ON EXTENDING THE GENERALIZED HOUGH TRANSFORM
Benguang Yao · Summit (Simon Fraser University) · 1990
Matching models to images is a very important task in image understanding.The generalized Hough transform (GHT) proposed by Ballard has been proven to be a very effective method for matching models of arbitrary shapes to images.It converts the problem of global pattern detection into a problem of local peak finding.However, the GHT has difficulty handling images with occlusions due to the reduced peak values when objects are partially occluded.It can easily make mistakes when images contain patterns similar to the model.Moreover, it is also very difficult to apply parallel processing.Thc unique peak spot results in contention when processors try to access it.In this thesis, An improved version of Ballard's GHT is proposed which provides a potentially more robust and systematic technique, the linear Hough transform, for solving the problems in the detection of partially occluded objects.We use a linear numeric pattern to replace the peak in the GHT and use the relationship between entries in the linear pattern to achieve high robustness.Partial matches of the linear pattern are used for partial object detection.Finally, we present a parallel version of our new technique to exploit the parallel computational power of array processors.High performance is achieved by taking advantage of the speed-up due to reduced contention in the accumulation process which results with our linear numeric pattern.