CSE 576: Image Understanding
|original image||false-colored labeling|
Your K-means implementation should take a color PPM image, such as the one shown above on the left, and a number of clusters K. It should output a grayscale image with each pixel labeled by its class. (You can pass this image to autocolor to produce a false-colored image such as the one above.)
You can also pass this output image to conrgn (make sure you have the updated version!) to separate out the connected components within each class.
You should use the input images fb06931, fb06932, fb06933, fb11117, fb11122, fb23460, and fb23469, available from the image data page. For each input image, turn in printouts (preferably color) of
This technique will likely produce too many connected components. How could you apply techniques covered in class to clean up the connected components results? Answer this question in your submission. You don't need to implement your answer, just describe a procedure and argue that it would work.
You should also turn in a listing of your K-means implementation.