A multi-scale plane-detection method based on the Hough transform and region growing

2016 ◽  
Vol 31 (154) ◽  
pp. 166-192 ◽  
Author(s):  
Xiaoxu Leng ◽  
Jun Xiao ◽  
Ying Wang
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Region Growing ◽  
Multi Scale

The Multi-Scale Hough Transform Lane Detection Method Based on the Algorithm of Otsu and Canny

2014 ◽  
Vol 1042 ◽  
pp. 126-130 ◽  
Author(s):  
Yu Chai ◽  
Su Jing Wei ◽  
Xin Chun Li
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Lane Detection ◽  
Transform Method ◽  
Canny Operator ◽  
The Road ◽  
Multi Scale ◽  
Lane Departure ◽  
Lane Line

In order to improve the accuracy of detecting lane for automatic vehicle driving, a method for detecting the straight part of Lane is proposed, which is the Multi-Scale Hough transform method for lane detection based on the algorithm of Otsu and Canny. First of all, by the methods of Otsu to segment image and use the morphology operation of erode and dilate to wipe off the information of roadside trees and fences to strengthen the road boundary characteristics.Then the lane edge and feature is gained by the canny operator. At last, using Standard Hough Transform, Progressiveness Probabilities Hough Transform and Multi-Scale Hough Transform complete the detection of lane’s straight part. The experimental results show that, Multi-Scale Hough Transform method can accurately detect the lane line and provide the reliable basis for the path planning, automatic follow-up vehicle driving and lane departure warning.

Study on Damage Detection of Bridge Based on Wavelet Multi-Scale Analysis

2013 ◽  
Vol 639-640 ◽  
pp. 1010-1014 ◽  
Author(s):  
Ke Ding ◽  
Ting Peng Chen
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Detection Method ◽  
Mode Shapes ◽  
Scale Analysis ◽  
Multi Scale ◽  
Damage Location ◽  
Transform Coefficients ◽  
Beam Bridge ◽  
Multi Scale Analysis

The damage detection method based on wavelet multi-scale analysis is presented in the paper. The damage location can be identified by analyzing the multi-scale wavelet transform coefficients of curvatures of mode shapes. The extreme value of wavelet transform coefficients indicates the damage location. But it is difficult to detect the location of defect if the defect is near to the equilibrium position of vibration. In order to solve this problem, we put forward a method which is to add the wavelet transform coefficients of multi modals together. The method can effectively overcome the above problem. Three damage situations of simply supported beam bridge are discussed in the paper. The results show that the peaks of wavelet transform coefficients indicate the damage location of structural. It is possible to pinpoint the damage location based on wavelet multi-scale analysis on curvatures of mode shapes.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

scholarly journals Detection method for auto guide vehicle’s walking deviation based on image thinning and Hough transform

2019 ◽  
Vol 52 (3-4) ◽  
pp. 252-261 ◽  
Author(s):  
Xiaohua Cao ◽  
Daofan Liu ◽  
Xiaoyu Ren
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Detection Algorithm ◽  
Visual Navigation ◽  
Guide Line ◽  
Actual Environment ◽  
Lane Line ◽  
Image Thinning ◽  
High Level ◽  
Refinement Algorithm

Auto guide vehicle’s position deviation always appears in its walking process. Current edge approaches applied in the visual navigation field are difficult to meet the high-level requirements of complex environment in factories since they are easy to be affected by noise, which results in low measurement accuracy and unsteadiness. In order to avoid the defects of edge detection algorithm, an improved detection method based on image thinning and Hough transform is proposed to solve the problem of auto guide vehicle’s walking deviation. First, the image of lane line is preprocessed with gray processing, threshold segmentation, and mathematical morphology, and then, the refinement algorithm is employed to obtain the skeleton of the lane line, combined with Hough detection and line fitting, the equation of the guide line is generated, and finally, the value of auto guide vehicle’s walking deviation can be calculated. The experimental results show that the methodology we proposed can deal with non-ideal factors of the actual environment such as bright area, path breaks, and clutters on road, and extract the parameters of the guide line effectively, after which the value of auto guide vehicle’s walking deviation is obtained. This method is proved to be feasible for auto guide vehicle in indoor environment for visual navigation.

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