Automatic Extraction of Rock Joints from Laser Scanned Data by Moving Least Squares Method and Fuzzy K-means Clustering
نویسندگان
چکیده
Recent development of laser scanning device increased the capability of representing rock outcrop in a very high resolution. Accurate 3D point cloud model with rock joint information can help geologist to estimate stability of rock slope on-site or off-site. An automatic plane extraction method was developed by computing normal directions and grouping them in similar direction. Point normal was calculated by moving least squares (MLS) method considering every point within a given distance to minimize error to the fitting plane. Normal directions were classified into a number of dominating clusters by fuzzy K-means clustering. Region growing approach was exploited to discriminate joints in a point cloud. Overall procedure was applied to point cloud with about 120,000 points, and successfully extracted joints with joint information. The extraction procedure was implemented to minimize number of input parameters and to construct plane information into the existing point cloud for less redundancy and high usability of the point cloud itself. * Corresponding author.
منابع مشابه
Fuzzy Clustering Approach Using Data Fusion Theory and its Application To Automatic Isolated Word Recognition
In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...
متن کاملLinear Feature Extraction from Scanned Color Maps with Complex Background
Map digitization is one of the most important means of geographic data acquisition. Automatic extraction of geographic features from scanned maps is the key technique to improve the efficiency of map digitization. With regard to linear feature extraction from color topographic map images, it is usually performed by interactive or automatic line tracking based on color separated binary images. B...
متن کاملMulti-Output Adaptive Neuro-Fuzzy Inference System for Prediction of Dissolved Metal Levels in Acid Rock Drainage: a Case Study
Pyrite oxidation, Acid Rock Drainage (ARD) generation, and associated release and transport of toxic metals are a major environmental concern for the mining industry. Estimation of the metal loading in ARD is a major task in developing an appropriate remediation strategy. In this study, an expert system, the Multi-Output Adaptive Neuro-Fuzzy Inference System (MANFIS), was used for estimation of...
متن کاملFUZZY K-NEAREST NEIGHBOR METHOD TO CLASSIFY DATA IN A CLOSED AREA
Clustering of objects is an important area of research and application in variety of fields. In this paper we present a good technique for data clustering and application of this Technique for data clustering in a closed area. We compare this method with K-nearest neighbor and K-means.
متن کاملA method for automated discontinuity analysis of rock slopes with 3D laser scanning
This paper describes the interim results of a study to characterize discontinuous rock masses using 3D laser scanning data. One of the main advantages of this method is that now an unbiased, rapid and accurate discontinuity analysis can be done. With 3D laser scanning it is now also possible to measure rock faces whose access is restricted or rock slopes along highways or railway lines where wo...
متن کامل