Morphological Image Processing: Architecture and VLSI design

1992 ◽  
Author(s):  
Petrus Paulus Jonker
Keyword(s):  
Image Processing ◽  
Vlsi Design ◽  
Morphological Image Processing ◽  
Morphological Image ◽  
Processing Architecture

scholarly journals A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Emerson Carlos Pedrino ◽  
José Hiroki Saito ◽  
Valentin Obac Roda
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Genetic Programming ◽  
Color Image ◽  
Reconfigurable Architecture ◽  
Programming Approach ◽  
Practical Applications ◽  
Morphological Image Processing ◽  
Morphological Image ◽  
Processing Architecture

Mathematical morphology supplies powerful tools for low-level image analysis. Many applications in computer vision require dedicatedhardwarefor real-time execution. The design of morphological operators for a given application is not a trivial one. Genetic programming is a branch of evolutionary computing, and it is consolidating as a promising method for applications of digital image processing. The main objective of genetic programming is to discover how computers can learn to solve problems without being programmed for that. In this paper, the development of an original reconfigurable architecture using logical, arithmetic, and morphological instructions generated automatically by a genetic programming approach is presented. The developed architecture is based on FPGAs and has among the possible applications, automatic image filtering, pattern recognition and emulation of unknown filter. Binary, gray, and color image practical applications using the developed architecture are presented and the results are compared with similar techniques found in the literature.

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.

Sign in / Sign up

Export Citation Format

Share Document