Object recognition algorithm based on inexact graph matching and its application in a color vision system for recognition of electronic components on PCBs

1997 ◽  
Author(s):  
Natalia H. Kroupnova ◽  
Maarten J. Korsten
Keyword(s):  
Object Recognition ◽  
Color Vision ◽  
Graph Matching ◽  
Vision System ◽  
Recognition Algorithm ◽  
Electronic Components ◽  
Inexact Graph Matching

Research of Object Recognition Algorithm Based on Variable Illumination

2011 ◽  
Vol 255-260 ◽  
pp. 2096-2100
Author(s):  
Song Hao Piao ◽  
Qiu Bo Zhong ◽  
Shu Ai Wang ◽  
Xian Feng Wang
Keyword(s):  
Image Segmentation ◽  
Object Recognition ◽  
Edge Detection ◽  
Color Image ◽  
Vision System ◽  
Recognition Algorithm ◽  
Color Image Segmentation ◽  
Color Information ◽  
Shape Information ◽  
Variable Illumination

The robot vision system is the critical component of the soccer robot, in football competition, robot perceive the most of the information from the vision system. Because of the variable illumination conditions, the traditional image segmentation method based on color information is not satisfactory. Based on the color information and shape information of the object, this paper proposes a object recognition algorithm that combine color image segmentation with edge detection. This algorithm implement image segmentation use color information in the HSV color space obtain the pixel of the object, then use this pixel implement edge detection to recognize the object. Experiments show that this algorithm can recognize the object exactly in the different illumination conditions, satisfy the requirement of the competition.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

scholarly journals Small Object Recognition Algorithm of Grain Pests Based on SSD Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 43202-43213
Author(s):  
Zongwang Lyu ◽  
Huifang Jin ◽  
Tong Zhen ◽  
Fuyan Sun ◽  
Hui Xu
Keyword(s):  
Object Recognition ◽  
Feature Fusion ◽  
Recognition Algorithm ◽  
Small Object
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