Research on hand recognition system for service robot

2014 ◽  
Vol 27 (4) ◽  
pp. 305-311 ◽  
Author(s):  
Xiaohua Wang ◽  
Caishun Li ◽  
Min Hu ◽  
Hong Zhu
Keyword(s):  
Recognition System ◽  
Service Robot ◽  
Hand Recognition

Object detection and mapping for service robot tasks

Robotica ◽  
2007 ◽  
Vol 25 (2) ◽  
pp. 175-187 ◽  
Author(s):  
Staffan Ekvall ◽  
Danica Kragic ◽  
Patric Jensfelt
Keyword(s):  
Object Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Semantic Knowledge ◽  
Service Robot ◽  
Map Building ◽  
Extensive Evaluation ◽  
Localization And Mapping ◽  
Different Types ◽  
Robot Tasks

SUMMARYThe problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

scholarly journals Online Training for Face Recognition System Using Improved PCA

2011 ◽  
Vol 2 (2) ◽  
pp. 1303
Author(s):  
Widodo Budiharto
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Indoor Environment ◽  
Online Training ◽  
Recognition System ◽  
Service Robot ◽  
Challenging Problem ◽  
Face Database ◽  
Face Images ◽  
Face Recognition System

The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals. Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, this research proposes an online face recognition system using improved PCA for a service robot in indoor environment based on stereo vision. Tested images are improved by generating random values for varying the intensity of face images. A program for online training is also developed where the tested images are captured real-time from camera. Varying illumination in tested images will increase the accuracy using ITS face database which its accuracy is 95.5 %, higher than ATT face database’s as 95.4% and Indian face database’s as 72%. The results from this experiment are still evaluated to be improved in the future.

Door Knob Hand Recognition System

2017 ◽  
pp. 259-284
Author(s):  
David Zhang ◽  
Guangming Lu ◽  
Lei Zhang
Keyword(s):  
Recognition System ◽  
Hand Recognition

scholarly journals Body Gestures Recognition System to Control a Service Robot

2017 ◽  
Vol 9 (9) ◽  
pp. 69-76 ◽  
Author(s):  
José L. Medina-Catzin ◽  
◽  
Anabel Martin-Gonzalez ◽  
Carlos Brito-Loeza ◽  
Victor Uc-Cetina
Keyword(s):  
Recognition System ◽  
Service Robot

scholarly journals Hand Recognition Using Thermal Image and Extension Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Meng-Hui Wang
Keyword(s):  
Neural Network ◽  
Infrared Camera ◽  
Recognition System ◽  
Thermal Image ◽  
Recognition Method ◽  
Good Tolerance ◽  
Personal Recognition ◽  
Recognition Systems ◽  
Hand Recognition ◽  
High Degree

Hand recognition is one of the popular biometry methods for access control systems. In this paper, a new scheme for personal recognition using thermal images of the hand and an extension neural network (ENN) is presented. The features of the recognition system are extracted from gray level hand images, which are taken by an infrared camera. The main advantage of the thermal image is that it can reduce errors and noise in the features extracted stage, which is most important to increase the accuracy of recognition systems. Moreover, a new recognition method based on the ENN is proposed to perform the core functions of the hand recognition system. The proposed ENN-based recognition method also permits rapid adaptive processing for a new pattern, as it only tunes the boundaries of classified features or adds a new neural node. It is feasible to implement the proposed method on a Microcomputer for a portable personal recognition device. From the tested examples, the proposed method has a significantly high degree of recognition accuracy and shows good tolerance to errors added.

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