Application of the Fundamental Solution Method to object recognition in the pictures

Tomasz Klekiel

doi:10.18495/comengapp.v7i1.224

[1] D. J. Kang, I. S. Kweon, An edge-based algorithm for discontinuity adaptive color image smoothing, Pattern Recognition 34 (2001) 333-342
[2] J. Hornegger, H. Niemann, R. Risack, Appearance-based object recognition using optimal feature transforms, Pattern Recognition 33 (2000) 209}224
[3] P. Toivanen, J. Ansam€aki, J. Parkkinen, J.Mielik€ainen, Edge detection in multispectral images using the self-organizing map, Pattern Recognition Letters 24 (2003) 2987-2994
[4] Bertalmio M, Sapiro G, Ballester C, Caselles V. Image inpainting. In:Proceedings of SIGGRAPH '00, New Orleans; 2000. p. 417-424.
[5] Sun J, Yuan L, Jia J, Shum HY. Image completion with structure propagation. ACM Transactions on Graphics 2005;24(3):861-8.
[6] J. Shen, X. Jin, C. Zhou, C. C. Wang, Gradient based image completion by solving the Poisson equation, Computers & Graphics 31 (2007) 119-126
[7] M. Mainberger, A. Bruhn, JoachimWeickert, SorenForchhammer, Edge-based compression of cartoon-like images with homogeneous diffusion, Pattern Recognition 44 (2011) 1859-1873
[8] M. H.Mahoor, M. A. Mottale, Face recognition based on 3D ridge images obtained from range data, Pattern Recognition 42 (2009)445-451
[9] C. B. Akgul, B. Sankur, Y. Yemez, F. Schmitt, A framework for histogram-induced 3d descriptors, 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, September 4-8, 2006, copyright by EURASIP
[10] J. Morel, A. Petro, C. Sbert, Fourier implementation of Poisson image editing, Pattern Recognition Letters 33 (2012) 342-348
[11] H. Deng, K. L. Chan, J. Liu, The Poisson equation for image texture modelling, Pattern Recognition Letters 24 (2003) 1571-1582
[12] X. Pan, Q. You, Z. Liu, Q. H. Chen, 3D shape retrieval by Poisson histogram, Pattern Recognition Letters 32 (2011) 787-794
[13] M. Anvaripour, H. Ebrahimnezhad, Accurate object detection using local shape descriptors, Pattern Anal Applic, Springer-Verlag London 2013
[14] L. Gorelick, R. Basri, Shape Based Detection and Top-Down Delineation Using Image Segments, Int J Comput Vis (2009) 83: 211-232
[15] Y. Smyrlis, A. Karageorghis, Rotation of the Sources and Normalization of the Fundamental Solutions in the MFS, R. Wyrzykowski et al. (Eds.): PPAM 2001, LNCS2328, pp. 762-769, 2002.
[16] G. Fairweather, A. Karageorghis, The method of fundamental solutions for elliptic boundary value problems, Advances in Computational Mathematics 9 (1998) 69-95
[17] L. Hui, D. Pei-jun, Z. Chang-sheng, S. Ning, Edge detection method of remote sensing images based on mathematical morphology of multi-structure elements, Chinese Geographical Science Volume 14, Number 3, pp. 263-268, 2004 Science Press, Beijing, China
[18] B. Chanda, M. K. Kundu, Y. V. Padmaja, A multi-scale morphologic edge detector, Pattern Recognition, Vol 31, No. 10, pp. 1469-1478, 1998
[19] Y. Yu, C. Chang, A new edge detection approach based on image context analysis, Image and Vision Computing 24 (2006) 1090-1102
[20] P. Tsai, C.C. Chang, Y.C. Hu, An adaptive two-stage edge detection scheme for digital color images, Real-Time Imaging 8 (4) (2002) 329-343
[21] M. S. Shafiq, S. T. Tumer, H. C. Guler, Marker detection and trajectory generation algorithms for a multicamera based gait analysis system, Mechatronics 11 (2001) 409-437
[22] P. Gorzelanczyk, J. A. Kolodziej, Some remarks concerning the shape of the source contour with application of the method of fundamental solutions to elastic torsion of prismatic rods, Engineering analysis with boundary elements 32 (1), 64-75

Abstract viewed - 1179 times
PDF downloaded - 893 times

Affiliations

Tomasz Klekiel
Division of Biomedical Engineering, Faculty of Mechanical Engineering, University of Zielona Gora

Content Top

Application of the Fundamental Solution Method to object recognition in the pictures

Tomasz Klekiel

Vol 7 No 1 (2018)

DOI 10.18495/comengapp.v7i1.224

Submitted: Oct 3, 2017

Published: Feb 10, 2018

Abstract

Recognition of objects in pictures and movies requires the use of techniques, such as filtering, segmentation and classification. Image filtering is required to remove all artifacts that hinder the unequivocal identification and sharpen interesting objects. Segmentation refers to finding areas of images respected to individual objects. For the selected areas corresponding to objects in the selected picture, the classification of objects finally gives information about the type of object which orientation is made. This paper presents a method for the classification of objects from drawings as a bitmap using the method of fundamental solutions (MFS). The MFS was tested on the selected bitmap depicting simple geometric shapes. The correlations between errors occurring on the boundary for particular shapes are used for the selection of geometric shape figures. Due to this correlation, it is possible to recognize the shape of the image appearing on the drawing by an analysis consisting of the comparison of recognized points describing the shape of contour to a database containing solutions of boundary value problems for the selected shape. In one way, the comparison of the pattern can determine which shape from database it is most similar to in terms of contour. This article appear that this approach is very simple and clearly. In result, this method can be used to recognition of the objects in the systems of real-time processing.