scholarly journals 3D Mathematical Modelling Technology in Visual Rehearsal System of Sports Dance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Chen Chen ◽  
Fahd S Alotaibi ◽  
Saeed Aldulaimi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Mathematical Modelling ◽  
Research Topic ◽  
Experimental Results ◽  
Dance Movement ◽  
Human Animation ◽  
Computer Vision Technology ◽  
Definition Of ◽  
Video Image Processing

Abstract Computer vision technology and video image processing technology in the visual rehearsal of sports dance is a hot research topic. Based on this research background, the thesis uses 3D mathematical modelling technology to interpolate and extract the captured sports and dance movement information to make the final synthesised human animation natural, smooth and lifelike. At the same time, the thesis realises the method of action cohesion through the definition of characteristic action unit attributes and association constraints. Then, it applies it to the visual rehearsal system of sports dance. Finally, the analysis of experimental results proves that the proposed method can improve the precision and recall of rehearsal.

scholarly journals Survey of rice seed quality analysis for varietal purity estimation by using image processing techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 34
Author(s):  
S. Durai ◽  
C. Mahesh ◽  
T. Sujithra ◽  
A. Suresh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
South India ◽  
Seed Quality ◽  
Quality Analysis ◽  
Rice Seed ◽  
Two Factors ◽  
Computer Vision Technology ◽  
Processing Techniques ◽  
Varietal Purity

 In south India rice is the major food source and in agriculture, rice production covers more than 70 percentages of entire forming. But in recent the production only from south India not enough to satisfy the need of all, such a huge demand is there. The better production comes from the selection of good seeds. Up to now formers depend on two factors for selecting better seeds, One is the brand which is approved by some quality standards and second one is analyzed manually by experienced people. Both are risky one, we are not pretty much sure the accuracy of analyze. The second one is seeing and feeling. The inspection is not consistent also very time consuming. In the other way we can use computer vision technology to analyze the quality of the seeds. In recent years many of the big industries they are using computer vision technology with Digital Image Processing for many of the applications. In this Paper we are going to discuss the different seed quality analyzing methods and accuracy of result also. Moreover there are different factors and features are there for it, here we are going to study about varietal purity estimation by different methods.

Vehicle Security Distance Control Based on Binocular Parallax Vision

2014 ◽  
Vol 644-650 ◽  
pp. 207-210
Author(s):  
Shuang Liu ◽  
Xiang Jie Kong ◽  
Ming Cai Shan
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
High Precision ◽  
Stereo Matching ◽  
Vision System ◽  
Intelligent Transportation ◽  
Distance Control ◽  
Calibration Methods ◽  
Computer Vision Technology ◽  
Vehicle Security

Binocular parallax vision system is a kind of computer vision technology. Two cameras on different locations can get two different pictures of same object. The space position of the object can be calculated by the parallax information of two different pictures. The binocular parallax vision technology includes cameras calibration, image processing, and stereo matching analysis. The paper will introduce the inside and outside parameters calibration methods, and combing the traffic applications, designed the calibrating scheme. The parameters that obtained according to the scheme can meet the demands of measuring the vehicle distance. The high precision can meet the needs of intelligent transportation vehicles in a security vehicles spacing survey, which is an effective way for measuring the front car distance.

scholarly journals Texture Classification Using Scattering Statistical and Cooccurrence Features

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Juan Wang ◽  
Jiangshe Zhang ◽  
Jie Zhao
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Texture Classification ◽  
Texture Features ◽  
Research Topic ◽  
Experimental Results ◽  
Statistical Features ◽  
Important Research ◽  
Scattering Transform ◽  
Important Research Topic

Texture classification is an important research topic in image processing. In 2012, scattering transform computed by iterating over successive wavelet transforms and modulus operators was introduced. This paper presents new approaches for texture features extraction using scattering transform. Scattering statistical features and scattering cooccurrence features are derived from subbands of the scattering decomposition and original images. And these features are used for classification for the four datasets containing 20, 30, 112, and 129 texture images, respectively. Experimental results show that our approaches have the promising results in classification.

Posture Correction Technique Based on Visual Analysis

2014 ◽  
Vol 644-650 ◽  
pp. 2632-2635
Author(s):  
An Di
Keyword(s):  
Computer Vision ◽  
Visual Analysis ◽  
Experimental Results ◽  
Basketball Player ◽  
High Quality ◽  
Correction Technique ◽  
Definition Of

In basketball training, if the posture of basketball player is deviated, great impact will be brought to basketball training. Therefore, this paper presents a posture correction technology based on vision analysis. A lot of computer vision image are collected in basketball training, this images are enhanced to improve definition of image, with the high-quality images to identify wrong posture, and compare with standard posture to achieve posture correction in basketball training. Experimental results show that the proposed algorithm for posture correction in basketball training can improve the accuracy of correction, so as to meet the actual needs of basketball training.

scholarly journals An Automated Approach to Mitigate Transcription Errors in Braille Texts for the Portuguese Language

2021 ◽  
Author(s):  
André Roberto Ortoncelli ◽  
Marlon Marcon ◽  
Franciele Beal
Keyword(s):  
Higher Education ◽  
Image Processing ◽  
Computer Vision ◽  
Experimental Results ◽  
Selection Function ◽  
Quota System ◽  
Synthetic Images ◽  
Lack Of Knowledge

The quota system in Brazil made it possible to include blind studentsin higher education. Teachers’ lack of knowledge about the braillesystem can represent a barrier between them and students who useit for writing and reading. Computer-vision-based transcriptionsolutions represent mechanisms for reducing understanding restrictionson this system. However, such tools face nuisances inherentto image processing systems, e.g., illumination, noise, and scale,harming the result. This paper presents an automated approachto mitigate transcription errors in braille texts for the Portugueselanguage. We propose a selection function, combined with dictionaries,that provides the best correspondence of words based ontheir braille representation. We validated our proposal on a datasetof synthetic images by submitting them to different noise levelsand testing the proposal’s robustness. Experimental results confirmthe effectiveness of the solution compared to a standard approach.As a contribution of this paper, we expect to provide a method tosupport robust and adaptable solutions to real use conditions.

scholarly journals Detecção de Roubo de Computadores em Laboratório Usando Visão Computacional

2020 ◽  
Author(s):  
Gabriel Andrade Cordeiro ◽  
Giovani Grockotzki ◽  
Itamar Junior de Azevedo ◽  
João Mantovani ◽  
Matheus Henrique da Silva Santos ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Video Analysis ◽  
Region Of Interest ◽  
Background Information ◽  
Laboratory Monitoring ◽  
Exit Door ◽  
Definition Of ◽  
Computer Labs

Computer theft in computer labs causes academic damage to coursesthat require this resource and ends up directly harming students. Inthis context, this paper describes a methodology applied to detectcomputer removal through video analysis in real-time. For eachframe, image processing and computer vision techniques were used,subtracting background information, binarization, segmentationof the region of interest and definition of contours. The case studywas developed at a Brazilian university. For theft detection, it wasconsidered a black computer tower case carried by people leavingthe laboratory. Monitoring is carried out by a camera positioned infront of the lab exit door. The software developed alerts a suspiciousactivity that may indicate a possible computer theft.

Research on the Real-Time Infrared Tracking Athletics Image Registration Based on Computer Vision

2013 ◽  
Vol 791-793 ◽  
pp. 2002-2006
Author(s):  
Hai Wang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Standard Deviation ◽  
Image Registration ◽  
Computer Hardware ◽  
Good Effect ◽  
Superposition Method ◽  
Linear Superposition ◽  
Computer Vision Technology ◽  
Infrared Tracking

with the development of computer hardware, computer vision technology has been applied to the engineering field. Through the computer vision technology, the image track registration algorithm is conducted in-depth research, and based on the images rigid body transformation, affine, projection and linear superposition method, this paper is the reconstruction of wavelet algorithm and program. This paper uses the general image processing software MATLAB to carry on image processing for the track and field sports, and then the computer vision infrared tracking image registration is numerically simulated, we find that the standard deviation of wavelet reconstruction algorithm achieves 72.3258, which is maximum in four algorithms, entropy and joint entropy respectively reach 5.2332 and 6.2369, after pixel reaching 800, coincidence degree is still maintained at more than 90%. Through the fusion degree of image and the standard deviation of gray entropy, it can be seen that the image registration is achieved good effect.

scholarly journals Comparative Study of Computer Vision Based Line Followers Using Raspberry Pi and Jetson Nano

2021 ◽  
Vol 17 (4) ◽  
Author(s):  
Gunawan Dewantoro ◽  
Jamil Mansuri ◽  
Fransiscus Dalu Setiaji
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection Method ◽  
Robot Vision ◽  
Raspberry Pi ◽  
Robot Motion ◽  
Computer Vision Technology ◽  
Wavy Surfaces ◽  
Robot Performance ◽  
Edge Detection Method

The line follower robot is a mobile robot which can navigate and traverse to another place by following a trajectory which is generally in the form of black or white lines. This robot can also assist human in carrying out transportation and industrial automation. However, this robot also has several challenges with regard to the calibration issue, incompatibility on wavy surfaces, and also the light sensor placement due to the line width variation. Robot vision utilizes image processing and computer vision technology for recognizing objects and controlling the robot motion. This study discusses the implementation of vision based line follower robot using a camera as the only sensor used to capture objects. A comparison of robot performance employing different CPU controllers, namely Raspberry Pi and Jetson Nano, is made. The image processing uses an edge detection method which detect the border to discriminate two image areas and mark different parts. This method aims to enable the robot to control its motion based on the object captured by the webcam. The results show that the accuracies of the robot employing the Raspberry Pi and Jetson Nano are 96% and 98%, respectively.

scholarly journals Underwater Object Detection using Tensorflow

2020 ◽  
Vol 32 ◽  
pp. 03037
Author(s):  
Avinash Mahavarkar ◽  
Ritika Kadwadkar ◽  
Sneha Maurya ◽  
Smitha Raveendran
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Object Detection ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Computer Vision Technology ◽  
Underwater Object ◽  
Suitable Environment

Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created so that Machine Learning algorithm will be used to train different images of the object. Open source Computer Vision has various functions which can be used for the image processing needs when an image is captured.

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