Orientational Local Binary Pattern Extraction Method for 3D Pollen Image

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.

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Research of Face Recognition Methods Based on Binding Feature Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.668 ◽

2014 ◽

Vol 568-570 ◽

pp. 668-671

Author(s):

Yi Long ◽

Fu Rong Liu ◽

Guo Qing Qiu

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Extraction Method ◽

Local Binary Pattern ◽

Feature Vector ◽

Recognition Rate ◽

Principal Component ◽

Face Image ◽

Feature Extraction Method ◽

Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

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Development of Intelligent Mobile Robot for Service Use. 3rd Report. Biological Real time Pattern Extraction Method Emulating Fly's Behavior and its Application to Hand-Eye System.

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C ◽

10.1299/kikaic.65.146 ◽

1999 ◽

Vol 65 (629) ◽

pp. 146-153

Author(s):

Takayuki TANAKA ◽

Junji OHWI ◽

Kazuo YAMAFUJI

Keyword(s):

Mobile Robot ◽

Real Time ◽

Extraction Method ◽

Service Use ◽

Pattern Extraction ◽

Time Pattern

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A Novel Texture Extraction Method for Digital Radiography

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.719-720.1148 ◽

2015 ◽

Vol 719-720 ◽

pp. 1148-1154

Author(s):

Shuang Qiao ◽

Jia Ning Sun ◽

Jian Li ◽

Ji Peng Huang

Keyword(s):

Digital Radiography ◽

Extraction Method ◽

Local Binary Pattern ◽

Low Dose ◽

Image Texture ◽

Post Processing ◽

Radiographic Images ◽

Shock Filter ◽

Texture Extraction ◽

Shock Filters

As known, there always exist severely degradation problems in digital radiography. How we can extract necessary textures from degraded radiographic images is the post-processing key. Local binary pattern (LBP) is a well-known method, which is widely used in fast image texture extraction. However, for noisy images, LBP can’t work well due to its sensitivity to details. On the other hand, as one of the important shock filters developed in recent years, complex shock filter possesses excellent capabilities in textural image processing. Here, by combining complex shock filter with LBP, a novel fast and efficient method, C-LBP is presented for texture extraction of degraded radiographic images. Experimental results show that comparing with traditional LBP, C-LBP not only distinguishes between noise and details in radiographic images, but also extracts image textures efficiently and rapidly, which plays an important role in developing nondestructive detection technique by low-dose ray radiography.

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