Eugene S. McVey

Multi-process constrained estimation

A method that maximizes the information flow through a constrained communications channel when it is desired to estimate the state of multiple nonstationary processes is described. The concept of a constrained channel is introduced as a channel that is not capable of transferring all of the information required. A measure of information is developed based on the estimation entropy utilizing the Kalman filter state estimator. It is shown that this measure of information can be used to determine which process to observe in order to maximize a measure of global information flow. For stationary processes, the sampling sequence can be computed a priori, but nonstationary processes require real-time sequence computation. >

Machine Vision Applied to Vehicle Guidance

Research on the semiautonomous operation of mobile robots in typical pathways is described. The image of the pathway will consist of two nearly vertical lines bounding a region with little texture (the pathway) after correction for perspective. In order to identify pathway boundaries, regions in the image space are examined using an edge detection algorithm, edges between regions are determined by the algorithm, and those corresponding to straight or nearly straight lines with large slope (path boundaries) are identified by means of the Hough transform. Once the path boundaries are identified, the horizontal distance from camera to road edge is determined. Next, a method to detect cubics in the roadway (i.e., obstacles) is presented. The region of interest in the roadway (from the camera to some predetermined distance in front of it) is known from the path boundary algorithm. The interior of this region is examined for edges. If edges are detected, it means that obstacles or shadows are present. A method to separate obstacles from shadows using stere vision is then presented.

Some Accuracy and Resolution Aspects of Computer Vision Distance Measurements

The distance between an object and stereo vision sensors can be measured using image processing and known system parameters. A detailed distance measurement synthesis procedure to meet system specifications is presented and illustrated with an example. An error analysis shows that error is proportional to distance. System parameters such as separation between sensor elements, sensor focal length, and sensor array dimensions are related in the design and error equations presented. The main desired design goal is to establish the smallest image sensor array size which will meet system operating specifications. Minimum and maximum distance, object height, optic parameters, scene shift, and sensor array parameters are related.

Improvement of Position and Velocity Detecting Accuracy by Signal Perturbation

A method is presented for increasing the accuracy of position and velocity detecting systems which make use of pattern recognition principles. The basic limitation of these systems is due to the binary nature of receptor elements which causes the receptor to have a nonlinear transfer characteristic.

Sensing Error for a Mobile Robot Using Line Navigation

The use of a contrasting line for the visual navigation of autonomous mobile robots in a factory environment is developed. Minimum and maximum linewidths are determined analytically by considering sensor geometry, field of view, and error conditions present in the system. The effects of these error conditions on the width of the line, as seen in the image plane, determines the optimal linewidth. Numerical examples using typical sensor parameters are given.

Experimental position and ranging results for a mobile robot

Experimental results are presented that support theory published in the literature concerning the use of a navigation line for the guidance of mobile robots. A method for the determination of a robots position is developed. A specialized edge operator, which aids in the segmentation of a navigation line from an image of a robots environment, is given. Use of this specialized edge operator in conjunction with the Hough transform is also presented. These methods are used to verify the analytical results given previously. Comparisons are made between experimental data and expected results as a function of various system parameters. Real-time implementation of these methods is considered.

A multilayered self-organizing artificial neural network for invariant pattern recognition

An artificial neural network that self-organizes to recognize various images presented as a training set is described. One application of the network uses multiple functionally disjoint stages to provide pattern recognition that is invariant to translations of the object in the image plane. The general form of the network uses three stages that perform the functionally disjoint tasks of preprocessing, invariance, and recognition. The preprocessing stage is a single layer of processing elements that performs dynamic thresholding and intensity scaling. The invariance stage is a multilayered connectionist implementation of a modified Walsh-Hadamard transform used for generating an invariant representation of the image. The recognition stage is a multilayered self-organizing neural network that learns to recognize the representation of the input image generated by the invariance stage. The network can successfully self-organize to recognize objects without regard to the location of the object in the image field and has some resistance to noise and distortions. >

A single-pixel target detection and tracking system

The authors present a pipeline method for detection and tracking of pixel-sized moving targets with unknown trajectories from a time sequence of highly noisy images. The pipeline target detection algorithm uses the temporal continuity of the smooth trajectories of moving targets and successfully detects and simultaneously tracks all the target trajectories by mapping them from the image sequence onto a single target frame. The pipeline method overcomes the constraint of a straight line trajectory that most other algorithms require for similar tasks. The algorithm is a complete parallel distributed processing type process, and therefore is highly time-efficient-ideal for real-time detection and tracking of arbitrary target trajectories in high-noise environments.<<ETX>>

A Method for the Fast Approximate Solution of Large Prime Implicant Charts

A method is presented which uses easily calculated probabilities to determine complete covers of prime implicant charts. Apparently the underlying principle of incomplete branching has not been applied previously to prime implicant charts. This principle differs in that branching is not carried out to complete covers, but only to a specified level, at which point each position is evaluated. All positions are then mimimaxed to the point of branching to choose the best branch. The method is easy to program and requires relatively little computer time to determine the cover. Time savings of up to 98 percent have been realized with an increase in the cost of the cover of less than 2 percent over conventional minimization methods. The method can be applied directly to any prime implicant chart, or can be used as a substitute for complete branching in charts where the application of dominance produces a cyclic chart.

Modified neocognitron with position normalizing preprocessor for translation invariant shape recognition

A pattern-recognition system that self-organizes to recognize objects by shape as part of an integrated visual network (IVN) for autonomous flight control is presented. The system uses a multistaged hierarchical neural network that exhibits insensitivity to the location of the object in the visual field. The networks two layers perform the functionally disjoint tasks of invariance (position normalization) and recognition (identification of the shape). The invariance stage is a multilayered neural network implementation of a modified Walsh-Hadamard transform that generates a representation of the object that is invariant with respect to the objects position. The recognition stage is a modified version of the Fukushima neocognitron that identifies the position-normalized representation by shape. The inclusion of the invariance stage allows reduction of the massively replicated processing structures used for translation invariance in the neocognition. This system offers roughly the same translation invariance capabilities as the neocognitron, with a dramatic reduction in the number of elements and the networks interconnection complexity