An improved wood texture feature description algorithm of Local Binary Pattern operator is proposed. Firstly, the linear LBP operator is used to extract the texture features of the image. Then, the similarity between textures is calculated for the sub-region of the wood image. Because of the extremely high texture similarity of the striped wood, this method can be used to quickly and accurately distinguish the two types of images of stripes and patterns. Experimental results show that the algorithm has strong ability to describe wood texture features, is more robust than traditional methods, and has higher recognition accuracy
Effective statistical feature extraction and classification are important in image-based automatic i...
Local Binary Pattern (LBP) can provide us with the spatial structure of images and describe the orig...
Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition app...
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Because texture of object is very valuable information in computer vision, it is important to descri...
The price of the wood according to the type of wood. Classification of the woods can be done by stud...
With the substantial expansion of image information, image processing and computer vision have signi...
Abstract Texture plays an important role in numerous computer vision applications. Many methods for ...
In this correspondence, a completed modeling of the local binary pattern (LBP) operator is proposed ...
The process of identifying images and patterns is one of the most important processes of digital ima...
Local Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces...
© 2017 Elsevier Ltd Local binary patterns (LBP) and its variants have shown great potentials in text...
Effective statistical feature extraction and classification are important in image-based automatic i...
Local Binary Pattern (LBP) can provide us with the spatial structure of images and describe the orig...
Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition app...
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Because texture of object is very valuable information in computer vision, it is important to descri...
The price of the wood according to the type of wood. Classification of the woods can be done by stud...
With the substantial expansion of image information, image processing and computer vision have signi...
Abstract Texture plays an important role in numerous computer vision applications. Many methods for ...
In this correspondence, a completed modeling of the local binary pattern (LBP) operator is proposed ...
The process of identifying images and patterns is one of the most important processes of digital ima...
Local Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces...
© 2017 Elsevier Ltd Local binary patterns (LBP) and its variants have shown great potentials in text...
Effective statistical feature extraction and classification are important in image-based automatic i...
Local Binary Pattern (LBP) can provide us with the spatial structure of images and describe the orig...
Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition app...