scholarly journals Method for automatic localization of MR-visible markers using morphological image processing and conventional pulse sequences: Feasibility for image-guided procedures

2007 ◽  
Vol 26 (4) ◽  
pp. 1087-1096 ◽  
Author(s):  
Harald Busse ◽  
Robert Trampel ◽  
Wilfried Gründer ◽  
Michael Moche ◽  
Thomas Kahn
Keyword(s):  
Image Processing ◽  
Pulse Sequences ◽  
Image Guided ◽  
Morphological Image Processing ◽  
Morphological Image ◽  
Mr Visible ◽  
Automatic Localization

scholarly journals Robotic grasp detection based on image processing and random forest

2019 ◽  
Vol 79 (3-4) ◽  
pp. 2427-2446 ◽  
Author(s):  
Jiahao Zhang ◽  
Miao Li ◽  
Ying Feng ◽  
Chenguang Yang
Keyword(s):  
Image Processing ◽  
Random Forest ◽  
Main Idea ◽  
Morphological Image Processing ◽  
Segmentation Approach ◽  
Novel Objects ◽  
Set Up ◽  
The One ◽  
Objects Detection ◽  
Morphological Image

AbstractReal-time grasp detection plays a key role in manipulation, and it is also a complex task, especially for detecting how to grasp novel objects. This paper proposes a very quick and accurate approach to detect robotic grasps. The main idea is to perform grasping of novel objects in a typical RGB-D scene view. Our goal is not to find the best grasp for every object but to obtain the local optimal grasps in candidate grasp rectangles. There are three main contributions to our detection work. Firstly, an improved graph segmentation approach is used to do objects detection and it can separate objects from the background directly and fast. Secondly, we develop a morphological image processing method to generate candidate grasp rectangles set which avoids us to search grasp rectangles globally. Finally, we train a random forest model to predict grasps and achieve an accuracy of 94.26%. The model is mainly used to score every element in our candidate grasps set and the one gets the highest score will be converted to the final grasp configuration for robots. For real-world experiments, we set up our system on a tabletop scene with multiple objects and when implementing robotic grasps, we control Baxter robot with a different inverse kinematics strategy rather than the built-in one.

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