Range image segmentation through pattern analysis of the multi-scale wavelet transform

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 we develop PASSEF (pattern analysis of scale space for the detection of features). PASSEF creates a fuzzy edge map and we then apply a morphological watershed algorithm to this map to create a segmentation.

The PASSEF 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. We train PASSEF with scale-space signatures from the edge points of a training image. Once trained, the system can determine the degree of edgeness 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. Our 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. We discuss these advantages in relation to the development of the PASSEF system and provide a critical analysis of the system based on these three goals. The PASSEF system achieves the stated goals for the detection of step-edge features. Our results also show that this technique might be useful in the detection of other features such as crease edges. We suggest future work for extending the capabilities of the system.