A Distributed Image Processing Function Set for an Image Mining System

An Image Mining System (IMS) requires real time processing often using special purpose hardware. The work herein presented refers to the application of cluster computing for on line image processing inside an IMS, where the end user benefits from the operation on data with a high degree locality and parallelism. The virtual parallel computer is composed by a cluster of personal computers connected by a low cost network. The aim is to minimise the processing time of a high level image processing package. The image processing function set developed to manage the parallel execution is described and some results obtained from the parallelisation of image processing algorithms are discussed.

[1]  Zhiyi Yang,et al.  Parallel Image Processing Based on CUDA , 2008, 2008 International Conference on Computer Science and Software Engineering.

[2]  José Crespo,et al.  Theoretical aspects of morphological filters by reconstruction , 1995, Signal Process..

[3]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[4]  Beng Chin Ooi,et al.  An Evaluation of Color-Spatial Retrieval Techniques for Large Image Databases , 2001, Multimedia Tools and Applications.

[5]  William K. Pratt,et al.  Digital image processing, 2nd Edition , 1991, A Wiley-Interscience publication.

[6]  David B. Skillicorn,et al.  Strategies for parallel data mining , 1999, IEEE Concurr..

[7]  Jacqueline Fernández,et al.  Towards a parallel image mining system , 2007 .

[8]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  Jorge G. Barbosa,et al.  Parallel Image Processing System on a Cluster of Personal Computers (Best Student Paper Award: First Prize) , 2000, VECPAR.

[10]  Sanjay Ranka,et al.  Parallel Processing for Computer Vision and Image Understanding - Guest Editors' Introduction to the Special Issue , 1992, Computer.

[11]  William Nick Street,et al.  Automatic Feature Mining for Personalized Digital Image Retrieval , 2001, MDM/KDD.

[12]  Bernd Jähne,et al.  Digital Image Processing: Concepts, Algorithms, and Scientific Applications , 1991 .

[13]  Steven K. Feiner,et al.  Computer graphics: principles and practice (2nd ed.) , 1990 .

[14]  Fillia Makedon,et al.  Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram , 2004, JCDL.

[15]  Selim Aksoy,et al.  Interactive training of advanced classifiers for mining remote sensing image archives , 2004, KDD.

[16]  Keiji Yanai,et al.  Generic image classification using visual knowledge on the web , 2003, ACM Multimedia.

[17]  Henryk Krawczyk,et al.  A parallel environment for image data mining , 2002, Proceedings. International Conference on Parallel Computing in Electrical Engineering.

[18]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[19]  Roman M. Palenichka,et al.  Effective image and video mining: an overview of model-based approaches , 2005, MDM '05.

[20]  D. Hubel,et al.  Segregation of form, color, movement, and depth: anatomy, physiology, and perception. , 1988, Science.

[21]  Mikhail A. Vorontsov.  Parallel image processing based on an evolution equation with anisotropic gain: integrated optoelectronic architectures , 1999 .