NDLI: Modified oriented Gaussian derivative filter based texture detection algorithm and parameter estimation

Content Provider	IEEE Xplore Digital Library
Author	Vengalil, S.K. Sinha, N. Raghavan, G.S.
Copyright Year	2015
Description	Author affiliation: Int. Inst. of Inf. Technol., Bangalore, India (Vengalil, S.K.; Sinha, N.; Raghavan, G.S.)
Abstract	Texture in images is used as a cue for various computer vision tasks like segmentation, classification and object detection. In this paper, we explore a variation of texture detection technique using oriented Gaussian derivative filters with multiple scales and orientations. After obtaining the filter responses at each pixel location, K-means clustering is used to determine regions with different textures. The modification to the algorithm is that, we are using only magnitude of the filter responses at each pixel location for determining regions with different textures through K-means clustering. This modification results in drastic changes in texture detection quality compared to original algorithm. In this work, we also propose a novel strategy to determine the optimal range of filter scale that yields semantically meaningful textures. The first and second order statistics of the filter response is used to determine the optimal range of filter scales. The proposed method is illustrated on synthetic images containing only textured regions with different textures.
Starting Page	1
Ending Page	6
File Size	1557032
Page Count	6
File Format	PDF
e-ISBN	9781479999859
DOI	10.1109/CONECCT.2015.7383920
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Computer vision Clustering algorithms Electronic mail Gabor filters Detection algorithms Information technology
Content Type	Text
Resource Type	Article

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