Robust Fitting Using Mean Shift: Applications in Computer Vision

2004 ◽  
pp. 307-318 ◽  
Author(s):  
D. Suter ◽  
H. Wang
Keyword(s):  
Computer Vision ◽  
Mean Shift ◽  
Robust Fitting

Curling stone tracking based on an enhanced mean-shift algorithm using optimal feature vector

2020 ◽  
pp. 175433712096772
Author(s):  
Junghu Kim ◽  
Youngjoon Han
Keyword(s):  
Computer Vision ◽  
Feature Vector ◽  
Mean Shift ◽  
Game Analysis ◽  
Mean Shift Algorithm ◽  
Computer Vision Technology ◽  
Game Elements ◽  
Camera Sensors ◽  
Contact Sensors ◽  
Optimal Feature

Computer vision technology can automatically detect and recognize objects on the ground or on a court, such as balls, players, and lines, using camera sensors. These are non-contact sensors, which do not interfere with an athlete’s movement. The game elements detected by such measuring equipment can be used for game analysis, judgment, context recognition, and visualization. This paper proposes a method to automatically track the position of stones in curling sport images using computer vision technology. The authors extract the optimal feature vector of the mean-shift tracking algorithm by obtaining the optimal histogram from the color and edge information of the curling stone, thereby adaptively controlling the number of bins in the histogram. After evaluating the performance of the curling stone tracking method among 1424 image frames from curling sport videos, the authors found that the proposed method improved detection rate (overlap threshold = 0.9) by 14.85% compared to the general mean-shift method.

scholarly journals Robust Fitting in Computer Vision: Easy or Hard?

2019 ◽  
Vol 128 (3) ◽  
pp. 575-587 ◽  
Author(s):  
Tat-Jun Chin ◽  
Zhipeng Cai ◽  
Frank Neumann
Keyword(s):  
Computer Vision ◽  
Robust Fitting ◽  
Fitting In

Research on Mean Shift Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 4021-4025 ◽  
Author(s):  
Yi Zhi Zhao ◽  
Huan Wang ◽  
Guo Cai Yin
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Target Tracking ◽  
Mean Shift ◽  
Moving Target ◽  
Object Model ◽  
Probability Density Estimation ◽  
Complete Occlusion ◽  
Mean Shift Algorithm ◽  
High Level

Computer vision is a diverse and relatively new field of study. Object tracking plays a crucial role as a preliminary step for high-level image processing in the field of computer vision. However, mean shift algorithm in the target tracking has some defects, such as: the application of fixed bandwidth for probability density estimation usually causes lack of smooth or too smooth; moving target often appears partial occlusion or complete occlusion due to the complexity of the background; background pixels in object model will induce localization error of object tracking, and so on. Therefore, this paper elaborates several elegant algorithms to solve some of the problems. After discussing the application of Mean shift in the field of target tracking, this paper presented an improved Mean shift algorithm by combining Mean Shift and Kalman Filter.

scholarly journals A Hybrid Surf-Based Tracking Algorithm with Online Template Generation

2019 ◽  
Vol 8 (12) ◽  
pp. 794-798
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Mean Shift ◽  
Data Sets ◽  
Camera Motion ◽  
Interest Point ◽  
Illumination Change ◽  
Mean Shift Algorithm ◽  
Pose Variation

Visual tracking is the most challenging fields in the computer vision scope. Occlusion full or partial remains to be a big mile stone to achieve .This paper deals with occlusion along with illumination change, pose variation, scaling, and unexpected camera motion. This algorithm is interest point based using SURF as detector descriptor algorithm. SURF based Mean-Shift algorithm is combined with Lukas-Kanade tracker. This solves the problem of generation of online templates. These two trackers over the time rectify each other, avoiding any tracking failure. Also, Unscented Kalman Filter is used to predict the location of target if target comes under the influence of any of the above mentioned challenges. This combination makes the algorithm robust and useful when required for long tenure of tracking. This is proven by the results obtained through experiments conducted on various data sets.

scholarly journals Robust Fitting in Computer Vision: Easy or Hard?

2018 ◽  
pp. 715-730 ◽  
Author(s):  
Tat-Jun Chin ◽  
Zhipeng Cai ◽  
Frank Neumann
Keyword(s):  
Computer Vision ◽  
Robust Fitting ◽  
Fitting In

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision

Computer vision for industrial applications

1983 ◽  
Vol 2 (5) ◽  
pp. 130
Author(s):  
J.A. Losty ◽  
P.R. Watkins
Keyword(s):  
Computer Vision ◽  
Industrial Applications

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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