Knowledge-based machine vision systems for space station automation

Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed

A graph theoretic approach to scene matching

The ability to match two scenes is a fundamental requirement in a variety of computer vision tasks. A graph theoretic approach to inexact scene matching is presented which is useful in dealing with problems due to imperfect image segmentation. A scene is described by a set of graphs, with nodes representing objects and arcs representing relationships between objects. Each node has a set of values representing the relations between pairs of objects, such as angle, adjacency, or distance. With this method of scene representation, the task in scene matching is to match two sets of graphs. Because of segmentation errors, variations in camera angle, illumination, and other conditions, an exact match between the sets of observed and stored graphs is usually not possible. In the developed approach, the problem is represented as an association graph, in which each node represents a possible mapping of an observed region to a stored object, and each arc represents the compatibility of two mappings. Nodes and arcs have weights indicating the merit or a region-object mapping and the degree of compatibility between two mappings. A match between the two graphs corresponds to a clique, or fully connected subgraph, in the association graph. The task is to find the clique that represents the best match. Fuzzy relaxation is used to update the node weights using the contextual information contained in the arcs and neighboring nodes. This simplifies the evaluation of cliques. A method of handling oversegmentation and undersegmentation problems is also presented. The approach is tested with a set of realistic images which exhibit many types of sementation errors

Image Segmentation using Two-Layer Pulse Coupled Neural Network with Inhibitory Linking Field

For over a decade, the Pulse Coupled Neural Network(PCNN) based algorithms have been used for imagesegmentation. Though there are several versions of the PCNNbased image segmentation methods, almost all of them use singlelayerPCNN with excitatory linking inputs. There are fourmajor issues associated with the single-burst PCNN which needattention. Often, the PCNN parameters including the linkingcoefficient are determined by trial and error. The segmentationaccuracy of the single-layer PCNN is highly sensitive to the valueof the linking coefficient. Finally, in the single-burst mode,neurons corresponding to background pixels do not participatein the segmentation process. This paper presents a new 2-layernetwork organization of PCNN in which excitatory andinhibitory linking inputs exist. The value of the linkingcoefficient and the threshold signal at which primary firing ofneurons start are determined directly from the image statistics.Simulation results show that the new PCNN achieves significantimprovement in the segmentation accuracy over the widelyknown Kuntimad’s single burst image segmentation approach.The two-layer PCNN based image segmentation methodovercomes all three drawbacks of the single-layer PCNN

A hardware implementation of a relaxation algorithm to segment images

Relaxation labelling is a mathematical technique frequently applied in image processing algorithms. In particular, it is extensively used for the purpose of segmenting images. The paper presents a hardware implementation of a segmentation algorithm, for images consisting of two regions, based on relaxation labelling. The algorithm determines, for each pixel, the probability that it should be labelled as belonging to a particular region, for all regions in the image. The label probabilities (labellings) of every pixel are iteratively updated, based on those of the pixel's neighbors, until they converge. The pixel is then assigned to the region correspondent to the maximum label probability. The system consists of a control unit and of a pipeline of segmentation stages. Each segmentation stage emulates in the hardware an iteration of the relaxation algorithm. The design of the segmentation stage is based on commercially available digital signal processing integrated circuits. Multiple iterations are accomplished by stringing stages together or by looping the output of a stage, or string of stages, to its input. The system interfaces with a generic host computer. Given the modularity of the architecture, performance can be enhanced by merely adding segmentation stages