An improved neural network inversion technique that scales the search vector in accordance with the geometry of the problem has been developed. It searches in the direction of the gradient with a vector whose size is inversely related to the size of the gradient. To avoid unlimited growth of the search vector where the gradient is small, an upper bound is set on the size of the search vector. The network was trained by backpropagation and the training was halted when the network produced no error on the training set, where the output was categorized by binary thresholding. The results show the superior performance of the improved method. The technique was applied to automated cytology screening. A set of 400 object feature vectors randomly selected from a large database of 1929 feature vectors served as the initial training data.>

To simulate the edge perception ability of human eyes and detect scene edges from an image, context information must be used in the edge detection process. To accomplish the optimal use of the context, the authors introduce an edge detection scheme which uses the context of the whole image. The edge context for each pixel is the set of all row monotonically increasing paths through the pixel. The edge detector assigns a pixel that edge state having highest edge probability among all the paths. Experiments indicate the validity of the edge detector. Upon comparing the performance of the context dependent edge detector with the context free second directional derivative zero-crossing edge operator, the authors find that the context dependent edge detector is superior.>

A description is given of the relationship between morphologically filtering and then sampling vs. sampling, and then morphologically filtering in the sampled domain. The authors also describe the relationship between morphologically filtering vs. sampling, morphologically filtering in the sampled domain, and then reconstructing. Unlike the standard communication sampling theory where for appropriately low-pass filtered images there is commutivity between sampling and filtering, this is not the case for appropriately morphologically simplified images. The relationship which does exist shows that the commutivity holds to within one sampling interval distance in the unsampled domain and to within two sampling intervals in the sampled domain.>

A real-time scheme is introduced that meets a need for fast, reliable, multiple-target motion detection without accurate object velocity or structure measurements. A multiresolution approach decomposes images into a pyramid with several spatial frequency bands to selectively detect motion of interest. Multiple moving targets are detected using a multiple window, coarse-to-fine focus of attention scheme to reconstruct motion energy and search for targets. Real-time sensor motion (translation and rotation) compensations use hierarchical correlation and minimum perturbation. Results are shown for multiple moving targets in FLIR (forward-looking infrared) image sequences from both stationary and moving sensors.>

To obtain the optimal use of context, an edge detection scheme is introduced which uses the context of the whole image. The edge context for each pixel is the set of all row-monotonically-increasing paths through the pixel. The edge detector assigns to a pixel the edge state having the highest edge probability among all the paths. Experiments indicate the validity of the edge detector. A comparison with the performance of the context-free second-directional derivative zero-crossing edge operator shows that the context-dependent edge detector is superior.>

Edge operators based on gray-scale morphologic operations are introduced. These operators can be efficiently implemented in near real time machine vision systems which have special hardware support for gray-scale morphologic operations. The simplest morphologic edge detectors are the dilation residue and erosion residue operators. The underlying motivation for these and some of their combinations are discussed and justified. Finally, the blur-minimum morphologic edge operator is defined. Its inherent noise sensitivity is less than the dilation or the erosion residue operators. Some experimental results are provided to show the validity of these morphologic operators. When compared with the enhancement/thresholding edge detectors and the cubic facet second derivative zero-crossing edge operator, the results show that all the edge operators have similar performance when the noise is small. However, as the noise increases, the second derivative zero-crossing edge operator and the blur-minimum morphologic edge operator have much better performance than the rest of the operators. The advantage of the blur-minimum edge operator is that it is less computationally complex than the facet edge operator.