Multiscale Corner Detection of Gray Level Images Based on Log-Gabor Wavelet Transform

2007 ◽  
Author(s):  
Xinting Gao ◽  
Farook Sattar ◽  
Ronda Venkateswarlu
Keyword(s):  
Wavelet Transform ◽  
Corner Detection ◽  
Gabor Wavelet ◽  
Gray Level ◽  
Gabor Wavelet Transform ◽  
Log Gabor Wavelet ◽  
Log Gabor ◽  
Gray Level Images

Content Based Image Retrieval Based on Log Gabor Wavelet Transform

2011 ◽  
Vol 403-408 ◽  
pp. 871-878 ◽  
Author(s):  
Megha Agarwal ◽  
Rudra Prakash Maheshwari
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Retrieval System ◽  
Gabor Wavelet ◽  
Content Based Image Retrieval ◽  
Natural Image ◽  
Feature Descriptor ◽  
Gabor Wavelet Transform ◽  
Log Gabor Wavelet ◽  
Log Gabor

This paper proposes a novel approach of content based image retrieval based on Log Gabor Wavelet Transform (LGWT). It is observed that LGWT better represents an image compared to Gabor Wavelet Transform (GWT). Experimental results illustrate the comparative analysis of proposed retrieval system and the retrieval system based on GWT feature descriptor. It is verified that LGWT based retrieval system improves the average precision and average recall (55.46% and 32.03% respectively) from GWT based retrieval system (50.61% and 31.63% respectively). All the experiments are performed on Corel 1000 natural image database.

scholarly journals Identification method of cashmere and wool based on texture features of GLCM and Gabor

2021 ◽  
Vol 16 ◽  
pp. 155892502198917
Author(s):  
Yaolin Zhu ◽  
Jiayi Huang ◽  
Tong Wu ◽  
Xueqin Ren
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Texture Feature ◽  
Identification Accuracy ◽  
Gabor Wavelet ◽  
Gray Level ◽  
Gabor Wavelet Transform ◽  
Second Moment ◽  
Feature Vectors ◽  
Occurrence Matrix

The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

Down Syndrome Diagnosis Based on Gabor Wavelet Transform

2011 ◽  
Vol 36 (5) ◽  
pp. 3205-3213 ◽  
Author(s):  
Şafak Saraydemir ◽  
Necmi Taşpınar ◽  
Osman Eroğul ◽  
Hülya Kayserili ◽  
Nuriye Dinçkan
Keyword(s):  
Down Syndrome ◽  
Wavelet Transform ◽  
Gabor Wavelet ◽  
Gabor Wavelet Transform ◽  
Syndrome Diagnosis
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