lecture10imageSegmentation

lecture10imageSegmentation - Finding connected components...

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Finding connected components in images Connected components are usually regions that correspond to objects in the scene
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Digital images are 2D arrays of pixels (gray or RGB) Human faces can be recognized at 64 x 64 pixels per face Eyes can be located by finding 2 bright spots inside of dark spots
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Pixels in an eye Pupil is usually very dark Iris is also dark compared to “white of eye” So, brightest and darkest pixels of a face might be in the eye Maybe = 20 Maybe = 120
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engineered imaging (OCR parts counting) More uniform objects More uniform background Thresholding works Objects actually separated
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Results of “pacmen” analysis 15 objects detected Location known Area known 3 distinct clusters of 5 values of area; 85, 145, 293
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Example red blood cell image Many blood cells are separate objects Many touch – bad! Salt and pepper noise from thresholding How useable is this data?
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Results of CC analysis 63 separate objects detected Single cells have area about 50 Noise spots Gobs of cells
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“coloring algorithms” Detect and color (or label) each distinct set of connected pixels. Many variations
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lecture10imageSegmentation - Finding connected components...

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