The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

Boris Alpatov; Pavel Babayan; Maksim Ershov; Valery Strotov

doi:10.1117/12.2241091

24 October 2016 The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

Boris Alpatov, Pavel Babayan, Maksim Ershov, Valery Strotov

Author Affiliations +

Proceedings Volume 10007, High-Performance Computing in Geoscience and Remote Sensing VI; 100070A (2016) https://doi.org/10.1117/12.2241091
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Conference Presentation

Citation Download Citation

Boris Alpatov, Pavel Babayan, Maksim Ershov, and Valery Strotov "The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system", Proc. SPIE 10007, High-Performance Computing in Geoscience and Remote Sensing VI, 100070A (24 October 2016); https://doi.org/10.1117/12.2241091

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