Image segmentation via piecewise constant regression

Scott Thomas Acton; Alan Conrad Bovik

doi:10.1117/12.185955

16 September 1994 Image segmentation via piecewise constant regression

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185955
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

We introduce a novel unsupervised image segmentation technique that is based on piecewise constant (PICO) regression. Given an input image, a PICO output image for a specified feature size (scale) is computed via nonlinear regression. The regression effectively provides the constant region segmentation of the input image that has a minimum deviation from the input image. PICO regression-based segmentation avoids the problems of region merging, poor localization, region boundary ambiguity, and region fragmentation. Additionally, our segmentation method is particularly well-suited for corrupted (noisy) input data. An application to segmentation and classification of remotely sensed imagery is provided.

Citation Download Citation

Scott Thomas Acton and Alan Conrad Bovik "Image segmentation via piecewise constant regression", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185955

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