Extraction of the Palm Vein Texture Features Based on Gabor Wavelet Transforms

2014 ◽  
Vol 511-512 ◽  
pp. 429-432 ◽  
Author(s):  
Yan Shi ◽  
Long Wu Wang ◽  
Hui Ying Lan ◽  
Guang Zeng
Keyword(s):  
Wavelet Transforms ◽  
Ring Finger ◽  
Texture Feature ◽  
Texture Features ◽  
Middle Finger ◽  
Gabor Wavelet ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Shape Structure ◽  
Palm Vein

A texture feature extraction method based on Gabor wavelet transform is presented to avoid the limitations of the shape structure characteristics extraction in the palm vein image. First acquire the palm vein images by independent structures of vein acquire device then extracts ROI area of vein images by determining the centerline of the middle finger and ring finger interphalangeal. Correcting the vein image by Gamma operator will improve the contrast of the processing image. Finally, transform the image based on Gabor wavelet to extract texture feature.

Adaptive Texture Feature Extraction with Application to Ultrasonic Image Analysis

1998 ◽  
Vol 20 (2) ◽  
pp. 132-148 ◽  
Author(s):  
H.J. Huisman ◽  
J.M. Thijssen
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Texture Features ◽  
Small Data ◽  
Data Sets ◽  
Large Set ◽  
Texture Feature Extraction ◽  
Feed Forward Neural Network ◽  
Feature Extraction Method ◽  
Optimal Feature

Computer texture analysis methods use texture features that are traditionally chosen from a large set of fixed features known in literature. These fixed features are often not specifically designed to the problem at hand, and as a result they may have low discriminative power, and/or may be correlated. Increasing the number of selected fixed features is statistically not a good solution in limited data environments such as medical imaging. For that reason, we developed an adaptive texture feature extraction method (ATFE) that extracts a small number of features that are tuned to the problem at hand. By using a feed-forward neural network, we ensure that even nonlinear relations are captured from the data. Using extensive, repeated synthetic ultrasonic images, we compared the performance of ATFE with the optimal feature set. We show that the ATFE method is capable of robust operation on small data sets with a performance close to that of the optimal feature set. Another experiment confirms that our ATFE is capable of capturing nonlinear relations from the dataset. We conclude that our method can improve performance in practical, limited dataset situations where an optimal fixed feature set can be hard to find.

Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval

2011 ◽  
pp. 44-67 ◽  
Author(s):  
Gang Zhang ◽  
Z. M. Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Image ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Texture Feature Extraction ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Occurrence Matrix

Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first proposes a framework of content-based medical image retrieval system. It then analyzes the important texture feature extraction and description methods further, such as the co-occurrence matrix, perceptual texture features, Gabor wavelet, and so forth. Moreover, the chapter analyzes the improved methods for these methods and demonstrates their application in content-based medical image retrieval.

Wear Particles Recognition Based on Improved LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1874-1878
Author(s):  
Guo De Wang ◽  
Zhi Sheng Jing ◽  
Guo Wei Qin ◽  
Shan Chao Tu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Binary Sequence ◽  
Texture Feature ◽  
Experimental Results ◽  
Wear Particles ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

Wear particles recognition is a key link in the process of Ferrography analysis. Different kinds of wear particles vary greatly in texture, texture feature is one of the most important feature in wear particles recognition. Local Binary Pattern (LBP) is an efficient operator for texture description. The binary sequence of traditional LBP operator is obtained by the comparison between the gray value of the neighborhood and the gray value of the center pixel of the neighborhood, the comparison is too simple to cause the loss of the texture. In this paper, an improved LBP operator is presented for texture feature extraction and it is applied to the recognition of severe sliding particles, fatigue spall particles and laminar particles. The experimental results show that our method is an effective feature extraction method and obtains better recognition accuracy compared with other methods.

Facial Expression Recognition Based on Gabor Texture Features and Centre Binary Pattern

2015 ◽  
Vol 742 ◽  
pp. 257-260 ◽  
Author(s):  
Li Sai Li ◽  
Zi Lu Ying ◽  
Bin Bin Huang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Wavelet Filters ◽  
Adaboost Algorithm ◽  
Gabor Feature

This paper was proposed a new algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Centre Binary Pattern (CBP). Firstly, gabor texture feature were extracted from every expression image. Five scales and eight orientations of gabor wavelet filters were used to extract gabor texture features. Then the CBP features were extracted from gabor feature images and adaboost algorithm was used to select final features from CBP feature images. Finally, we obtain expression recognition results on the final expression features by Sparse Representation-based Classification (SRC) method. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm had a much higher recognition rate than the traditional algorithms.

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

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