Phase reconstruction of digital holography with the peak of the two-dimensional Gabor wavelet transform

2009 ◽  
Vol 48 (18) ◽  
pp. 3308 ◽  
Author(s):  
Jiawen Weng ◽  
Jingang Zhong ◽  
Cuiying Hu
Keyword(s):  
Wavelet Transform ◽  
Digital Holography ◽  
Gabor Wavelet ◽  
Two Dimensional ◽  
Phase Reconstruction ◽  
Gabor Wavelet Transform

Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network Combined Neural Network

2016 ◽  
Vol 13 (10) ◽  
pp. 7074-7079
Author(s):  
Yajun Xu ◽  
Fengmei Liang ◽  
Gang Zhang ◽  
Huifang Xu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Network Model ◽  
Neural Network Model ◽  
Gabor Wavelet ◽  
Two Dimensional ◽  
Human Face ◽  
Gabor Wavelet Transform ◽  
Gabor Function ◽  
The Neural Network

This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the BP neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, when the method combining Gabor wavelet transform and the neural network is used to test the human face, it will not influence the detection results despite of complex textures and illumination variations on face images. Besides, when ORL human face database is used to test the model, the human face detection accuracy can reach above 0.93.

Two-Dimensional Gabor Wavelet Transform Profilometry Using Two-Dimensional Grid Fringe Pattern

2010 ◽  
Vol 30 (8) ◽  
pp. 2242-2248
Author(s):  
杨初平 Yang Chuping ◽  
翁嘉文 Weng Jiawen ◽  
林芳 Lin Fang
Keyword(s):  
Wavelet Transform ◽  
Fringe Pattern ◽  
Gabor Wavelet ◽  
Two Dimensional ◽  
Gabor Wavelet Transform

Hyperspectral imaging technique based on Geodesic K-medoids clustering and Gabor wavelets for pork quality evaluation

2017 ◽  
Vol 15 (06) ◽  
pp. 1750066 ◽  
Author(s):  
Shan Zeng ◽  
Ling Chen ◽  
Liang Jiang ◽  
Chongjun Gao
Keyword(s):  
Wavelet Transform ◽  
Quality Evaluation ◽  
Clustering Algorithm ◽  
Hyperspectral Image ◽  
Gabor Wavelet ◽  
Pork Quality ◽  
Two Dimensional ◽  
Gabor Wavelet Transform ◽  
Fcm Algorithm ◽  
Characteristic Wavelength

This paper presents a pork quality evaluation method based on the hyperspectral image datasets of 96 pork samples in the range of 400–1000[Formula: see text]nm. First, through the K-medoids clustering algorithm based on manifold distance, 30 important wavelengths are selected from 753 wavelengths, and final 8 optimum wavelengths are obtained based on the discriminant value and the spectral resolution. Then, the two-dimensional Gabor wavelet transform is used to extract the eight texture features of the image under the final eight wavelengths respectively, to form a 64-dimensional features of pork quality. Finally, using the fussy C-means (FCM) algorithm based on Isomap dimension reduction, the pork quality evaluation model is constructed. The result of wavelength extraction experiments show that although there is a strong linear correlation between adjacent bands in the hyperspectral image, there is an obvious nonlinear manifold relation in the whole band. Therefore, the K-medoids clustering algorithm based on manifold distance in this paper is more reasonable than the traditional principal component analysis (PCA) in characteristic wavelength selection. According to the experiment of pork quality evaluation, two-dimensional Gabor wavelet transform can extract the texture characteristics of pork better. Compared with the FCM algorithm based on PCA, the FCM algorithm based on Isomap can better solve the high-dimensional clustering problem, and can distinguish fresh chilled meat, frozen-thawed meat and spoiled meat accurately. The study shows that hyperspectral image technology can be used in pork quality evaluation.

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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