2 February 1998 Shift-, rotation-, and scale-invariant shape recognition system using an optical Hough transform
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We present a hybrid shape recognition system with an optical Hough transform processor. The features of the Hough space offer a separate cancellation of distortions caused by translations and rotations. Scale invariance is also provided by suitable normalization. The proposed system extends the capabilities of Hough transform based detection from only straight lines to areas bounded by edges. A very compact optical design is achieved by a microlens array processor accepting incoherent light as direct optical input and realizing the computationally expensive connections massively parallel. Our newly developed algorithm extracts rotation and translation invariant normalized patterns of bright spots on a 2D grid. A neural network classifier maps the 2D features via a nonlinear hidden layer onto the classification output vector. We propose initialization of the connection weights according to regions of activity specifically assigned to each neuron in the hidden layer using a competitive network. The presented system is designed for industry inspection applications. Presently we have demonstrated detection of six different machined parts in real-time. Our method yields very promising detection results of more than 96% correctly classified parts.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Volker R. Schmid, Volker R. Schmid, Gerhard Bader, Gerhard Bader, Ernst H. Lueder, Ernst H. Lueder, } "Shift-, rotation-, and scale-invariant shape recognition system using an optical Hough transform", Proc. SPIE 3306, Machine Vision Applications in Industrial Inspection VI, (2 February 1998); doi: 10.1117/12.301235; https://doi.org/10.1117/12.301235


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