Govender, NatashaWarrell, JTorr, PNicolls, F2014-10-092014-10-092014-08Govender, N., Warrell, J., Torr, P. and Nicolls, F. 2014. Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information. In: Advances in Computer Vision, Istanbul, 22-23 August 2014http://www.robots.ox.ac.uk/~tvg/publications/2014/shape_recongition.pdfhttp://hdl.handle.net/10204/7718Advances in Computer Vision, Istanbul, 22-23 August 2014. Abstract only added.Shape recognition is essential for robots to perform tasks in both human and industrial environments. Many algorithms have been developed for shape recognition with varying results. However, few of the proposed methods actively look for additional information to improve the initial shape recognition results. We propose an initial system which performs shape recognition using the euclidean distances of Fourier descriptors. To improve upon these results we build multinomial and Gaussian probabilistic models using the extracted Fourier descriptors and show how actively looking for cues using mutual information can improve the overall results. These probabilistic models achieve excellent results while significantly improving on the initial system.enShape recognitionHuman and industrial environmentsGaussian probabilistic modelsFourier descriptorsConference PresentationGovender, N., Warrell, J., Torr, P., & Nicolls, F. (2014). Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information. http://hdl.handle.net/10204/7718Govender, Natasha, J Warrell, P Torr, and F Nicolls. "Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information." (2014): http://hdl.handle.net/10204/7718Govender N, Warrell J, Torr P, Nicolls F, Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information; 2014. http://hdl.handle.net/10204/7718 .TY - Conference Presentation AU - Govender, Natasha AU - Warrell, J AU - Torr, P AU - Nicolls, F AB - Shape recognition is essential for robots to perform tasks in both human and industrial environments. Many algorithms have been developed for shape recognition with varying results. However, few of the proposed methods actively look for additional information to improve the initial shape recognition results. We propose an initial system which performs shape recognition using the euclidean distances of Fourier descriptors. To improve upon these results we build multinomial and Gaussian probabilistic models using the extracted Fourier descriptors and show how actively looking for cues using mutual information can improve the overall results. These probabilistic models achieve excellent results while significantly improving on the initial system. DA - 2014-08 DB - ResearchSpace DP - CSIR KW - Shape recognition KW - Human and industrial environments KW - Gaussian probabilistic models KW - Fourier descriptors LK - https://researchspace.csir.co.za PY - 2014 T1 - Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information TI - Probabilistic models for 2D active shape recognition using Fourier descriptors and mutual information UR - http://hdl.handle.net/10204/7718 ER -