Roland H. Schaefer

Optical hit–miss morphological transform

We present an optical correlator implementation of the morphological hit-miss transform. This provides improved recognition of objects in clutter compared with standard optical pattern-recognition correlator techniques. The hit-miss transform is modified to use rank-order filtering since this gives better performance in noise and clutter. By varying the correlation plane threshold, we can perform dilations, rank-order filters, and erosions on the same multifunctional coherent optical correlator system. We quantify the thresholds required for generic object part recognition and provide simulated and optical laboratory data.

Optical correlation filter fusion for object detection

We consider the detection of candidate objects (regions of interest) in a scene containing high clutter, multiple objects in different classes, independent of aspect view, with hot, cold, bimodal, and partial object variations and with high and low contrast targets. We use three different filters with each designed to produce high probability of detection (PD). We fuse the results from different outputs to reduce the probability of false alarms (PFA). All filters are realizable on an optical correlator.

Nonlinear optical hit–miss transform for detection

Morphological processing involves nonlinear low-level image-processing operations that can be realized on optical processors. Amodified version of the hit-miss morphological transform is described for object detection. Simulation results and optical laboratory realizations are presented. Some of the simple filters required can be realized as ternary-phase-amplitude optical filters.

Optical implementation of gray-scale morphology

There is much work concerning morphological image processing, both binary and gray scale. Almost all implementations to date are performed electronically on standard computers, specialized processors, or specialized hardware. Prior work has described implementation of binary morphology on an optical processor, as well as indicating the relative merits of using an optical system. However, the restriction to binary morphology on an optical system has required that gray scale problems be reduced to binary morphology solutions using judiciously chosen binarization thresholds. This paper describes how gray scale morphology can be implemented on an optical correlator system using a threshold decomposition algorithm. A series of thresholded binary correlations are formed optically and summed on a CCD detector array or spatial light modulator, to produce the output morphologically processed gray scale image. The speed this optical system is much faster than 30 gray scale images per second. The details of the architecture used to implement threshold decomposition on an optical system is described, and issues relating to the implementation of binary morphology on an optical system are discussed. The threshold decomposition algorithm is discussed with attention to ways to reduce the number of intermediate processing steps required.

Optical morphological processors: gray scale with binary structuring elements, detection, and clutter reduction

We consider morphological processing for clutter reduction and object detection. For detection, we compare a binary and gray-scale Hit-Miss Transform and find that the binary operator is preferable. For clutter reduction, we find gray-scale morphology to be preferable. We present a new gray-scale clutter reduction morphological algorithm for low clutter cases and a new algorithm for high clutter cases. In all morphological processing, we find binary structuring elements to be adequate; this is very attractive for our gray-scale morphology decomposition algorithm and its optical implementation.

Morphological processing to reduce shading and illumination effects

Optical morphological correlators are considered for shading and illumination problems that arise in robotics and product inspection and for contrast problems that arise in infrared (IR) imagery for automatic target recognition (ATR).

Multilevel hit-miss transform for object detection

The rank-order hit-miss transform (HMT) filter is a significant new advancement in pattern recognition. We detail a new version of our HMT algorithm used for detection, the filter parameters used, and detection (PD) and false alarm (PFA) results. In detection, these filters are required to locate all objects in a scene with clutter present. This must be achieved for objects in multiple different classes, with 3-D distortion and contrast differences present. Thus, they represent considerably new image processing filters.

Correlation filter fusion for detection: morphological, wavelet, and Gabor methods

We consider the detection of candidate objects (regions of interest) in a scene containing high clutter, multiple objects in different classes, independent of aspect view, with hot/cold/bimodal/partial object variations, and with low contrast targets. We use three different filters with each designed to produce high probability of detection (PD). We fuse the results from different outputs to reduce false alarms (PFA). All filters are realizable on a correlator.

Optical gray-scale morphology for target detection

The use of the optical hit-or-miss (HOM) morphological operation for target detection in automatic target detection (ATR) imagery is considered. Formal gray-scale morphology can be implemented with a number of binary morphological operations. Each binary morphological operation can be implemented on a binary optical correlator at very high speeds with very large structuring elements (SEs). A modified HOM algorithm is advanced and demonstrated on ATR gray-scale imagery for target detection. The algorithm is very attractive for implementation on a binary optical correlator.

Optical morphological processing of disordered structures

Morphological processing combined with other techniques is used to analyze disordered structures. Disordered structures can consists of a number of objects of a given shape (the task is to determine the number of objects and their length and orientation distribution) or texture (this occurs when the number of particles is large) and the task is to describe the texture and to discriminate different textures. To solve such problems, we employ a new morphological skeletonization algorithm, a Hough Transform together with morphological operations, morphological erosions with directional structuring elements, and develop new parameters to describe and distinguish textures. Our algorithms can be implemented in digital or optical processors.