Showing papers on "Scale-invariant feature transform published in 1995"

Image processing method and apparatus for extracting lines from an image by using the Hough transform

Abstract: An image processing apparatus for extracting lines from an image using the Hough transform. A processor element is assigned to each quantization point in a Hough space. Each processor element calculates, for each scanning line of the image, intersections of the scanning line and the line corresponding to this processor element once per scanning line. A black pixel (a Hough transform object point) on the scanning line is also obtained sequentially. The coordinate values of the intersection are compared with those of the Hough transform object point, and when they agree, voting to a ballot box memory of the processor element is performed. The voting results become Hough transform data. This makes it possible to implement a high speed Hough transform, and to reduce the size of the apparatus.

Peak detection in Hough transform via self-organizing learning

Abstract: In this paper, we suggest a novel concept of applying the self-organizing map (SOM) in the Hough domain for a significant reduction of the Hough space. By using the SOM as the output space of the generalized Hough transform, the conventional 4-D Hough domain is replaced by a 10/spl times/10 map, organized in a rectangular grid. Experimental results indicate high accuracy in voting is attainable despite its small memory requirement.

The Hough transform and the UpWrite: a comparison

Abstract: Compares two methods of pattern recognition, a standard technique known as The Hough transform and a more recent development. The UpWrite, which may be interpreted as a modified version of a quadratic neural network. The authors restrict themselves to the problem of recognising ellipses in binary images. The two techniques are compared experimentally and the authors draw conclusions from the results.