A Robust Model Fitting-based Method for Transmission Line Extraction from Airborne LiDAR Point Cloud Data

Airborne Light Detection And Ranging(LiDAR)can provide high-quality three-dimensional information for the safety inspection of electricity corridors.However,the robust extraction of transmission lines from airborne point cloud data is still greatly challenging.Therefore,this paper proposes a robust transmission line extraction method based on model fitting from airborne point cloud data.First,the candidate power line generation method based on height information is used to reduce the computational complexity at the subsequent steps and the false positives in the extracted results.Then,on the basis of the block-and-slice-constraint Euclidean clustering,a linear structure recognition method based on RANdom SAmple Consensus(RANSAC)is proposed to produce the initial individual transmission line components.Finally,a robust nonlinear least square-based fitting method is developed for the individual transmission line to generate the parameters of its mathematical model for further optimizing the extraction.Experiments were performed on LiDAR point cloud data captured from the helicopter and Unmanned Aerial Vehicle(UAV)platform.Results indicate that the proposed method can efficiently extract the different types of transmission lines along electricity corridors,with the average precision of approximately 98.1%,the average recall of approximately 95.9%,and the average quality of approximately 94.2%,respectively.

A Line Extraction Algorithm for Hand Drawings

This paper presents an algorithm for extracting lines from hand drawings.It starts from contour pixel tracing, fits them into contour segments, and thenextracts skeleton lines from the contour segments. The algorithm finds all con-tours in one scan of the input matrix without detecting and marking multiplepixels. In line extraction, the method Elastic Contour Segment nacing is pro-posed which extracts lines by referring to the contour segments at both sides,overcoming noise and passing through blotted areas by fitting and extrapolation.Experiments on free hand mechanical drawings, sketches, letter/numerals,as well as Chinese characters are carried out and satisfactory results are achieved.

A Novel Airborne 3D Laser Point Cloud Hole Repair Algorithm Considering Topographic Features

Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3D laser scanning technology to mountain mapping,the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications.In order to solve this problem,we propose to repair the valley and ridge line first,and then repair the point cloud hole.The main technical steps of the method include the following points:First,the valley and ridge feature lines are extracted by the GIS slope analysis method;Then,the valley and ridge line missing from the hole are repaired by the mathematical interpolation method,and the repaired results are edited and inserted to the original point cloud;Finally,the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired.Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method.The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software,the average repair accuracy of the proposed method,in the 16 m buffer zone of valley line and ridge line,is increased from 56.31 cm to 31.49 cm.The repair performance is significantly improved.

Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning

Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.

Region-based structure line detection for cartoons

Cartoons are a worldwide popular visual entertainment medium with a long history. Nowadays,with the boom of electronic devices, there is an increasing need to digitize old classic cartoons as a basis for further editing, including deformation,colorization, etc. To perform such editing, it is essential to extract the structure lines within cartoon images.Traditional edge detection methods are mainly based on gradients. These methods perform poorly in the face of compression artifacts and spatially-varying line colors,which cause gradient values to become unreliable. This paper presents the first approach to extract structure lines in cartoons based on regions. Our method starts by segmenting an image into regions, and then classifies them as edge regions and non-edge regions. Our second main contribution comprises three measures to estimate the likelihood of a region being a non-edge region.These measure darkness, local contrast, and shape.Since the likelihoods become unreliable as regions become smaller, we further classify regions using both likelihoods and the relationships to neighboring regions via a graph-cut formulation. Our method has been evaluated on a wide variety of cartoon images, and convincing results are obtained in all cases.

Navigation algorithm based on semantic segmentation in wheat fields using an RGB-D camera

Determining the navigation line is critical for the automatic navigation of agricultural robots in the farmland.In this research,considering a wheat field as the typical scenario,a novel navigation line extraction algorithm based on semantic segmentation is proposed.The data containing horizontal parallax,height,and grayscale information(HHG)is constructed by combining re-encoded depth data and red-green-blue(RGB)data.The HHG,RGB,and depth data are used to achieve scene recognition and navigation line extraction for a wheat field.The method includes two main steps.First,the semantic segmentation of the wheat,ground,and background are performed using a fully convolutional network(FCN).Second,the navigation line is fitted in the camera coordinate system on the basis of the semantic segmentation result and the principle of camera pinhole imaging.Our segmentation model is trained using 508 randomly selected images from a data set,and the model is tested on 199 images.When labelled data are used as the reference benchmark,the mean intersection over union(mIoU)of the HHG data is greater than 95%,which is the highest among the three types of data.The semantic segmentation methods based on the RGB and HHG data show higher navigation line extraction accuracy rates(with the absolute value of the angle deviation less than 5)than the compared methods.The mean and standard deviation of the angle deviation of the two methods are within 0.1and 2.0,while the mean and standard deviation of the distance deviation are less than 30 mm and 60 mm,respectively.These values meet the basic requirements of agricultural machinery field navigation.The novelty of this work is the proposal of a navigation line extraction algorithm based on semantic segmentation in wheat fields.This method is high in accuracy and robustness to interference from crop occlusion.