Searching Ball Around ROI to Increase Computational Processing of Detection

Dikarinata, Rangga; Wibowo, Iwan Kumianto; Bachtiar, Mochamad Mobed; Haq, Muhammad Abdul

doi:10.1109/ies50839.2020.9231903

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input image is filtered by two methods, HSV filtering and Morphology Filtering. First, the image coloring format is converted from RGB into HSV format as follows [32] image, the HSV format image is filtered, and it focuses on the ball's color in binary format like in [33]. The morphology filter intends to increase (dilate/open operation) or reduce (erosion/close operations) the selected structure area as follows in [32].…”

Section: F Computer Visionmentioning

confidence: 99%

Object Position Estimation based on Dual Sight Perspective Configuration

et al. 2021

View full text Add to dashboard Cite

Development of the coordination system requires the dataset because the dataset could provide information around the system that the coordination system can use to make decisions. Therefore, the capability to process and display data-related positions of objects around the robots is necessary. This paper provides a method to predict an object’s position. This method is based on the Indoor Positioning System (IPS) idea and object position estimation with the multi-camera system (i.e., stereo vision). This method needs two input data to estimate the ball position: the input image and the robot’s relative position. The approach adopts simple and easy calculation technics: trigonometry, angle rotations, and linear function. This method was tested on a ROS and Gazebo simulation platform. The experimental result shows that this configuration could estimate the object’s position with Mean Squared Error was 0.383 meters. Besides, R squared distance calibration value is 0.9932, which implies that this system worked very well at estimating an object’s position.

show abstract

Section: F Computer Visionmentioning

confidence: 99%

Object Position Estimation based on Dual Sight Perspective Configuration

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to set the ROI to extract the area of focus in the image before the subsequent image processing [16]. In machine vision, the form of ROI usually includes rectangles, circles, ellipses, irregular polygons, and so forth [17]. However, if we use a closed graph, such as a rectangle, to select the ROI, we still retain a large amount of useless information inside the target.…”

Section: Region Of Interestmentioning

confidence: 99%

Research on Target Localization Method of CRTS-III Slab Ballastless Track Plate Based on Machine Vision

Liu

Zheng

et al. 2021

Electronics

View full text Add to dashboard Cite

In the construction of high-speed railway infrastructure, a CRTS-III slab ballastless track plate has been widely used. Anchor sealing is an essential step in the production of track plates. We design a novel automated platform based on industrial robots with vision guidance to improve the automation of a predominantly human-powered anchor sealing station. This paper proposes a precise and efficient target localization method for large and high-resolution images to obtain accurate target position information. To accurately update the robot’s work path and reduce idle waiting time, this paper proposes a low-cost and easily configurable visual localization system based on dual monocular cameras, which realizes the acquisition of track plate position information and the correction of position deviation in the robot coordinate system. We evaluate the repeatable positioning accuracy and the temporal performance of the visual localization system in a real production environment. The results show that the repeatable positioning accuracy of this localization system in the robot coordinate system can reach ±0.150 mm in the x- and y-directions and ±0.120° in the rotation angle. Moreover, this system completes two 18-megapixel image acquisitions, and the whole process takes around 570 ms to meet real production needs.

show abstract

Searching Ball Around ROI to Increase Computational Processing of Detection

Cited by 2 publications

References 5 publications

Object Position Estimation based on Dual Sight Perspective Configuration

Object Position Estimation based on Dual Sight Perspective Configuration

Research on Target Localization Method of CRTS-III Slab Ballastless Track Plate Based on Machine Vision

Contact Info

Product

Resources

About