Showing papers on "Generalised Hough transform published in 1984"

Generalised Hough Transform

Abstract: The Hough transform technique was originally a method for detecting, in images, lines and other shapes characterisable by analytic functions. It has recently been extended (primarily by Ballard) to handle the correlation of 2D and 3D shapes which have no analytic description. One way of describing the technique is as a mapping from a spatially indexed feature space to a non-spatially indexed parameter space for the purpose of scene segmentation. The image segmentation technique of histogramming-then-thresholding can be used as an illustration. A very crude, and only under special circumstances successful, segmentation technique is to create a histogram of grey-level image intensity levels, ie, map the spatially indexed intensity values into a non-spatiaily indexed intensity grey level feature space. If the image originally consisted of an object and background of very different average reflectance properties, the histogram may have two clearly defined peaks. Simple thresholding at the minimum between them may suffice to segment figure from ground. Extending this idea to a colour feature space one can see that points contributing to peaks in the non-spatially indexed colour space may originate from significant spatially extended segments of the image, the blue of the sky, the green of the grass, the red roofs etc. Yet another example: consider the optic flow or instantaneous velocity image, grouping the points that are all moving in the same direction with the same velocity may be sufficient to segment out of the image the onrushing traffic from the background, etc. etc.