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Segmenting images using localized histograms and region merging

J. Ross Beveridge1, Joey Griffith1, Ralf R. Kohler1, Allen R. Hanson1 and Edward M. Riseman1

(1) Computer and Information Science, University of Massachusetts, Amherst, USA

Abstract  A working system for segmenting images of complex scenes is presented. The system integrates techniques that have evolved out of many years of research in low-level image segmentation at the University of Massachusetts and elsewhere. This paper documents the result of this historical evolution. Segmentations produced by the system are used extensively in related image interpretation research.
The system first produces segmentations based upon an analysis of spatially localized feature histograms. These initial segmentations are then simplified using a region merging algorithm. Parameter selection for the local histogram segmentation algorithm is facilitated by mapping the multidimensional parameter space to a one-dimensional parameter which regulates region fragmentation. An extension of this algorithm to multiple features is also presented. Experience with roughly 100 images from different domains has shown the system to be robust and effective. Samples of these results are included.

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Referenced by
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  1. Yao, W. (2005) An Estimation/Correction Algorithm for Detecting Bone Edges in CT Images. IEEE Transactions on Medical Imaging 24(8)
    [CrossRef]
  2. Andrade, E.L. (2005) Region-Based Analysis and Retrieval for Tracking of Semantic Objects and Provision of Augmented Information in Interactive Sport Scenes. IEEE Transactions on Multimedia 7(6)
    [CrossRef]
  3. Xun Wang (2003) A divide and conquer deformable contour method with a model based searching algorithm. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 33(5)
    [CrossRef]
  4. Sclaroff, S. (2001) Deformable shape detection and description via model-based region grouping. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(5)
    [CrossRef]
  5. Jia-Ping Wang (1998) Stochastic relaxation on partitions with connected components and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(6)
    [CrossRef]
  6. Stevens, M.R. (1997) Precise matching of 3-D target models to multisensor data. IEEE Transactions on Image Processing 6(1)
    [CrossRef]
  7. Kim, J. (2003) Combining static and dynamic features using neural networks and edge fusion for video object extraction. IEE Proceedings - Vision Image and Signal Processing 150(3)
    [CrossRef]
  8. Herbordt, Martin C. (1992) Nonuniform region processing on SIMD arrays using the coterie network. Machine Vision and Applications 5(2)
    [CrossRef]
  9. Dubuisson, Marie-Pierre (1995) Contour extraction of moving objects in complex outdoor scenes. International Journal of Computer Vision 14(1)
    [CrossRef]
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