Resource Efficient Tool Recognition Approach Using Modified Shape Feature Extraction Technique for Robotic Vision Systems

Resource Efficient Tool Recognition Approach Using Modified Shape Feature Extraction Technique for Robotic Vision Systems

© 2021 by IJETT Journal
Volume-69 Issue-9
Year of Publication : 2021
Authors : Navya Mohan, Athul Thomas, James Kurian
DOI :  10.14445/22315381/IJETT-V69I9P219

How to Cite?

Navya Mohan, Athul Thomas, James Kurian, "Resource Efficient Tool Recognition Approach Using Modified Shape Feature Extraction Technique for Robotic Vision Systems," International Journal of Engineering Trends and Technology, vol. 69, no. 9, pp. 153-160, 2021. Crossref,

This paper discusses a resource-efficient hardware realization approach employing an improved shape feature vector for object recognition in the field of vision robotics. A modified shape signature method with less computational power is presented to analytically select the unique shape boundary points for 1D representation of 2D images. The robust real-time object recognition system is implemented using MATLAB and tested with the images of the KTH database for hand tools, which perform object localization irrespective of illumination and background settings. Results are further verified using an image set of robotic handling mechanical components, which are captured under their own experimental setup. Feature extraction utilizing Fast Fourier Transform and classification of the extracted vectors are analyzed.
To discriminate between mechanical parts, containing nine classes and consists of fifty-four images, each captured under different background. Shape feature using FFT offers translation, rotation, and scale-invariant attributes, while improved shape signature ensures less dedicated hardware resource utilization, thus making the robotic vision module compact. The experimental results demonstrated that the proposed vision-based robotic system achieves an overall recognition accuracy with optimum utilization of resources. Thereby computational power required is less to an acceptable degree, and hence realization made easy.

Real-Time Robotic Vision Systems, Tool recognition, Feature extraction, Shape Signature, Machine Vision

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