A Fuzzy R Code Similarity Detection Algorithm

2014 ◽  
pp. 21-30 ◽  
Author(s):  
Maciej Bartoszuk ◽  
Marek Gagolewski
Keyword(s):  
Detection Algorithm ◽  
Similarity Detection

scholarly journals Improved Similarity Detection Algorithm of the Video Scene

2009 ◽  
Vol 9 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Ju-Won Yu ◽  
Jong-Weon Kim ◽  
Jong-Uk Choi ◽  
Kyoung-Yul Bae
Keyword(s):  
Detection Algorithm ◽  
Similarity Detection ◽  
Video Scene

Sequential similarity detection algorithm based on image edge feature

2013 ◽  
Vol 19 (1) ◽  
pp. 79-83 ◽  
Author(s):  
Guo-hong Ma ◽  
Cong Wang ◽  
Pei Liu ◽  
Shu-lin Zhu
Keyword(s):  
Detection Algorithm ◽  
Similarity Detection ◽  
Image Edge ◽  
Edge Feature

Robot View Navigation Based on Level-Divided Strategy

2013 ◽  
Vol 753-755 ◽  
pp. 3108-3111
Author(s):  
Yin Bing Li
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Image Matching ◽  
Search Strategy ◽  
Detection Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Exact Matching ◽  
Matching Accuracy ◽  
Similarity Detection ◽  
Time Requirements

In allusion to the colored image matching characteristic in the system of robot view navigation, SSDA (the sequential similarity detection algorithm) is improved and adaptive genetic algorithm is brought in; meanwhile, level-divided search strategy connective with rough and exact matching. The improved algorithm can enhance the image matching speed with no matching accuracy reduced, so that real-time requirements of robot view navigation can be met and robot view navigation will be of preferable robustness.

scholarly journals An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Luan Xidao ◽  
Xie Yuxiang ◽  
Zhang Lili ◽  
Zhang Xin ◽  
Li Chen ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Feature Detection ◽  
Feature Vector ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Local Feature ◽  
Image Similarity ◽  
Sift Feature ◽  
Similarity Detection ◽  
Overcomplete Dictionary

Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.

scholarly journals Image Processing Methods for Gesture-Based Robot Control

2021 ◽  
Vol 13 (1) ◽  
pp. 101-113
Author(s):  
Abdelouahab Zaatri ◽  
Hamama Aboud
Keyword(s):  
Image Processing ◽  
Feature Tracking ◽  
Robot Control ◽  
Feature Recognition ◽  
Detection Algorithm ◽  
Human Robot Interaction ◽  
Body Parts ◽  
Robot Interaction ◽  
Processing Methods ◽  
Similarity Detection

Abstract In this paper we discuss some image processing methods that can be used for motion recognition of human body parts such as hands or arms in order to interact with robots. This interaction is usually associated to gesture-based control. The considered image processing methods have been experienced for feature recognition in applications involving human robot interaction. They are namely: Sequential Similarity Detection Algorithm (SSDA), an appearance-based approach that uses image databases to model objects, and Kanade-Lucas-Tomasi (KLT) algorithm which is usually used for feature tracking. We illustrate the gesture-based interaction by using KLT algorithm. We discuss the adaptation of each of these methods to the context of gesture-based robot interaction and some of their related issues.

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