Computer Vision, Graphics, and Image Processing
Detecting partially occluded ellipses using the Hough transform
Image and Vision Computing - 4th Alvey Vision Meeting
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Nonlinear Image Processing
An Introduction to Nonlinear Image Processing
Computer Vision
Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Improved techniques for automatic image segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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A novel generalized Hough transform algorithm which makes use of the color similarity between homogeneous segments as the voting criterion is proposed in this paper. The input of the algorithm is some regions with homogeneous color. These regions are obtained by first pre-segmenting the image using the morphological watershed algorithm and then refining the resultant outputs by a region merging algorithm. Region pairs belonging to the object are selected to generate entries of the reference table for the Hough transform. Every R-table entry stores a relative color between the selected region pairs. This is done in order to compute the color similarity and in turn generate votes during the voting process and some relevant information to recover the transformation parameters of the object. Based on the experimental results, our algorithm is robust to change of illumination, occlusion and distortion of the segmentation output. It recognizes objects which were translated, rotated, scaled and even located in a complex environment.