sensing image processing web services.

关键词： REMOTE-sensing images image processing WEB services parallel programs (Computer programs) GEOSPATIAL data GEOGRAPHIC information systems

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DECOMPOSITION BY PARTIAL LINEARIZATION IN MULTIUSER SYSTEMS

DECOMPOSITION BY PARTIAL LINEARIZATION IN MULTIUSER SYSTEMS

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IEEE International Conference on Acoustics, Speech, and Signal processing

作者： G. Scutari F. Facchinei P. Song D. P. Palomar J.-S. Pang Dpt. of Electrical Eng. State University of New York (SUNY) at Buffalo Dpt. of Computer Control and Management Eng. University of Rome "La Sapienza" Dpt. of Electronic and Computer Eng. Hong Kong Univ. of Science and Technology Dpt. of Industrial and Enterprise Systems Eng. University of Illinois

ISBN: (纸本)9781479903573

We propose a decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising in the design of wireless multi-user interfering systems. Our main contributions are: i) the development of the first provably convergent Jacobi best-response algorithm, where all users simultaneously solve a suitably convexified version of the original sum-utility optimization problem;ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics;and iii) a framework that can be easily particularized to well-known applications, giving rise to practical algorithms that outperform all existing ad-hoc methods proposed for very specific problems. Our framework contains as special cases well-known gradient algorithms for nonconvex sum-utility problems, and many block-coordinate descents schemes for convex functions.

关键词： Nonconvex social problems parallel & distributed optimization Successive convex approximation decomposition Linearization Frameworks Partial Heuristics Pricing algorithms descent

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High-performance computing in image registration

High-performance computing in image registration

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Conference on High-Performance Computing in Remote Sensing ii

作者： Zanin, Michele Remondino, Fabio Dalla Mura, Mauro FBK Via Sommar 18 I-38123 Trento Italy Grenoble Inst Technol GIPSA Iab Grenoble France

ISBN: (纸本)9780819492791

Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high performance computing techniques in order to deliver timely responses e. g. for rapid decisions or real-time actions. Thus, parallel or distributed computing methods, Digital Signal Processor (DSP) architectures, Graphical processing Unit (GPU) programming and Field-Programmable Gate Array (FPGA) devices have become essential tools for the challenging issue of processing large amount of geo-data. The article focuses on the processing and registration of large datasets of terrestrial and aerial images for 3D reconstruction, diagnostic purposes and monitoring of the environment. For the image alignment procedure, sets of corresponding feature points need to be automatically extracted in order to successively compute the geometric transformation that aligns the data. The feature extraction and matching are ones of the most computationally demanding operations in the processing chain thus, a great degree of automation and speed is mandatory. The details of the implemented operations (named LARES) exploiting parallel architectures and GPU are thus presented. The innovative aspects of the implementation are (i) the effectiveness on a large variety of unorganized and complex datasets, (ii) capability to work with high-resolution images and (iii) the speed of the computations. Examples and comparisons with standard CPU processing are also reported and commented.

关键词： High Performance Computing Graphical processing Unit photogrammetry image registration

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Orthogonal Nonnegative Locally Linear Embedding

Orthogonal Nonnegative Locally Linear Embedding

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lei Wei Naiyang Guan Xiang Zhang Zhigang Luo Dacheng Tao National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China These authors contributed equally to this paper National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China Centre for Quantum Computation and Intelligence Systems and Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479906505

Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance between X and WH. NMF has been widely used in pattern recognition, data mining and computer vision because the non-negativity constraints on both W and H usually yield intuitive parts-based representation. However, NMF suffers from two problems: 1) it ignores geometric structure of dataset, and 2) it does not explicitly guarantee partsbased representation on any datasets. In this paper, we propose an orthogonal nonnegative locally linear embedding (ONLLE) method to overcome aforementioned problems. ONLLE assumes that each example embeds in its nearest neighbors and keeps such relationship in the learned subspace to preserve geometric structure of a dataset. For the purpose of learning parts-based representation, ONLLE explicitly incorporates an orthogonality constraint on the learned basis to keep its spatial locality. To optimize ONLLE, we applied an efficient fast gradient descent (FGD) method on Stiefel manifold which accelerates the popular multiplicative update rule (MUR). The experimental results on real-world datasets show that FGD converges much faster than MUR. To evaluate the effectiveness of ONLLE, we conduct both face recognition and image clustering on real-world datasets by comparing with the representative NMF methods.

关键词： Nonnegative matrix factorization Locally linear embedding Fast gradient descent Stiefel manifold Dataset Nonnegative matrix decomposition geometrical construction NMF protocol images embedding Flue gas desulfurization Nearest neighbor Orthogonal

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image Noise Removal using image Inpainting

Image Noise Removal using Image Inpainting

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Conference on image processing - Algorithms and Systems X and parallel processing for Imaging Applications ii

作者： Bakhtiari, Somayeh Mohyedinbonab, Elmira Agaian, Sos Jamshidi, Mo Univ Texas San Antonio Dept Elect Eng San Antonio TX 78249 USA

ISBN: (纸本)9780819489425

In this paper, new methods are addressed for impulse and speckle noise removal in images. The approach is based on the fusion of noise detection and image inpainting techniques. To avoid destroying the real structures of the image, the noise areas are first recognized to be repaired by an inpainting algorithm, subsequently. To distinguish the impulse noise areas in the image, a Neuro-Fuzzy model is employed and, to extract the speckled regions an algorithm is proposed based on Frost filtering and image resizing. The advantage of inpainting technique over the regular filtering methods is that it will be easier to generalize to all types of noise. Once we detect the damaged pixels in the image, the inpainting algorithm will be able to repair them. Various types of images under three levels of noise are tested using PSNR and SSIM measures. The experimental results demonstrate the great ability of the new approaches to suppress the noise properly, while preserving critical details of the image.

关键词： image denoising Impulse noise Speckle noise Neuro-Fuzzy prediction Inpainting Frost filter

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Optimal Fractional Filter for image segmentation

Optimal Fractional Filter for image segmentation

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Conference on image processing - Algorithms and Systems X and parallel processing for Imaging Applications ii

作者： Nakib, A. Schulze, Y. Petit, E. Univ Paris Est Creteil LISSI EA 3956 F-94010 Creteil France

ISBN: (纸本)9780819489425

In this paper, we present a new image thresholding algorithm based on fractional filter (FF). Our experiments showed that a good segmentation result corresponds to an optimal order of the filter. Then, we propose to use geometric moments to find the optimal order. The proposed algorithm, called FLM, allows including contextual information such as the global object shape and uses the properties of the two-dimensional fractional integration. The efficiency of FLM was illustrated by the comparison to other six competing methods recently published and it was tested on real-world problem.

关键词： image filtering image thresholding fractional differentiation Legendre moments' Brain MRI

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PQH: A Multithreaded parallel NN Search Index for Content-based image Retrieval

PQH: A Multithreaded Parallel NN Search Index for Content-ba...

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26th IEEE International parallel and distributed processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Chen, Hui-zhong Jing, Ning Chen, Yong-guang Chen, Luo Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China Ordnance Engn Coll Shijiazhuang 050003 Peoples R China

ISBN: (纸本)9780769546766

In this paper, we propose a NN search index method called parallel Quantized Hashing (PQH) for relevant retrieval in Content-Based image Retrieval (CBIR) systems. Aiming at improving the performance of query, the PQH partitions the original dataset into several subsets and keeps the pointers to quantized vectors of each subset in a hashing like structure. It only needs to filter parts of the query space to get candidates via neighboring masks and accesses the corresponding real vectors for precise calculation. The neighboring masks are sets of mask vectors indicating cells close to the query point using different metrics. The PQH is multithreaded designed so that the construction and the query can be run in parallel upon multi-core processors. Our extensive experiments confirm that the sequential PQH is of high query efficiency and outperforms the existing methods such as VA+-file and LSH. The parallel run PQH achieves even higher performance and returns appropriate NN results. And more important it is simple for practical implement in real applications.

关键词： kNN multithreaded parallelism CBIR

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Texture and color descriptors as a tool for context-aware patch-based image inpainting

Texture and color descriptors as a tool for context-aware pa...

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Conference on image processing - Algorithms and Systems X and parallel processing for Imaging Applications ii

作者： Ruzic, Tijana Pizurica, Aleksandra Univ Ghent TELIN IPI IBBT Ghent Belgium

ISBN: (纸本)9780819489425

State-of-the-art results in image inpainting are obtained with patch-based methods that fill in the missing region patch-by-patch by searching for similar patches in the known region and placing them at corresponding locations. In this paper, we introduce a context-aware patch-based inpainting method, where the context is represented by texture and color features of a block surrounding the patch to be filled in. We use this context to recognize other blocks in the image that have similar features and then we constrain the search for similar patches within them. Such an approach guides the search process towards less ambiguous matching candidates, while also speeding up the algorithm. Experimental results demonstrate benefits of the proposed context-aware approach, both in terms of inpainting quality and computation time.

关键词： inpainting patch-based algorithms Gabor filters

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Application of 1D FIR filter methods to 3D polygonal meshes

Application of 1D FIR filter methods to 3D polygonal meshes

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Conference on image processing - Algorithms and Systems X and parallel processing for Imaging Applications ii

作者： Ward, William S. Univ Texas San Antonio Dept Elect Engn San Antonio TX 78249 USA

ISBN: (纸本)9780819489425

This paper discusses a procedure of filtering a three dimensional (3-D) polygonal mesh by utilizing the basic methods of finite impulse response (FIR), one dimensional (1-D) filtering. A method of linearizing a 3-D mesh was developed in order to apply the 1-D filter methods. With the development of low cost 3-D scanners, which physically scan and digitize real-world objects, the amount of "noise" that is found on these models has increased. This noise can come from various sources, including unwanted imperfections in the object itself, and from the device being used to scan the object. This newly developed filtering method will provide not only a way to decrease the noise in models, but increase details of the model as well, using low-pass and high-pass filters respectively.

关键词： 3-D filtering FIR mesh linearization model denoise vertex

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parallel processing Architecture for H.264 Deblocking Filter on Multi-core Platforms

Parallel Processing Architecture for H.264 Deblocking Filter...

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Conference on image processing - Algorithms and Systems X and parallel processing for Imaging Applications ii

作者： Prasad, Durga P. Sonachalam, Sekar Kunchamwar, Mangesh K. Gunupudi, Nageswara Rao Parallel Prisms (United States)

ISBN: (纸本)9780819489425

Massively parallel computing (multi-core) chips offer outstanding new solutions that satisfy the increasing demand for high resolution and high quality video compression technologies such as H.264. Such solutions not only provide exceptional quality but also efficiency, low power, and low latency, previously unattainable in software based designs. While custom hardware and Application Specific Integrated Circuit (ASIC) technologies may achieve low-latency, low power, and real-time performance in some consumer devices, many applications require a flexible and scalable software-defined solution. The deblocking filter in H.264 encoder/decoder poses difficult implementation challenges because of heavy data dependencies and the conditional nature of the computations. Deblocking filter implementations tend to be fixed and difficult to reconfigure for different needs. The ability to scale up for higher quality requirements such as 10-bit pixel depth or a 4:2:2 chroma format often reduces the throughput of a parallel architecture designed for lower feature set. A scalable architecture for deblocking filtering, created with a massively parallel processor based solution, means that the same encoder or decoder will be deployed in a variety of applications, at different video resolutions, for different power requirements, and at higher bit-depths and better color sub sampling patterns like YUV, 4:2:2, or 4:4:4 formats. Low power, software-defined encoders/decoders may be implemented using a massively parallel processor array, like that found in HyperX technology, with 100 or more cores and distributed memory. The large number of processor elements allows the silicon device to operate more efficiently than conventional DSP or CPU technology. This software programing model for massively parallel processors offers a flexible implementation and a power efficiency close to that of ASIC solutions. This work describes a scalable parallel architecture for an H.264 compliant deblocking

关键词： deblocking filter parallel processing multi core processors dataflow H.264 scalable software architecture

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