The enhanced vision system and recognition algorithm of ground targets images

2002 ◽  
Author(s):  
Xue Wenan
Keyword(s):  
Vision System ◽  
Recognition Algorithm

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.

A Micro-Computer Based Part/Tool Monitoring System for a Robotic Cell

1991 ◽  
Author(s):  
Saeid Motavalli ◽  
Behnam Bahr ◽  
Hamid M. Lankarani
Keyword(s):  
Tool Wear ◽  
Vision System ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
View Point ◽  
Robotic Cell ◽  
Specific Sequence ◽  
Tool Monitoring ◽  
Monitoring Camera ◽  
Wear Monitoring

Abstract This paper describes the elements of a unique robotic cell integrated with a vision system. The cell consists of a PUMA 560 robot, a vision system, and a conveyor belt. The robot end effector is a drill used in a drilling operation. The vision system has a two fold function: monitoring tool wear, and recognizing parts passing over the conveyor belt. Both tool monitoring and part recognition are performed using the vision system. The vision system is PC-based and uses two CCD video cameras. One camera is mounted vertically overlooking the conveyor belt, and the other camera is mounted horizontally for tool wear monitoring. Algorithms have been developed that interface the vision system with the robot. Whenever a part reaches the view point of the camera, an image of the part is captured. A recognition algorithm has been developed that recognizes the part and signals the robot to perform a specific sequence of operation on that part. The part is then moved to the drilling station where the PUMA robot perform the operation sequence according to the stored process plan. After each part is manufactured, the robot moves the drill to the view point of the tool monitoring camera, where an image of the drill head is captured. This image is then processed with the developed tool monitoring algorithm and tool wear is identified.

scholarly journals Multisensor-Based Autonomous Grasp Planning for Mobile Manipulator Navigation System with a Novel Soft Gripper

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Heng Zhang ◽  
Yingbai Hu ◽  
Jianghua Duan ◽  
Qing Gao ◽  
Langcheng Huo ◽  
...  
Keyword(s):  
High Performance ◽  
Visual Recognition ◽  
Vision System ◽  
Recognition Algorithm ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Success Rates ◽  
Complex Environments ◽  
Grasp Planning ◽  
Window Approach

Mobile manipulators are widely used in different fields for transferring and grasping tasks such as in medical assisting devices, industrial production, and hotel services. It is challenging to improve navigation accuracies and grasping success rates in complex environments. In this paper, we develop a multisensor-based mobile grasping system which is configured with a vision system and a novel gripper set in an UR5 manipulator. Additionally, an error term of a cost function based on DWA (dynamic window approach) is proposed to improve the navigation performance of the mobile platform through visual guidance. In the process of mobile grasping, the size and position of the object can be identified by a visual recognition algorithm, and then the finger space and chassis position can be automatically adjusted; thus, the object can be grasped by the UR5 manipulator and gripper. To demonstrate the proposed methods, comparison experiments are also conducted using our developed mobile grasping system. According to the analysis of the experimental results, the motion accuracy of the mobile chassis has been improved significantly, satisfying the requirements of navigation and grasping success rates, as well as achieving a high performance over a wide grasping size range from 1.7 mm to 200 mm.

scholarly journals Novel SGH Recognition Algorithm Based Robot Binocular Vision System for Sorting Process

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Xiaoyang Yu ◽  
Shuang Liu ◽  
Ming Pang ◽  
Jixun Zhang ◽  
Shuchun Yu
Keyword(s):  
Binocular Vision ◽  
Vision System ◽  
Texture Feature ◽  
Similarity Judgment ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Single Feature ◽  
Hu Moments ◽  
Feature Based ◽  
Binocular Vision System

To achieve automatic sorting on commodity trademarks, a binocular vision system has been constructed in this paper. By adjusting camera pose, this system can obtain greater shooting perspective. In order to improve sorting accuracy, a now SGH recognition method is proposed. SGH consists of spatial color histogram (Sfeature), gray level cooccurrence matrix (Gfeature), and Hu moments (H) feature, which represent color feature, texture feature, and shaper feature, respectively. Similarity judgment function is built by using SGH. The experimental results show that SGH algorithm has a higher visual accuracy compared to single feature based recognition method.

Research on the Visual Recognition Algorithm for Loading Robot Vision System

2012 ◽  
Vol 271-272 ◽  
pp. 1645-1648
Author(s):  
Yong Tao Yang ◽  
Huai Xing Wen
Keyword(s):  
Approximation Algorithm ◽  
Stereo Vision ◽  
Visual Recognition ◽  
Object Segmentation ◽  
Vision System ◽  
Robot Vision ◽  
Target Object ◽  
Recognition Algorithm ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

In order to study the questions about the recognition of relative position for the operating object and calculation of the object volume in the loading robot binocular stereo vision system. Based on the characteristics of the operating object, proposed the method that use of its vertices only to match the corresponding point of the camera imaging for location identification, also raised the approximation algorithm that firstly, cut up the whole, followed by calculate the volume of the various parts, then carry out the sum of the each segmentation volume. Experiments and analysis showed that the distance of camera and the object greater affect the visual system, less impact on the intensity of light；In the target object segmentation， the number of partition k=11 is better. Both methods produced the small errors for the visual recognition of the system, it can meet actual needs.

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.

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