A vision system capable of extracting features from a semi-structured environment for vehicle guidance is described in this paper. The system is primarily used for road following via the detection of mud tracks in a tropical environment. The scene captured by a CCD colour camera is digitised into 24-bit colour images with a resolution of 320x240 pixels. Partitioning of the scene into road and non-road areas is based on the results of a colour image segmentation algorithm applied to these images. The RGB colour images from the camera are converted to HSI format. Training samples of road and non-road features of the terrain to be explored, stored in a database, are used to classify blocks of pixels using only the hue information content of the images. A Bayesian classifier in conjunction with a smooth thresholding function is used for the segmentation algorithm on a per block basis. This approach results in the recognition of traversable areas, particularly non-metalled roads. Experimental results have showed that the algorithm is invariant to shadow conditions, i.e. roads were detected under varying light conditions. Due to the soil conditions of the test sites, small puddles of water on the mud tracks are also classified as driveable areas. The system outputs a one bit 2-D map of the image every 200ms. Field results of the proposed approach have shown favourable responses for real-time implementation on an autonomous ground vehicle.
Optical testing involving interferometric fringes is often adequate to obtain a quantitative estimate of a critical parameter such as "asphericity," i.e., the deviation of a surface from a perfect sphere at a specific radial distance. We propose a two-stage neural network solution that can provide such a quantitative output. The first stage is a self-organizing map network that efficiently performs fringe thinning by detecting fringe maxima even in the case of noisy low-contrast images. The second stage is a back-propagation-trained neural network that "learns" to interpret the thinned fringes represented by polynomial coefficients. Fringe patterns from a sample set of optical components, with known parameters that cover a wide range, are used for training. Experimental results were obtained in the case of fringe patterns from a Talbot interferometer for testing aspheric glass molds used in ophthalmic lens making. On test molds with asphericity values close to 0.050 mm, the difference between values obtained from the neural network and by contact measurement was less than 0.002 mm.
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