We talked about color segmentation in
lecture; images were segmented by clustering together color vectors.
For 1. Extract 8 x 8 patches around each input pixel. For each patch, compute the 2D spectrum using a 2D Fourier transform (use a windowed Fourier transform to reducing ringing). Remove the DC component. This gives a 63 dimensional feature vector at each pixel. Perform segmentation of the input image by treating this feature vector similar to the color vectors, using the k-means algorithm. 2. As you may notice on some images,
applying the k-means algorithm directly gives rise to some pretty
rough edges around the different segmented regions. To avoid this,
we are going to Find some pictures to evalute and test the performance of your texture segmentation method. |

Course Information > Übungen >