Partitioned Hough transform for ellipsoid detection
Pattern Recognition
Antialiasing the Hough transform
CVGIP: Graphical Models and Image Processing
Equivalence of Hough curve detection to template matching
Communications of the ACM
Assessment of Center of Rotation of the Glenohumeral Joint
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
On the Hough Technique for Curve Detection
IEEE Transactions on Computers
Viewer Independent Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough Transform from the Radon Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class of sampling-error free measures in oversampled band-limited images
Pattern Recognition Letters
Pattern Recognition Letters
Optimization of an Hough transform algorithm for the search of a center
Pattern Recognition
Generalised relaxed Radon transform (GR2T) for robust inference
Pattern Recognition
Hi-index | 0.01 |
The generalized Radon (or Hough) transform is a well-known tool for detecting parameterized shapes in an image. The Radon transform is a mapping between the image space and a parameter space. The coordinates of a point in the latter correspond to the parameters of a shape in the image. The amplitude at that point corresponds to the amount of evidence for that shape. In this paper we discuss three important aspects of the Radon transform. The first aspect is discretization. Using concepts from sampling theory we derive a set of sampling criteria for the generalized Radon transform. The second aspect is accuracy. For the specific case of the Radon transform for spheres, we examine how well the location of the maxima matches the true parameters. We derive a correction term to reduce the bias in the estimated radii. The third aspect concerns a projection-based algorithm to reduce memory requirements.