scholarly journals A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

2019 ◽  
Vol 11 (14) ◽  
pp. 1636 ◽  
Author(s):  
Xudong Lai ◽  
Jingru Yang ◽  
Yongxu Li ◽  
Mingwei Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Land Management ◽  
Extraction Method ◽  
Point Cloud ◽  
Texture Feature ◽  
Texture Features ◽  
Extraction Methods ◽  
Building Extraction ◽  
Texture Feature Extraction

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

2020 ◽  
Vol 39 (4) ◽  
pp. 4847-4858
Author(s):  
Lei Wang ◽  
Jinhai Sun ◽  
Tuojian Li
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Feature Recognition ◽  
Texture Feature ◽  
Texture Features ◽  
Spatial Domain ◽  
Image Texture ◽  
Support Vector ◽  
Texture Feature Extraction

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

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.

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.

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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