| Date post: | 23-Jan-2017 |
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Engineering |
| Upload: | abhishek-meena |
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Assignment #1 CS676: Computer Vision and Image Processing
HW : Implementation of Mean Shift algorithm for image segmentation
Group # 15 Abhishek Meena - 12028 Jitendra Kumar Nagar - 12325
Algorithm
Step 1: Mean Shift Clustering:
Step2: Mean Shift Image Segmentation
Results:
Different image segmentation obtained by varying the 3 parameters: Kernel: Gaussian
hs = spatial bandwidth if the kernel
hr = range bandwidth of the kernel
th = threshold set for he convergence Image # 42049
Original image (42049)
Segmented image and data points with their clusters for (hs,hr) = (8,7)
Image # 101085
Original Image
Image # 119082
Image # 170057
Image # 86000
![Deep Mean-Shift Priors for Image Restorationpapers.nips.cc/paper/6678-deep-mean-shift-priors... · to the mean-shift vector [8], and it has recently been shown that denoising autoencoders](https://static.documents.pub/doc/80x56/5eb672e5dcf8565d963f6c77/deep-mean-shift-priors-for-image-to-the-mean-shift-vector-8-and-it-has-recently.jpg)
![Deep Mean-Shift Priors for Image Restorationto the mean-shift vector [8], and it has recently been shown that denoising autoencoders (DAEs) learn such a mean-shift vector field for](https://static.documents.pub/doc/80x56/5eb6755bd4f1cd506c3e9e21/deep-mean-shift-priors-for-image-restoration-to-the-mean-shift-vector-8-and-it.jpg)
