Optical system to compute intensity moments: design

Position-invariant, rotation-invariant, and scale-invariant process for binary image recognition

A novel recognition process is presented that is invariant under position, rotation, and scale changes. The recognition process is based on the Fang-Häusler transform [Appl. Opt. 29, 704 (1990)] and is applied to the autoconvolved image, rather than to the image itself. This makes the recognition process sensitive not only to the image histogram but also to its detailed pattern, resulting in a more reliable process that is also applicable to binary images. The proposed recognition process is demonstrated, by use of a fast algorithm, on several types of binary images with a real transform kernel, which contains amplitude, as well as phase, information. Good recognition is achieved for both synthetic and scanned images. In addition, it is shown that the Fang-Hausler transform is also invariant under a general affine transformation of the spatial coordinates.

Invariant optical-pattern recognition based on a contour bank

We propose a new optical pattern-recognition method that is invariant to shift, rotation, and scale changes. By means of a zero-order circular-harmonic filter the input object can be transformed into a correlation peak whose outline retains a shape similar to the outline of the input object. After multilevel thresholding and binarization, a bank composed of many contours with different sizes is formed. The invariant recognition can be obtained by a small contour line being taking as a reference and its second-order circular-harmonic component being taken as a filter. In addition to the scale invariance the correlation peak is superior to the classical circular-harmonic filter and the phase-only circular-harmonic filter in terms of peak intensity, half-maximum width, and peak-to-sidelobe ratio.

Effective hybrid processor to compute image moments for pattern recognition

A hybrid optical-digital processor is presented for computing the invariant moments of images in real time, which consists of a holographic mask, two lenses, a charge-coupled-device detector, and a microcomputer. The processor is tested by inputting some roman letters, and the produced results show that the invariant moments of a letter are approximately independent of shift and rotation and that the moments are distinct with different letters.

Optical location of centroids of nonoverlapping objects

We present an optical technique for finding the centroids of nonoverlapping objects in a scene, thus locating the objects and preserving the underlying advantage of matched filtering approaches to pattern recognition. One is then free to extract any feature desired at these centroid locations rather than restricted to the matched filter test statistic. Furthermore, this allows general feature extraction avoiding prior scene segmentation into individual objects. The technique can also be used for tracking the motion of rigid or nonrigid objects. It consists of cross-correlating the input f(x,y) with a windowed version of the function x + iy and detecting the zeros of the magnitude of the resulting correlation. At these points the x and y first moments vanish. The window is selected based on the size and separation of the objects in a scene. Experimental verification as well as restrictions are also presented.

Effective optical processor for computing image moments at TV rate: use in handwriting recognition

An incoherent multiplexed optical processor is described that computes in real time the moments of images sensed by a TV camera and displayed on a monitor. The first ten moments are generated in parallel and are detected by a photodetector array. They are collected by a microcomputer for subsequent pattern recognition use. Calibration and resolution problems are discussed with regard to the effective 2% system accuracy. Its use in real-time processing of one hundred handwritten pages shows the processor to be a practical moment generator.