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5th International Conference in image processing and Communications, IP&C 2013

ISBN: (纸本)9783319016214

The proceedings contain 52 papers. The special focus in this conference is on image processing and Communications. The topics include: Adaptive windowed threshold for box counting algorithm in cytoscreening applications;corneal endothelial grid structure factor based on coefficient of variation of the cell sides lengths;a distributed approach for development of deformable model-based segmentation methods;enhancement of low-dose CT brain scans using graph-based anisotropic interpolation;using of EM algorithm to image reconstruction problem with tomography noises;the perception of humanoid robot by human;a fast histogram estimation based on the Monte Carlo method for image binarization;adaptation of the combined image similarity index for video sequences;time-frequency analysis of image based on stockwell transform;2DKLT-based color image watermarking;knowledge transformations applied in image classification task;problems of infrared and visible-light images automatic registration;semantics driven table understanding in born-digital documents;contextual possibilistic knowledge diffusion for images classification;image feature extraction using compressive sensing;a modification of the parallel spline interpolation algorithms;local Eigen background substraction;polarization imaging in 3D shape reconstrucion;layer image components geometry optimization;extraction of data from limnigraf chart images;a simplified visual cortex model for efficient image codding and object recognition;fuzzy rule-based systems for optimizing power consumption in data centers;an algorithm for finding shortest path tree using ant colony optimization metaheuristic and a sensor network gateway for IP network and SQL database.

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The challenges of image segmentation in big remotely sensed imagery data

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Annals of GIS 2014年第4期20卷 233-244页

作者： Xing, Jin Sieber, Renée Kalacska, Margaret Department of Geogrpahy McGill University Burnside Hall Building 805 Sherbrooke Street West Montreal H3A 0B9 QC Canada

With the increase in spatial, spectral and temporal resolutions of Earth observing systems, geospatial and remote sensing (RS) image research is shifting towards a big data paradigm. One of the most important challenges in RS big data is image segmentation, which is defined as a process to group pixels together by a predefined criteria. image segmentation allows for the extraction of features such as roads or habitats or buildings. image segmentation is rendered more difficult with big data because the computing power on single platforms cannot keep pace with the size and velocity of new data. Big data sets must be decomposed for the analysis in distributed and parallel computing platforms. Decomposition through techniques like slicing by spatial extent obscures the geometric and topological information in geospatial data, for example generating fake artefacts. To address these challenges, we propose a geospatial cyberinfrastructure (GCI) that coordinates cloud computing, MapReduce framework, image segmentation algorithms, a spatial extent splitting method and a recomposing technique using moving window. This GCI is evaluated on cloud computing to identify features in a 312.07 GB high-resolution colour aerial photo with Hadoop. K-means-based image segmentation is selected as the case study. We deploy the architecture in private cloud and public cloud implementation, respectively. The results demonstrate the benefits of the decomposing and recomposing methods in segmenting images, removing fake artefacts and reducing information distortion. More general problems in big data are revealed, among them I/O problems, particularly in the amount of preprocessing and post-processing that will be required in any analysis of big imagery data. We conclude with implications for scalability and suggestions to speed up decomposition and recomposition. © 2014, © 2014 Taylor & Francis.

关键词： big data cloud computing decomposition geospatial cyberinfrastructure image segmentation MapReduce recomposition spatial feature extraction

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parallel implementation of a X-ray tomography reconstruction algorithm for high-resolution studies

Parallel implementation of a X-ray tomography reconstruction...

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13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013

作者： Garcia, J. Abella, M. de Molina, C. Liria, E. Isaila, F. Carretero, J. Desco, M. Computer Architecture and Communication Area University Carlos III Madrid Spain Bioengineering and Aerospace Engineering Dept. University Carlos III Madrid Spain Madrid Spain Madrid Spain

ISBN: (纸本)9783319008455

Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flatpanel detector orbiting in a circular trajectory. image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp, Davis and Kress (FDK). Currently there is a strong need to speed-up the reconstruction of CT data in order to extend its clinical applications. The evolution of the semiconductor detector panels has resulted in an increase of detector elements density, which produces a higher amount of data to process. This work focuses on both standard and future high-resolution studies, in which multiple level of parallelism will be needed in the reconstruction process. In addition, this paper addresses the future challenges of processing high-resolution images in many-core and distributed architectures. © Springer International Publishing Switzerland 2014.

关键词： parallel architectures

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A Rapid Fluorescence Lifetime image Acquistion Method Based on Time-gated Fluorescence Lifetime Imaging Microscopy 2

A Rapid Fluorescence Lifetime Image Acquistion Method Based ...

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2nd International Conference on Systems and Informatics (ICSAI)

作者： Liu, Yang Zhou, Yan Liu, Yuliang Chinese Acad Sci Inst Semicond Optoelect Syst Lab Beijing Peoples R China

ISBN: (纸本)9781479954582

Besides the regular biological or biomedical imaging applications, time-gated fluorescence lifetime imaging microscopy (FLIM) could be used to image living cells or rapid biochemistry reactions by its advantage of wide field and obvious parallel acquisition speed. As the basis of system detection mode, a good fitting algorithm could satisfy the lifetime precision and acquisition demand at the same time. The common used fluorescence lifetime fitting methods include rapid fluorescence lifetime determination(RLD), linear least square method (LLS) and iterative nonlinear least square (NLLS) method. In this study, we compared these three methods and analyzed the lifetime results. It demonstrated the RLD algorithm could achieve a fast processing speed and a good contrast of lifetime map, however, it shows the results of the rapid fitted lifetime map exists some distributed noise caused by insufficient time-resolved fluorescence intensity images. To remove these noise spots and preserve the lifetime map precision, we chose a four-order partial differential equations (PDEs), which achieved a trade-off between noise-removing and lifetime map feature. In this paper, we proposed a rapid fluorescence lifetime acquisition method combining rapid lifetime determination and four-order PDEs algorithm based on time-gated FLIM, which could satisfy the high speed detection and lifetime precision demand of living cells or rapid biochemical reactions.

关键词： time-gated FLIM rapid fitting method noise removal fourth -order PDE nonlinear diffusion

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Advanced image processing methods as a tool to map and quantify different types of biological soil crust

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2014年 90卷 59-67页

作者： Rodriguez-Caballero, Emilio Escribano, Paula Canton, Yolanda Univ Almeria Dept Agron Almeria 04120 Spain Estn Expt Zonas Aridas Almeria 04120 Spain

Biological soil crusts (BSCs) modify numerous soil surface properties and affect many key ecosystem processes. As BSCs are considered one of the most important components of semiarid ecosystems, accurate characterisation of their spatial distribution is increasingly in demand. This paper describes a novel methodology for identifying the areas dominated by different types of BSCs and quantifying their relative cover at subpixel scale in a semiarid ecosystem of SE Spain. The approach consists of two consecutive steps: (i) First, Support Vector Machine (SVM) classification to identify the main ground units, dominated by homogenous surface cover (bare soil, cyanobacteria BSC, lichen BSC, green and dry vegetation), which are of strong ecological relevance. (ii) Spectral mixture analysis (SMA) of the ground units to quantify the proportion of each type of surface cover within each pixel, to correctly characterize the complex spatial heterogeneity inherent to semiarid ecosystems. SVM classification showed very good results with a Kappa coefficient of 0.93%, discriminating among areas dominated by bare soil, cyanobacteria BSC, lichen BSC, green and dry vegetation. Subpixel relative abundance images achieved relatively high accuracy for both types of BSCs (about 80%), whereas general overestimation of vegetation was observed. Our results open the possibility of introducing the effect of presence and of relative cover of BSCs in spatially distributed hydrological and ecological models, and assessment and monitoring aimed at reducing degradation in these areas. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： Biological soil crust mapping Surface cover quantification Hyperspectral imagery Dryland Multiple endmember spectral mixture analysis (MESMA)

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Approximate Maximum Common Sub-graph Isomorphism Based on Discrete-Time Quantum Walk 22

Approximate Maximum Common Sub-graph Isomorphism Based on Di...

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22nd International Conference on Pattern Recognition (ICPR)

作者： Lu, Kai Zhang, Yi Xu, Kai Gao, Yinghui Wilson, Richard Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China Natl Univ Def Technol Coll Elect Sci & Engn Changsha Hunan Peoples R China Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

ISBN: (纸本)9781479952083

Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The problem has found application in many areas where the similarity of graphs is important, for example in scene matching, video indexing, chemical similarity and shape analysis. In this paper, a novel algorithm Qwalk is proposed for approximate MCS, utilizing the discrete-time quantum walk. Based on the new observation that isomorphic neighborhood group matches can be detected quickly and conveniently by the destructive interference of a quantum walk, the new algorithm locates an approximate solution via merging neighborhood groups. Experiments show that Qwalk has better accuracy, universality and robustness compared with the state-of-the-art approximate MCS methods. Meanwhile, Qwalk is a general algorithm to solve the MCS problem approximately while having modest time complexity.

关键词： discrete-time quantum walk maximum common sub-graph quantum interference

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Combination feature for image retrieval in the distributed datacenter

Combination feature for image retrieval in the distributed d...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Di Yang Jianxin Liao Qi Qi Jingyu Wang Haifeng Sun Shan Jiang State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479976164

Since the emergence of cloud datacenters provides an enormous amount of resources easily accessible to people, it is challenging to provide an efficient search framework in such a distributed environment. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These methods are insufficient to meet requirements of content based image retrieval (CBIR) and more powerful search frameworks are needed. In this paper, we present LCFIR, an effective image retrieval framework for fast content location in the distributed situation. It adopts the peer-to-peer paradigm and combines color and edge features. The basic idea is to construct multiple replicas of an image's index through exploiting the property of Locality Sensitive Hashing (LSH). Thus, the indexes of similar images are probabilistically gathered into the same node without the knowledge of any global information. The empirical results show that the system is able to yield high accuracy with load balancing, and only contacts a few number of the participating nodes.

关键词： Indexes Peer-to-peer computing image retrieval image color analysis Feature extraction Query processing image edge detection

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Complex Network Analysis Using parallel Approximate Motif Counting

Complex Network Analysis Using Parallel Approximate Motif Co...

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International Symposium on parallel and distributed processing (IPDPS)

作者： George M. Slota Kamesh Madduri Department of Computer Science and Engineering The Pennsylvania State University University Park PA USA

Subgraph counting forms the basis of many complex network analysis metrics, including motif and anti-motif finding, relative graph let frequency distance, and graph let degree distribution agreements. Determining exact subgraph counts is computationally very expensive. In recent work, we present FASCIA, a shared-memory parallel algorithm and implementation for approximate subgraph counting. FASCIA uses a dynamic programming-based approach and is significantly faster than exhaustive enumeration, while generating high-quality approximations of subgraph counts. However, the memory usage of the dynamic programming step prohibits us from applying FASCIA to very large graphs. In this paper, we introduce a distributed-memory parallelization of FASCIA by partitioning the graph and the dynamic programming table. We discuss a new collective communication scheme to make the dynamic programming step memory-efficient. These optimizations enable scaling to much larger networks than before. We also present a simple parallelization strategy for distributed subgraph counting on smaller networks. The new additions let us use subgraph counts as graph signatures for a large network collection, and we analyze this collection using various subgraph count-based graph analytics.

关键词： Fascia Approximation methods Dynamic programming Heuristic algorithms Peer-to-peer computing Color image color analysis

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Post processing for dense stereo matching by iterative local plane fitting

Post processing for dense stereo matching by iterative local...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Hongbo Lu Haibo Meng Kun Du Yuan Sun Yuanchao Xu Zhimin Zhang University of Chinese Academy of Sciences Beijing China ISKL Computer Architecture ICT CAS Beijing China Engineering Research Center of WCD NJUPT Nanjing China

Disparity refinement is an essential step of local stereo matching methods to produce fine dense disparity maps. The inherent defect of local stereo methods results in erroneous disparity in occluded areas. In this paper, we present a novel post processing method which can effectively improve the accuracy of dense disparity maps by rectifying disparity errors iteratively. Invalid disparities are first detected by left-right consistency check and color-disparity consistency check. For each invalid pixel, supports from valid pixels in the neighborhood are collected to determine the plane parameters of the local window. An iterative strategy is adopted to gradually propagate disparity information from valid pixels to invalid areas. We apply the proposed method to disparity maps produced by two recent stereo matching methods, and compare the refining results with other post processing methods. Experimental results show the effectiveness of our method in improving dense disparity maps.

关键词： Venus image color analysis Stereo vision Error analysis Fitting Reliability Proposals

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HNCP-ii: A 16-core 65nm microprocessor ASIC for image processing algorithms

HNCP-II: A 16-core 65nm microprocessor ASIC for image proces...

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International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC)

作者： Mohamed Amine Boussadi Thierry Tixier Alexis Landrault Jean-Pierre Derutin Institut Pascal - UMR 6602 CNRS / UBP / IFMA 24 Avenue des Landais 63171 Aubiere France

In many video and image processing applications, complexity has reached a point where the performance requirements can no longer be supported by standard architectures based on a single processor. This is why many systems are based on multiprocessor architecture. In this paper, we present HNCP-ii a 16-core flexible distributed memory ASIC dedicated to embedded application field (image processing applications). Connected through a torus on-chip network, each tile of the overall system includes an open-source RISC processor, a floating point unit, a video management module and a set of built in hardware features for multicore communication. This specific hardware enables to meet most relevant overall parallelism scheme (parallel skeletons) thanks to software-configurable modules. Results are presented for two image processing algorithms in term of execution time, speedup and efficiency. Area, power and timing performance of the synthesized ASIC are given for a CMOS 65nm technology node.

关键词： Computer architecture Skeleton Application specific integrated circuits Hardware Field programmable gate arrays Clocks Topology

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