Introduction to algorithms
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Space and Time Bounds on Indexing 3D Models from 2D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random perturbation models for boundary extraction sequence
Machine Vision and Applications - Special issue on performance evaluation
A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual organization in computer vision: status, challenges, and potential
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Perceptual Organization for Artificial Vision Systems
Perceptual Organization for Artificial Vision Systems
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
B-spline Contour Representation and Symmetry Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On the Distribution of Saliency
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
Grouping processes may benefit computationally when simple algorithms are used as part of the grouping process. In this paper we consider a common and extremely fast grouping process based on the connected components algorithm. Relying on a probabilistic model, we focus on analyzing the algorithm's performance. In particular, we derive the expected number of addition errors and the group fragmentation rate. We show that these performance figures depend on a few inherent and intuitive parameters. Furthermore, we show that it is possible to control the grouping process so that the performance may be chosen within the bounds of a given tradeoff. The analytic results are supported by implementing the algorithm and testing it on synthetic and real images.