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Color Segmentation

Color Segmentation. Article: A Comparison of Image Segmentation Algorithms . C. Pantofaru and M. Hebert. CMU Technical Report, September 2005. Xiang Li Jan, 13, 2009. Introduction.

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Color Segmentation

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  1. Color Segmentation Article: A Comparison of Image Segmentation Algorithms. C. Pantofaru and M. Hebert. CMU Technical Report, September 2005. Xiang Li Jan, 13, 2009

  2. Introduction • The Segmentation Algorithms • Mean Shift • Efficient Graph-based • Hybrid Segmentation Algorithm • Evaluation Methodology • NPR Index • Experiments

  3. Mean Shift Segmentation • Filtering • Find the modes of the underlying pdf • Associate with the modes any points in their basin of attraction • Clustering • EDISON system

  4. Mean Shift Segmentation Q: Is there a segmentation model that will identify overlapping, similar objects? Is this a problem that can only be solved by stereo vision since depth can help determine if an object in contiguous. Why cannot the segmentations which correctly identify structures in the image be at a too fine (high) level? Isn’t that level the higher the better?

  5. Efficient Graph-based Segmentation • Works directly on the data points in feature space • Adaptive Thresholding

  6. Hybrid Segmentation Algorithm • Mean shift filtering • Efficient graph-based clustering

  7. NPR Index • Normalized Probabilistic Rand index Stest ↔ S1 , … , Sk Q: can you clarify how the NPR indexes are generalized into each graph?

  8. Experiment • Average performance per image • Average performance per parameter choice Q: why use 3 different images to present results? Hard to intuitively compare results.

  9. Conclusions • Correctness Hybrid > Mean Shift > Graph-based • Stability with parameters Hybrid, Graph-based > Mean Shift • Stability of a particular parameter choice Hybrid, Mean Shift > Graph-based

  10. Questions • Paper talks about a starting point in each procedure run, how we can find this start point? Are these point chosen randomly? • Why we don’t use a fixed kernel bandwidth for all images? • A lot of recognition systems would be real-time systems. However, the stability and correctness experiments seemed valuable in comparing which algorithm to use. In the experiments that were done, this conclusion is valid, but what happens if you throw in other tests such as performance. • In the algorithm on page 5 why do we need step (c)? What does the time haveto do here?

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