Topological feature recognition and blend feature protection for non-duality point clouds

2020 ◽  
Vol 28 (10) ◽  
pp. 2301-2310
Author(s):  
Chun-kang ZHANG ◽  
◽  
Hong-mei LI ◽  
Xia ZHANG
Keyword(s):  
Feature Recognition ◽  
Point Clouds ◽  
Topological Feature

A Compared Approach on How Deep Learning May Support Reverse Engineering for Tolerance Inspection

2019 ◽  
Author(s):  
Michele Bici ◽  
Saber Seyed Mohammadi ◽  
Francesca Campana
Keyword(s):  
Deep Learning ◽  
Reverse Engineering ◽  
Shape Control ◽  
Feature Recognition ◽  
Point Clouds ◽  
Smart Manufacturing ◽  
Post Processing ◽  
Die Set ◽  
Set Up ◽  
Voxel Grid

Abstract Reverse Engineering (RE) may help tolerance inspection during production by digitalization of analyzed components and their comparison with design requirements. RE techniques are already applied for geometrical and tolerance shape control. Plastic injection molding is one of the fields where it may be applied, in particular for die set-up of multi-cavities, since no severe accuracy is required for the acquisition system. In this field, RE techniques integrated with Computer-Aided tools for tolerancing and inspection may contribute to the so-called “Smart Manufacturing”. Their integration with PLM and suppliers’ incoming components may set the information necessary to evaluate each component and die. Intensive application of shape digitalization has to front several issues: accuracy of data acquisition hardware and software; automation of experimental and post-processing steps; update of industrial protocol and workers knowledge among others. Concerning post-processing automation, many advantages arise from computer vision, considering that it is based on the same concepts developed in a RE post-processing (detection, segmentation and classification). Recently, deep learning has been applied to classify point clouds, considering object and/or feature recognition. This can be made in two ways: with a 3D voxel grid, increasing regularity, before feeding data to a deep net architecture; or acting directly on point cloud. Literature data demonstrate high accuracy according to net training quality. In this paper, a preliminary study about CNN for 3D points segmentation is provided. Their characteristics have been compared to an automatic approach that has been already implemented by the authors in the past. VoxNet and PointNet architectures have been compared according to the specific task of feature recognition for tolerance inspection and some investigations on test cases are discussed to understand their performance.

scholarly journals COMPARISON OF UAV-ENABLED PHOTOGRAMMETRY-BASED 3D POINT CLOUDS AND INTERPOLATED DSMs OF SLOPING TERRAIN FOR ROCKFALL HAZARD ANALYSIS

2016 ◽  
Vol XLII-2/W2 ◽  
pp. 71-77 ◽  
Author(s):  
J. Manousakis ◽  
D. Zekkos ◽  
F. Saroglou ◽  
M. Clark
Keyword(s):  
Feature Recognition ◽  
Hazard Analysis ◽  
Model Development ◽  
Back Analysis ◽  
Point Clouds ◽  
3D Analysis ◽  
Control Points ◽  
Image Block ◽  
Rockfall Hazard ◽  
3D Point Clouds

UAVs are expected to be particularly valuable to define topography for natural slopes that may be prone to geological hazards, such as landslides or rockfalls. UAV-enabled imagery and aerial mapping can lead to fast and accurate qualitative and quantitative results for photo documentation as well as basemap 3D analysis that can be used for geotechnical stability analyses. In this contribution, the case study of a rockfall near Ponti village that was triggered during the November 17th 2015 M<sub>w</sub> 6.5 earthquake in Lefkada, Greece is presented with a focus on feature recognition and 3D terrain model development for use in rockfall hazard analysis. A significant advantage of the UAV was the ability to identify from aerial views the rockfall trajectory along the terrain, the accuracy of which is crucial to subsequent geotechnical back-analysis. Fast static GPS control points were measured for optimizing internal and external camera parameters and model georeferencing. Emphasis is given on an assessment of the error associated with the basemap when fewer and poorly distributed ground control points are available. Results indicate that spatial distribution and image occurrences of control points throughout the mapped area and image block is essential in order to produce accurate geospatial data with minimum distortions.

Geometric and topological feature extraction of linear segments from 2D cross-section data of 3D point clouds

2015 ◽  
Author(s):  
Rajesh Ramamurthy ◽  
Kevin Harding ◽  
Xiaoming Du ◽  
Vincent Lucas ◽  
Yi Liao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Cross Section ◽  
Point Clouds ◽  
Topological Feature ◽  
Cross Section Data ◽  
Section Data ◽  
3D Point Clouds

Towards New Transitions Among Shape Representations

2003 ◽  
Author(s):  
Joris S. M. Vergeest ◽  
Chensheng Wang ◽  
Yu Song ◽  
Sander Spanjaard
Keyword(s):  
Feature Recognition ◽  
Shape Representation ◽  
Design Feature ◽  
Point Clouds ◽  
Shape Representations ◽  
New Methods ◽  
Feature Based ◽  
Representation Class ◽  
High Level ◽  
Feature Based Design

Four classes of shape representation are dominating nowadays in computer-supported design and modeling of products, (1) point clouds, (2) surface meshes, (3) solid/surface models and (4) design/styling models. To support applications such as high-level shape design, feature-based design, shape modeling, shape analysis, rapid prototyping, feature recognition and shape presentation, it is required that transitions among and within the four representation classes take place. Transitions from a “lower” representation class to “higher” class are far from trivial, and at the same time highly demanded for reverse design purposes. New methods and algorithms are needed to accomplish new transitions. A characterization of the four classes is presented, the most relevant transitions are reviewed and a relatively new transition, from point cloud directly to design/styling model is proposed and experimented. The importance of this transition for new methods of shape reuse and redesign is pointed out and demonstrated.

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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