Grouping ., -, ->, O-, into Regions, Curves, and Junctions
Mi-Suen Lee and Gerard Medioni
Institute for Robotics and Intelligent Systems, University of Southern California
Abstract
We address the problem of extracting segmented, structured information from
noisy data obtained through local processing of images. A unified
computational framework is developed for the inference of multiple salient
structures such as junctions, curves, regions, and surfaces from any
combinations of points, curve elements and surface patch elements inputs in
2-D and 3-D. The methodology is grounded in two elements: tensor calculus for
representation, and non-linear voting for data communication. Each input site
communicates its information (a tensor) to its neighborhood through a
predefined (tensor) field, and therefore cast a (tensor) vote. Each site
collects all the votes casted at its location and encodes them into a new
tensor. A local, parallel routine such as a modified marching cube/square
process then simultaneously detects junctions, curves, regions, and surfaces.
The proposed method is non-iterative, requires no initial guess or
thresholding, can handle the presence of multiple curves, regions and
surfaces in a large amount of noise while still preserves discontinuities,
and the only free parameter is scale.
Key Words:
shape inference, perceptual grouping, robust estimation
Results:
Inputs:
Peanut shape (n = 263 points) in increasing amount of noise:
with 2n noise points (left); with 4n noise points (middle); with 6n noise points (right)
Outputs:
The most salient surface extracted from the data shown in above
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