Journal article Open Access

Automatic Moment-Based Texture Segmentation

Tudor Barbu


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  <identifier identifierType="DOI">10.5281/zenodo.1089619</identifier>
  <creators>
    <creator>
      <creatorName>Tudor Barbu</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Automatic Moment-Based Texture Segmentation</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2013</publicationYear>
  <subjects>
    <subject>Image segmentation</subject>
    <subject>moment-based texture analysis</subject>
    <subject>automatic classification</subject>
    <subject>validity indexes.</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2013-11-06</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1089619</alternateIdentifier>
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  <version>9996875</version>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Then, an automatic pixel classification approach is proposed. The feature vectors are clustered using an unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.&lt;/p&gt;</description>
    <description descriptionType="Other">{"references": ["L. Shapiro, G. Stockman, Computer Vision, New Jersey, Prentice-Hall, 2001, pp. 279-325.", "M. Tuceryan, A. K. Jain, Texture Analysis. Handbook Pattern Recognition and Computer Vision. Singapore: World Scientific, ch. 2, 1993, pp. 235\u2013276.", "M. K. Pietik\u00e4inen, \"Texture Analysis in Machine Vision\", Series in Machine Perception and Artificial Intelligence, vol. 40, 2000.", "V. Levesque, \"Texture segmentation using Gabor filters\", in Center for Intelligent Machines Journal, 2000.", "M. N. Do, M. Vetterli, \"Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance\", in IEEE Transactions on Image Processing, 11:2, February 2002.", "T. Barbu, \"A Pattern Recognition Approach to Image Segmentation\", Proceedings of the Romanian Academy, Series A: Mathematics, Physics, Technical Sciences, Information Science, Volume 4, Number 2, 2003, pp. 143 \u2013 148.", "T. Barbu, \"An Automatic Graphical Recognition System using Area Moments\", WSEAS Transactions on Computers, Issue 9, Volume 5, 2006, pp. 2142-2147.", "B. Abraham, O. I. Camps, M. Sznaier, \"Dynamic Texture with Fourier Descriptors\", Proceedings of the 4th International Workshop on Texture Analysis and Synthesis, 2005, pp. 53-58.", "M. Tuceryan, A. K. Jain, \"Texture Segmentation Using Voronoi Polygons\", IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-12, 1990, pp. 211 - 216.\n[10]\tR. M. Haralick, K. Shanmugam, I. Dinstein, \"Textural features for image classification\", IEEE Transactions on Systems, Man, and Cybernetics, SMC - 3, 1973, pp. 610 - 621.\n[11]\tF. S. Cohen, D. B. Cooper, \"Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields\", IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-9, 1987, pp. 195-219.\n[12]\tT. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, A. Y. Wu, \"An Efficient K-Means Clustering Algorithm: Analysis and Implementation\", IEEE Trans. on Pattern Analysis and Machine Intelligence, Volume 24, Number 7, 2002, pp. 881-892.\n[13]\tJ. Dunn, \"Well separated clusters and optimal fuzzy partitions\", Journal of Cybernetics vol.  4, 1974, pp. 95-104.\n[14]\tD. L. Davies, D. W. Bouldin, \"A cluster separation measure\", IEEE Transactions on  Pattern Analysis and Machine Intelligence,  Vol. 1 (4), 2000, pp. 224-227.\n[15]\tT. Barbu, \"Approximations of the filtering problem via fractional steps method\", Communications in Applied Analysis, Vol. 8, No. 2, Dynamic Publishers, USA, 2004, pp. 263-278.\n[16]\tT. Barbu, V. Barbu, V., Biga, D. Coca, \"A PDE variational approach to image denoising and restoration\", Nonlinear Analysis: Real World Applications, Volume 10, Issue 3, 2009, pp. 1351-1361.\n[17]\tT. Kurita, \"An Efficient Agglomerative Clustering Algorithm for Region Growing\", Proc. of IAPR Workshop on Machine Vision Applications, MVA '94, Kawasaki, Dec. 13-15, 1994, pp. 210-213.\n[18]\tT. Barbu, \"An Automatic Unsupervised Pattern Recognition Approach\", Proceedings of the Romanian Academy, Series A: Mathematics, Physics, Technical Sciences, Information Science, Vol. 7, Number 1, January 2006, pp. 73 \u2013 78.\n[19]\tB. S. Manjunath, W. Y. Ma, \"Texture Features for Browsing and Retrieval of Image Data\", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 18, Number 8, Aug. 1996, pp. 837-842."]}</description>
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