Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

Ariel Fernández; José A. Ferrari

doi:10.1117/1.OE.56.5.053107

16 May 2017 Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

Ariel Fernández, José A. Ferrari

Author Affiliations +

Optical Engineering, Vol. 56, Issue 5, 053107 (May 2017). https://doi.org/10.1117/1.OE.56.5.053107

Abstract

Pattern recognition and feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital-only methods. We explore an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a pupil mask implemented on a high-contrast spatial light modulator for orientation/shape variation of the template. Real-time can also be achieved. In addition, by thresholding of the GHT and optically inverse transforming, the previously detected features of interest can be extracted.

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Ariel Fernández and José A. Ferrari "Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications," Optical Engineering 56(5), 053107 (16 May 2017). https://doi.org/10.1117/1.OE.56.5.053107

Received: 9 March 2017; Accepted: 27 April 2017; Published: 16 May 2017

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