Samuel G. Burgiss
Electrical Engineering
M.S. 1998
Range Image Segmentation through Pattern Analysis of Multi-Scale Wavelet Transform
Research Objectives:
This research focuses on segmenting range images using multi-scale edge information.
Image segmentation is a difficult problem, but it is an essential pre-processing
step in many computer vision systems. In the case of range images, segmentation
is performed in order to locate objects, fit surfaces, and render volumes.
Segmentation of a range image is the process of dividing the image into areas
that are associated with specific objects or object components. Often, these
objects or object components can be identified in a range image as patches that
are relatively uniform in range value or surface shape.
Methodology and Results:
This work presents an image segmentation method for range data that uses multi-scale
wavelet analysis in combination with pattern recognition. To segment range
images, a technique referred to as pattern analysis of scale space for the
detection of features (PASSEF) has been developed. Then, a morphological
watershed algorithm is applied to a fuzzy edge map to implement segmentation.
The system uses pattern recognition to classify points in an image based on
response to a feature detector over scale. A scale-space signature is the vector
of measurements at different scales taken at a single point in an image. The
system is trained with scale-space signatures from the edge points of a training
image. Once trained, the system can determine the degree of "edginess"
of points in a new image. A feature-detection framework based on multi-scale
analysis and pattern-recognition has several potential advantages over other
feature-detection systems. The goal is to create a system that exploits the
advantages of a multi-scale, pattern-recognition framework. These advantages are
detection of features at different scales (i.e. features of all sizes),
robustness to noise, and few or no free parameters. The PASSEF system achieves
the stated goals for the detection of step-edge features. The results also show
that this technique might be useful in the detection of other features such as
crease edges.
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This work was conducted by Samuel G. Burgiss while at IRIS lab under the supervision
of R. Whitaker (Thesis Chair) and M. A. Abidi. This work was supported by DOE's University Research Program in
Robotics under grant DOE-DE-FG02-86NE37968.
Papers
- S. G. Burgiss, E. D. Lester,
R. T. Whitaker, and M. A. Abidi, "Scene Segmentation from Vector-Valued
Images Using Anisotropic Diffusion", SPIE Conference on Intelligent
Robots and Computer Vision XVII: Algorithms, Techniques, and Active vision,
Boston, MA, 1998, 3522, pp. 527-538.
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