Discontinuity Detection Algorithm for Three-Dimensional Trajectory Data Analysis in Telerobotics

2012 ◽  
Vol 56 (1) ◽  
pp. 2537-2541 ◽  
Author(s):  
Kiwon Yeom ◽  
Stephen R. Ellis ◽  
Bernard D. Adelstein
Keyword(s):  
Data Analysis ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Trajectory Data ◽  
Discontinuity Detection

scholarly journals Fast Overlap Detection between Hard-Core Colloidal Cuboids and Spheres. The OCSI Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 72
Author(s):  
Luca Tonti ◽  
Alessandro Patti
Keyword(s):  
Colloidal Particles ◽  
Dynamic Properties ◽  
Wide Spectrum ◽  
Three Dimensional ◽  
Hard Core ◽  
Detection Algorithm ◽  
Three Dimensional Objects ◽  
Aggregation Behaviour ◽  
Sphere Radius ◽  
User Friendly

Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI’s performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects’ geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

Three Dimensional Detection of the via in the PCB CT Image Using Morphology Operation

2015 ◽  
Vol 752-753 ◽  
pp. 1406-1412
Author(s):  
Lei Zeng ◽  
Jian Chen ◽  
Han Ning Li ◽  
Bin Yan ◽  
Yi Fu Xu ◽  
...  
Keyword(s):  
Nondestructive Testing ◽  
Printed Circuit Board ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Circuit Board ◽  
Dimensional Structure ◽  
Three Dimension ◽  
Shape Information ◽  
Printed Circuit ◽  
Testing Technology

In modern industry, the nondestructive testing of printed circuit board (PCB) can prevent effectively the system failure and is becoming more and more important. As a vital part of the PCB, the via connects the devices, the components and the wires and plays a very important role for the connection of the circuits. With the development of testing technology, the nondestructive testing of the via extends from two dimension to three dimension in recent years. This paper proposes a three dimensional detection algorithm using morphology method to test the via. The proposed algorithm takes full advantage of the three dimensional structure and shape information of the via. We have used the proposed method to detect via from PCB images with different size and quality, and found the detection performances to be very encouraging.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

An Octree Method for Interference Detection in Computer Aided 3-D Packing

1994 ◽  
Author(s):  
Zuo Dai ◽  
Jianzhong Cha
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Time Response ◽  
Interference Detection ◽  
Computer Memory ◽  
Computational Accuracy ◽  
Data Space ◽  
Computer Aided ◽  
Packing Process

Abstract In simulating the three dimensional packing process with arbitrary shaped objects, the task of detecting interference between objects is important and very difficult. This paper, representing the three dimensional packing space and objects with an octree, presents an effective interference detection algorithm, which can overcome the performance shortcomings that the conventional methods have in terms of real-time response, computer memory and computational accuracy. By recording the distribution status of packing space in the “bits” of short integers, the data space can be compressed to 1/16 of that used by conventional algorithms.

A Low-Dimensional Dissimilarity Analysis of Unilateral and Bilateral Stroke-Impacted Hand Trajectories

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Jay Ryan U. Roldan ◽  
Dejan Milutinović ◽  
Zhi Li ◽  
Jacob Rosen
Keyword(s):  
Multidimensional Scaling ◽  
Healthy Subject ◽  
Motion Capture ◽  
Early Phase ◽  
Three Dimensional ◽  
High Rate ◽  
Motion Capture System ◽  
Trajectory Data ◽  
The Difference ◽  
Low Dimensional

In this paper, we propose a quantitative approach based on identifying hand trajectory dissimilarities through the use of a multidimensional scaling (MDS) analysis. A high-rate motion capture system is used to gather three-dimensional (3D) trajectory data of healthy and stroke-impacted hemiparetic subjects. The mutual dissimilarity between any two trajectories is measured by the area between them. This area is used as a dissimilarity variable to create an MDS map. The map reveals a structure for measuring the difference and variability of individual trajectories and their groups. The results suggest that the recovery of hemiparetic subjects can be quantified by comparing the difference and variability of their individual MDS map points to the points from the cluster of healthy subject trajectories. Within the MDS map, we can identify fully recovered patients, those who are only functionally recovered, and those who are either in an early phase of, or are nonresponsive to the therapy.

scholarly journals An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Wang ◽  
Xinzheng Niu ◽  
Jiahui Zhu ◽  
Zuoyan Liu
Keyword(s):  
Community Detection ◽  
Trajectory Analysis ◽  
Moving Objects ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Similarity Matrix ◽  
Trajectory Data ◽  
Trajectory Similarity ◽  
Community Detection Algorithm

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

Sign in / Sign up

Export Citation Format

Share Document