scholarly journals Three-Dimensional Image Recognition of Athletes' Wrong Motions Based on Edge Detection

2020 ◽  
Vol 53 (5) ◽  
pp. 733-738
Author(s):  
Haiying Wang
Keyword(s):  
Edge Detection ◽  
Image Recognition ◽  
Visual Motion ◽  
Three Dimensional ◽  
Visual Image ◽  
Detection Algorithm ◽  
Sports Training ◽  
Visual Reconstruction ◽  
Human Motions ◽  
Motion Amplitude

The traditional 3D visual motion amplitude tracking algorithms cannot acquire the complete contour features, not to mention the correction of wrong motions in sports training. To solve the problem, this paper designs a 3D visual image recognition method based on contourlet domain edge detection, and applies it to the recognition of athlete’s wrong motions in sports training. Firstly, the visual reconstruction and feature analysis of human motions were carried out, and the edge detection features were extracted by edge detection algorithm. Then, a 3D visual motion amplitude tracking method was proposed based on improved inverse kinematics. The simulation results show that the proposed algorithm can effectively realize the recognition of 3D visual images of athlete motions, and improve the correction and judgment ability of athlete motions.

Research on the Edge Detection Algorithm of Smokescreen Based on Mathematical Morphology

2014 ◽  
Vol 610 ◽  
pp. 429-436
Author(s):  
Xun Sun ◽  
Xuan Yu Wang ◽  
Zhi Rong Luo ◽  
Han Xiao
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Mathematical Morphology ◽  
Image Recognition ◽  
Operation Time ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Multi Scale ◽  
Practical Complexity

To solve the harmony problem of accuration, real-time with anti-noise capability on edge detection of smokescreen, the edge detection algorithm of smokescreen based on multi-scale mathematical morphological is designed, and the algorithm can effectively reduce the noise of the smokescreen image. Compared with the results of classical edge detection operator: Sobel, Roberts, Prowitt and Canny etc, it is concluded that the algorithm designed has obvious advantages in continuity, smoothness, image recognition, practical complexity, operation time and other related parameters.

Agent Searching Edge Detection Algorithm for Bone Images

2012 ◽  
Vol 433-440 ◽  
pp. 6453-6456
Author(s):  
Hong Guang Zhang ◽  
Yuan’ An Liu ◽  
Bi Hua Tang ◽  
Zhi Peng Jia ◽  
Yan Qin
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Feature Vector ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Human Skeleton ◽  
Edge Detection Algorithm ◽  
Vector Correlation ◽  
Computer Aided

Bone image segmentation is the important technology for computer aided bone diagnosis system and the foundation for three-dimensional visualization of the human skeleton. Agent searching edge detection algorithm for bone images is proposed. Based on neighbor region correlation and regional harmonic mean feature vector correlation, different species of agent accomplish searching bone edge and experimental results are satisfactory. Experimental results comparison about the proposed algorithm, Prewitt, Sobel, Log and Canny is illustrated that demonstrates the proposed algorithm has advantages in some respects.

Three-dimensional contour edge detection algorithm

2000 ◽  
Author(s):  
Yizhou Wang ◽  
Sim Heng Ong ◽  
Ashraf A. Kassim ◽  
Kelvin W. C. Foong
Keyword(s):  
Edge Detection ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Edge Detection Algorithm

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.

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