Text feature extraction of natural scenes using Gabor wavelet transformation based on scale overlapping

2011 ◽  
Author(s):  
Yin Fang ◽  
Chen Deyun ◽  
Wu Rui
Keyword(s):  
Feature Extraction ◽  
Wavelet Transformation ◽  
Gabor Wavelet ◽  
Natural Scenes ◽  
Text Feature ◽  
Gabor Wavelet Transformation

A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm

2013 ◽  
Vol 811 ◽  
pp. 430-434
Author(s):  
Hai Feng Wang ◽  
Kun Zhang ◽  
Hong E Ren
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transformation ◽  
Texture Feature ◽  
Near Neighbor ◽  
Classification Algorithm ◽  
Gabor Wavelet ◽  
Accuracy Rate ◽  
Gabor Wavelet Transform ◽  
Texture Image Classification ◽  
Gabor Wavelet Transformation

In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.

scholarly journals Multilingual Text Detection with Nonlinear Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lin Li ◽  
Shengsheng Yu ◽  
Luo Zhong ◽  
Xiaozhen Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Text Detection ◽  
Natural Scenes ◽  
Nonlinear Network ◽  
Text Feature ◽  
Representational Power ◽  
Multilingual Text

Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work.

FACIAL EXPRESSION RECOGNITION BASED ON GABOR WAVELET TRANSFORMATION AND ELASTIC TEMPLATES MATCHING

2006 ◽  
Vol 06 (01) ◽  
pp. 125-138 ◽  
Author(s):  
YONGZHAO ZHAN ◽  
JINGFU YE ◽  
DEJIAO NIU ◽  
PENG CAO
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Wavelet Transformation ◽  
Recognition Rate ◽  
Image Understanding ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
K Nearest Neighbors ◽  
Matching Algorithm ◽  
Gabor Wavelet Transformation

Facial expression recognition technology plays an important role in research areas such as psychological studies, image understanding and virtual reality etc. In order to achieve subject-independent facial expression recognition and obtain robustness against illumination variety and image deformation, facial expression recognition methods based on Gabor wavelet transformation and elastic templates matching are presented in this paper. First given a still image containing facial expression information, preprocessors are executed which include gray and scale normalization. Secondly, Gabor wavelet filters are adopted to extract expression features. Then the elastic graph for expression features is constructed. Finally, elastic templates matching algorithm and K-nearest neighbors classifier are used to recognize facial expression. Experiments show that expression features can be extracted effectively by Gabor wavelet transformation, which is insensitive to illumination variety and individual difference, and high recognition rate can be obtained using elastic templates matching algorithm, which is subject-independent.

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