High-speed acousto-optic mapping modulator for the generalized Hough transform

Fully invariant generalized Hough transform by out-of-focus multiview sensing with pupil array

Recognition of geometrical shapes in real-time and fully invariant (i.e., invariant under changes in position, scale, and orientation) is a demanding task in automated image analysis. In particular, the generalized Hough transform (GHT) is a well-known algorithm for the recognition of complex patterns out of edge binary images even with disconnected boundaries or corrupted by noise. In this work we present a space multiplexed optical implementation of the GHT which, by exploiting the redundancy derived from multiview sensing of a two-dimensional image and its out- of-focus capture with an adequate pupil array, allows us to obtain in a single shot the GHT of this image invariant to target shift, scale, and orientation. Experimental validation of the working principle is presented, along with an assessment of the robustness of the system against noise in the input.

Image segmentation by nonlinear filtering of optical Hough transform

The identification and extraction (i.e., segmentation) of geometrical features is crucial in many tasks requiring image analysis. We present a method for the optical segmentation of features of interest from an edge enhanced image. The proposed method is based on the nonlinear filtering (implemented by the use of a spatial light modulator) of the generalized optical Hough transform and is capable of discriminating features by shape and by size. The robustness of the method against noise in the input, low contrast, or overlapping of geometrical features is assessed, and experimental validation of the working principle is presented.

Real-time pattern recognition using an optical generalized Hough transform

We present some pattern recognition applications of a generalized optical Hough transform and the temporal multiplexing strategies for dynamic scale and orientation-variant detection. Unlike computer-based implementations of the Hough transform, in principle its optical implementation does not impose restrictions on the execution time or on the resolution of the images or frame rate of the videos to be processed, which is potentially useful for real-time applications. Validation experiments are presented.

Optical implementation of the generalized Hough transform with totally incoherent light

The generalized Hough transform is a well-established technique for detecting complex shapes in images containing noisy or missing data. We present an efficient optical implementation of this transform using an electrical lens with variable focal length and a rotating pupil mask matching the pattern to be found. The proposed setup works under fully (i.e., both spatially and temporally) incoherent illumination and can handle orientation changes or scale variations in the pattern. Validation experiments showing its real-time application are presented.

Optical Computing: A 60-Year Adventure

Optical computing is a very interesting 60-year old field of research. This paper gives a brief historical review of the life of optical computing from the early days until today. Optical computing generated a lot of enthusiasm in the sixties with major breakthroughs opening a large number of perspectives. The period between 1980 and 2000 could be called the golden age with numerous new technologies and innovating optical processors designed and constructed for real applications. Today the field of optical computing is not ready to die, it has evolved and its results benefit to new research topics such as nanooptics, biophotonics, or communication systems.