Experiment Analysis of Different Texture Based Features of Image Using Simplified Gabor Gaussian Wavelet Transform
Rajni Rani1, Kamaljit Kaur2
1Rajni Rani, Master of Technology in Computer Science & Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India.
2Kamaljit Kaur, Assistant Professor, Department Of Computer Science & Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India.
Manuscript received on July 27, 2013. | Revised Manuscript received on August 14, 2013. | Manuscript published on August 30, 2013. | PP: 365-368  | Volume-2, Issue-6, August 2013.  | Retrieval Number: F2105082613/2013©BEIESP

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Textures feature are one of the important features in computer vision for many applications. Texture feature are mostly used for Gabor wavelet transform. It is used for edge detection. Edge detection plays a vital role in computer vision and image processing. Edge of the image is one of the most significant features which are mainly used for image analyzing process. An efficient algorithm for extracting the edge features of images using simplified version of Gabor Wavelet is real time application. Gabor Wavelet is widely used for edge detection. Edge detection finds the real and imaginary part of images of Gabor Wavelet. It is based on noisy and the filtered images using Gabor Wavelet. The performance of Gabor filter is also evaluated by segmentation of noisy, filtered and original images. These statistical metrics are also displayed graphically and they are compared for both the noisy and the filtered images. Simplified Gabor Gaussian Wavelet based approach is highly effective at detecting both the location and orientation of edges. This Proposed technique is used to increase the Peak signal of Noise Ratio (PSNR), and Mean Square Error (MSE) in the MATLAB Software.
Keywords: Gabor Wavelet, Simplified Gabor Gaussian Wavelet Transform, Wavelet Transform.