Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring Surface Trace and Differential Structure from 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Uncertainty to Visual Exploration
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Volumetric segmentation of range images of 3D objects using superquadric models
CVGIP: Image Understanding
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding the parts of objects in range images
Computer Vision and Image Understanding
The Integration of Image Segmentation Maps using Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
A shock grammar for recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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This paper addresses the question of how to integrate local and global information - the goal being a stable mechanism to partition parametric data into meaningful classes without injecting a priori information about the data. To do this we introduce a novel framework to represent both local and global information and their interactions. Where both types of information are represented together in parameter space and together define a self-organisation or warping of the data. An unsupervised clustering analysis is then performed to extract from the parametric data classes that are stable and meaningful. As an example of this paradigm we consider the problem of shape decomposition. Here we describe how image discontinuities (i. e. curves, edges or local curvature) can be integrated with global parametric models that represent the image. The resulting class clusters are then equivalent to the inferred part decomposition. An example of how this process can be used is demonstrated by applying it to the specific problem of determining the parts of 3-D objects. Results on real laser rangefinder images of complex objects are presented.