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Error Truncate & Fast Gabor Filter Algorithm for the Application of Texture Segmentation


Author(s):

Sourabh Kumar Kashyap , Patel college of Science & Technology,Bhopal (M.P); Jitendra Kumar Mishra, Patel college of Science & Technology,Bhopal (M.P)

Keywords:

Gabor filter, Gabor energy, image quality assessment, Gabor features

Abstract:

In applications of image analysis and computer vision, Gabor filters have maintained their popularity in feature extraction. The reason behind this is that the resemblance between Gabor filter and receptive field of simple cells in visual cortex. Being successful in applications like face detection, iris recognition, fingerprint matching; where, Gabor feature based processes are amongst the best performers. The Gabor features can be derived by applying signal processing techniques both in time and frequency domain. The methods have been proposed to extract low dimension features from Gabor filtered images by considering the sparseness of the filter bank responses. Approaches like unsupervised segmentation of textured images have provided good approximation of Fisher's multiple linear discriminants with added advantage that they don't require a-priori-knowledge. Local texture properties are extracted from local linear transforms that have been optimized for maximal texture discrimination. Local statistics are estimated at the output of an equivalent filter bank by means of a non-linear transform followed by an iterative Gaussian smoothing algorithm. This process generates multiresolution sequence of feature planes with a half octave scale progression. The models like human preattentive texture perception have been proposed which involves steps like convolution, inhibition and texture boundary detection. Texture features are based on the local power spectrum obtained by a bank of Gabor filters. The concept of sparseness to generate novel contextual multiresolution texture descriptors is described. Image quality assessment (IQA) aims to provide computational models to measure the image quality in a perceptually consistent manner. The tradeoff between power consumption and speed performance has become a major design consideration when devices approach the sub-100 nm regime. It is especially critical when dealing with large data set, whereby the system is degraded in terms of power and speed. If the application can accept some errors, i.e. the application is Error- tolerant (ET), a large reduction in power and an increased in speed can be simultaneously achieved. Here we will use some scientific parameter for image quality like signal to noise ratio, FSIM, RFSIM, GMSD, SSIM MATLAB codes required in calculating these parameters are developed. Here algorithm is devolving by using of Matlab.


Other Details:

Manuscript Id :IJSTEV3I11065
Published in :Volume : 3, Issue : 11
Publication Date: 01/06/2017
Page(s): 97-102
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