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Image Segmentation

Image Segmentation. Chin-Ya Huang Mon-Ju Wu ECE 533 Final Project, Fall 2006 University of Wisconsin – Madison. Methodology. Acquire the color information and the edge information separately. Use hue, saturation and intensity to get color information.

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Image Segmentation

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  1. Image Segmentation Chin-Ya Huang Mon-Ju Wu ECE 533 Final Project, Fall 2006 University of Wisconsin – Madison

  2. Methodology • Acquire the color information and the edge information separately. • Use hue, saturation and intensity to get color information. • Use the Matlab “edge” command to extract the image boundary. • Combine the above result by getting the union of (2) and (3). • Final modification.

  3. Methodology

  4. Example Target: Extract the image of the soccer player out from the entire image.

  5. Step 1 : Using HSI as a threshold • Cut partial images to compute the HSI values. • Set the threshold value. • Examine through the image to acquire the color information.

  6. Step 2 : Using Matlab “edge” command • Use Matlab “edge” command to extract the course edges. • Use Matlab “imfill” command to fill the area whose surrounding course edges make a closure.

  7. edge imfill(edge)

  8. Step 3 : Combine the above images • Get the union of the images from Step 1 and Step 2. • Combine the color information and the edge.

  9. Step 4 : Final modification • Perform dilate on “edge” make sure all the separate line segments are connected. • Use “imfill” command to fill the area inside the boundary.

  10. Step 4 : Final modification (cont.) • Intersect the image from (B) with the image from Step 3 to remove the noise. • Image from (C) is the final result.

  11. Final Result Original Image

  12. Future Work • Develop a more powerful algorithm to perform interpolation in order to connect the disconnected line segment. • Find other ways to segment image other than using HSI. • Develop a more powerful algorithm for noise removal.

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