scholarly journals Distributed Architecture for Intelligent Robotic Assembly Part III: Design of the Invariant Object Recognition System

10.5772/5056 ◽  
2006 ◽  
Author(s):  
Mario Pena ◽  
Ismael Lopez
Keyword(s):  
Object Recognition ◽  
Recognition System ◽  
Robotic Assembly ◽  
Distributed Architecture ◽  
Invariant Object Recognition

SYMMETRY AXIS BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING

2009 ◽  
Vol 19 (01) ◽  
pp. 25-42 ◽  
Author(s):  
MASHUD HYDER ◽  
MD. MONIRUL ISLAM ◽  
M. A. H. AKHAND ◽  
KAZUYUKI MURASE
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Symmetry Axis ◽  
Recognition Performance ◽  
Recognition System ◽  
New Approach ◽  
Invariant Object Recognition ◽  
Different Shapes ◽  
Axis Of Symmetry ◽  
Concentric Circles

This paper presents a new approach, known as symmetry axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the axis of symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Invariant object recognition using a neural template classifier

1996 ◽  
Vol 14 (7) ◽  
pp. 473-483 ◽  
Author(s):  
H.W. Tang ◽  
V. Srinivasan ◽  
S.H. Ong
Keyword(s):  
Object Recognition ◽  
Invariant Object Recognition
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