A Fisher-Rao Metric for curves using the information in edges

Two curves which are close together in an image are indistinguishable given a measurement, in that there is no compelling reason to associate the measurement with one curve rather than the other. This observation is made quantitative using the parametric version of the Fisher-Rao metric. A probability density function for a measurement conditional on a curve is constructed. The distance between two curves is then defined to be the Fisher-Rao distance between the two conditional pdfs. A tractable approximation to the Fisher-Rao metric is obtained for the case in which the measurements are compound in that they consist of a point x and an angle α which specifies the direction of an edge at x. If the curves are circles or straight lines, then the approximating metric is generalized to take account of inlying and outlying measurements. An estimate is made of the number of measurements required for the accurate location of a circle in the presence of outliers. A Bayesian algorithm for circle detection is defined. The prior density for the algorithm is obtained from the Fisher-Rao metric. The algorithm is tested on images from the CASIA Iris Interval database

Active intelligent vision using the dynamic generalized Hough transform

Parametric transformation is a powerful tool in shape analysis which gives good results even in the presence of noise and occlusion. Major shortcomings of the technique are excessive storage requirements, computational complexity and the need to initiate a separate transformation process with respect to each feature under detection. In addition, standard parametric transformation processes the entire image treating each image point independently. The proposed method selectively segments the image and provides a feedback mechanism linking image and transform space. Decisions are made concerning the probable instance of shape under detection, the viability of processing and the need to gather further evidence from the image

Statistical Properties of the Hybrid Radon-Fourier Technique

The hybrid Radon-Fourier technique has been proposed for the discrimination and tracking of deforming and compound targets. The current work investigates the technique’s unique statistical properties which make it inherently robust with respect to performance. The Radon transform is used to generate the geometric signature waveform of the convex hull of the target, this then becomes the input to the Fourier Transform and the Fourier coefficients determine the parameters associated with the shape and motion of the target. Because, in general, relatively few points on the boundary of the object define the convex hull they will follow a Poisson distribution. In addition, for each point in the set of points defining the convex hull, there is a high probability that another neighboring point on or near the boundary may be substituted for that point with no significant effect on the performance of the algorithm. This means that the data may be extremely sparsely sampled without a significant degradation in the performance of the algorithm and with a corresponding reduction in the computational load. The theory is illustrated using 2-D data. The extension of the technique to 3-D data is discussed and is straightforward.

WTC2005-64385 ESTABLISHMENT OF THE ACCURACY AND CONSISTENCY OF USING AUTOMATIC IMAGE ANALYSIS TO CLASSIFY WEAR DEBRIS PARTICLES

ABSTRACT Wear debris particle analysis is an equipment health monitoring technique used to identify possible failure modes in various engine components. One of the first stages in the analysis involves the examination under a microscope of particles collected from the component's lubrication system on magnetic drain plugs and filters. However, the subjectivity of technicians' judgements means that diagnosis may not be consistent between technicians. A software tool capable of automatically classifying the images of wear debris particles has been developed and tested using an 800-image database. It is shown that using automatic image analysis for the classification of wear debris particle images is more consistent, accurate and informative when compared to the classifications assigned by wear debris experts

Vanishing Point Detection with an Intersection Point Neighborhood

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