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Lecture - Segmentation

Lecture - Segmentation - EE4780 Image Segmentation...

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EE 4780 Image Segmentation
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Bahadir K. Gunturk EE 7730 - Image Analysis I 2 Image Segmentation Group similar components (such as, pixels in an image, image frames in a video) to obtain a compact representation. Applications: Finding tumors, veins, etc. in medical images, finding targets in satellite/aerial images, finding people in surveillance images, summarizing video, etc. Methods: Thresholding, K-means clustering, etc.
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Bahadir K. Gunturk EE 7730 - Image Analysis I 3 Image Segmentation Segmentation algorithms for monochrome images generally are based on one of two basic properties of gray- scale values: Discontinuity The approach is to partition an image based on abrupt changes in gray-scale levels. The principal areas of interest within this category are detection of isolated points, lines, and edges in an image. Similarity The principal approaches in this category are based on thresholding, region growing, and region splitting/merging.
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Bahadir K. Gunturk EE 7730 - Image Analysis I 4 Thresholding Suppose that an image, f(x,y), is composed of light objects on a dark backround, and the following figure is the histogram of the image. Then, the objects can be extracted by comparing pixel values with a threshold T.
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Bahadir K. Gunturk EE 7730 - Image Analysis I 5 Thresholding
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Bahadir K. Gunturk EE 7730 - Image Analysis I 6 Thresholding
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Bahadir K. Gunturk EE 7730 - Image Analysis I 7 Thresholding It is also possible to extract objects that have a specific intensity range using multiple thresholds. Extension to color images is straightforward: There are three color channels, in each one specify the intensity range of the object… Even if objects are not separated in a single channel, they might be with all the channels… Application example: Detecting/Tracking faces based on skin color…
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Bahadir K. Gunturk EE 7730 - Image Analysis I 8 Thresholding Exercise: Cost of classifying a background pixel as an object pixel is Cb.
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