Authors:
Hossam Fraihat
;
Kurosh Madani
and
Christophe Sabourin
Affiliation:
University Paris-Est Creteil and Senart-FB Institute of Technology, France
Keyword(s):
Visual distance evaluation, Soft-Computing, Kinect, Visual information processing, Machine learning, ANFIS, MLP, SVR, Bilinear interpolation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Processing and Artificial Vision Applications
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper deals with visual evaluation of object distances using Soft-Computing based approaches and
pseudo-3D standard low-cost sensor, namely the Kinect. The investigated technique points toward robots’
vision and visual metrology of the robot’s surrounding environment. The objective is providing the robot
the ability of evaluating distances between objects in its surrounding environment. In fact, although
presenting appealing advantages, the Kinect has not been designed for metrological aims. The investigated
approach offers the possibility to use this low-cost pseudo-3D sensor for distance evaluation avoiding 3D
feature extraction and thus exploiting the simplicity of only 2D image’ processing. Experimental results
show the viability of the proposed approach and provide comparison between different machine learning
techniques as Adaptive-network-based fuzzy inference (ANFIS), Multi-layer Perceptron (MLP), Support
vector regression (SVR), Bilinear interpolation.