Research Papers

Gaussian and Gabor Filter Approach for Object Segmentation

[+] Author and Article Information
S. Thilagamani

Assistant Professor and Head
Department of Information Technology,
M. Kumarasamy College of Engineering,
Karur, Tamil Nadu 639 113, India
e-mail: thilagan1@yahoo.co.in

N. Shanthi

Professor and Dean
Department of Computer Science
and Engineering,
Nandha Engineering College,
Erode, Tamil Nadu 638052, India

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received June 13, 2013; final manuscript received January 7, 2014; published online March 10, 2014. Assoc. Editor: Charlie C. L. Wang.

J. Comput. Inf. Sci. Eng 14(2), 021006 (Mar 10, 2014) (7 pages) Paper No: JCISE-13-1111; doi: 10.1115/1.4026458 History: Received June 13, 2013; Revised January 07, 2014

The problem of segmenting the object from the background is addressed in the proposed Gaussian and Gabor Filter Approach (GGFA) for object segmentation. An improved and efficient approach based on Gaussian and Gabor Filter reads the given input image and performs filtering and smoothing operation. The region occupied by the object is extracted from the image by performing various operations like bilateral filtering, Edge detection, Clustering, and Region growing. The proposed approach experimented on standard images taken from Caltech datasets, Corel Photo CDs, and Weizmann horse datasets show significantly improved results.

Copyright © 2014 by ASME
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Fig. 7

Evaluations of GGFA on old car object class

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Fig. 2

Performance of HGM [22] for sample standard object classes

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Fig. 3

Performance of GGFA for sample standard object classes

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Fig. 6

GGFA-intermediate operations on old car object class

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Fig. 4

Accuracy on the standard image data set

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Fig. 5

GGFA versus [5,15] based on recall values




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