Resource Efficient Tool Recognition Approach Using Modified Shape Feature Extraction Technique for Robotic Vision Systems
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, https://doi.org/10.14445/22315381/IJETT-V69I9P219
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
 F. Lahajnar, R. Bernard, F. Pernuš, and S. Kova?i?., Machine vision system for inspecting electric plates, Comput. Ind., 47(1) (2002) 113–122, DOI: 10.1016/S0166-3615(01)00134-8
 S. Sheth, R. Kher, R. Shah, P. Dudhat, and P. Jani., vision Automatic Sorting System Using Machine vision., Academia.Edu, (2010) (2016) DOI: 10.13140/2.1.1432.1448
 D. Di Paola, A. Milella, G. Cicirelli, and A. Distance., An autonomous mobile robotic system for surveillance of indoor environments., Int. J. Adv. Robot. Syst., 7(1) (2010) 19–26 DOI: 10.5772/7254
 J. L. Shih and L. H. Chen., Colour image retrieval based on primitives of color moments., IEE Proc. Vision, Image Signal Process., 149(6) (2002) 370–374 2002, DOI: 10.1049/ip-vis:20020614.
 R. M. Madireddy, P. S. V. Gottumukkala, P. D. Murthy, and S. Chittipothula., A modified shape context method for shape-based object retrieval., Springerplus, 3(1) (2014) 1–12 ., DOI: 10.1186/2193-1801-3-674
 A. N., I. E. Rubert, K. M.S., and F. G., Shape retrieval using triangle-area representation and dynamic space warping., Pattern Recognit., 40(7) (2007) 1911–1920, DOI: https://doi.org/10.1016/j.patcog.2006.12.005
 M. M. Bronstein and I. Kokkinos., Scale-invariant heat kernel signatures for non-rigid shape recognition., Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., (2010) 1704–1711 DOI: 10.1109/CVPR.2010.5539838
 J. Canny., A Computational Approach to Edge Detection., IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8(6) (1986) 679–698 DOI: 10.1109/TPAMI.1986.4767851
 C. Direkolu and M. S. Nixon., Shape classification via image-based multiscale description., Pattern Recognit., 44(9) (2011) 2134–2146 DOI : 10.1016/j.patcog.2011.02.016
 G. C. H. Chuang and C. C. J. Kuo., Wavelet descriptor of planar curves: Theory and applications., IEEE Trans. Image Process., 5(1) (1996) 56–70 DOI: 10.1109/83.48167.
 M. R. Daliri and V. Torre., Robust symbolic representation for shape recognition and retrieval, Pattern Recognit., 41(5) (2008) 1782–1798 DOI: 10.1016/j.patcog.2007.10.020.
 S. Loncaric., A survey of shape analysis techniques, Pattern Recognit., 31(8) (1998) 983–1001 DOI: 10.1016/S0031-2023(97)00122-2
 D. Zhang and G. Lu., Review of shape representation and description techniques., Pattern Recognit., 37(1) (2014) 1–19, DOI : 10.1016/j.patcog.2003.07.008
 C. Zhu, H. Zhou, R. Wang, and J. Guo, “A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features, IEEE Trans. Geosci. Remote Sens., 48(9) (2010) 3446–3456 DOI: 10.1109/TGRS.2010.2046330.
 Y. Zhu and C. Huang., An Improved Median Filtering Algorithm for Image Noise Reduction., Phys. Procedia, 25(2012) 609–616 DOI: 10.1016/j.phpro.2012.03.133
 D. Zhang and G. Lu., Generic Fourier descriptor for shape-based image retrieval., Proc. - IEEE Int. Conf. Multimed. Expo, ICME 1(0) (2002)425–428 2002,DOI: 10.1109/ICME.2002.1035809.
 M. Mancini, H. Karaoguz, E. Ricci, P. Jensfelt, and B. Caputo., Kitting in the Wild through Online Domain Adaptation., IEEE Int. Conf. Intell. Robot. Syst., (2018) 1103–1109., DOI: 10.1109/IROS.2018.8593862.
 H. Karaoguz and P. Jensfelt., Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization, (2018) DOI: 1804.03905.
 D. S. Guru, R. Dinesh, and P. Nagabhushan., Boundary based corner detection and localization using new ‘cornerity’ index: A robust approach., Proc. - 1st Can. Conf. Comput. Robot Vis., (1997) (2004) 417–423,DOI: 10.1109/CCCRV.2004.1301477.