DOI QR코드

DOI QR Code

Determination of Object Similarity Closure Using Shared Neighborhood Connectivity

  • Radhakrishnan, Palanikumar (Department of computer science, College of Computer Science, King Khalid University) ;
  • Arokiasamy, Clementking (Department of computer science, College of Computer Science, King Khalid University)
  • Received : 2014.07.22
  • Accepted : 2014.09.26
  • Published : 2014.09.30

Abstract

Sequential object analysis are playing vital role in real time application in computer vision and object detections.Measuring the similarity in two images are very important issue any authentication activities with how best to compare two independent images. Identification of similarities of two or more sequential images is also the important in respect to moving of neighborhoods pixels. In our study we introduce the morphological and shared near neighborhoods concept which produces a sufficient results of comparing the two images with objects. Considering the each pixel compare with 8-connectivity pixels of second image. For consider the pixels we expect the noise removed images are to be considered, so we apply the morphological transformations such as opening, closing with erosion and dilations. RGB of pixel values are compared for the two sequential images if it is similar we include the pixels in the resultant image otherwise ignore the pixels. All un-similar pixels are identified and ignored which produces the similarity of two independent images. The results are produced from the images with objects and gray levels. It produces the expected results from our process.

Keywords

Similarity Measures;Morphology;Neighborhoods;Pattern recognition

References

  1. Radhakrishnan, P. Sagar, B.S.D. ; Venkatesh, B. "Morphological image analysis of transmission systems"Power Delivery, IEEE Transactions on (Volume:20 , Issue: 1 ) Jan. 219 - 223, 2005, https://doi.org/10.1109/TPWRD.2004.839213
  2. Y.Shan, H.S.Sawhney, A.Pope, "Measuring the Similarity of two image sequences", Asia conference on Computer Vision (ACCY04) 2004.
  3. C.C. Chen and H.T. Chu, Similarity Measurement Between Images, IEEE Conference on Computer Software and Applications (Compsac2005), 41-42, Edinburgh, UK, 2005.
  4. Simone Santini, Ramesh Jain, "Similarity Measures", IEEE Transactions on pattern analysis and machine Intelligence, Vol 21, No.9, September 1999.
  5. R.A. Jarvis, Edward A Patrick, "Clustering Using a Similarity Measure Based on Shared Near Neghbors", IEEE Transactions on computer vol C-22, No.11, November 1973
  6. Guadalupe J. Torres, Ram B. Basnet, Andrew H. Sung, and SrinivasMukkamala,"A Similarity Measure for Clustering and its Applications" Proceedings of World Academy of Science, Engineering and Technology, Vol. 31, ISSN 1307-6884, pp.490-496, Vienna, Austria, July 2008.
  7. Francisco Ortiz, "Gaussian Noise Removal by Color Morphology and Polar Color Models"Image Analysis and RecognitionLecture Notes in Computer Science, Volume 4141, pp 163-172, 2006

Cited by

  1. Efficient Object Localization using Color Correlation Back-projection vol.14, pp.5, 2016, https://doi.org/10.14400/JDC.2016.14.5.263