Image processing and computer vision Chapter 7 Mean-shift and cam-shift Ref o[1 Dorin Comaniciu, Peter Meer, "Mean Shift: A Robust Approach Toward Feature Space Analysis"Volume 24, Issue 5(May 2002),IEEE Transactions on Pattern Analysis and Machine Intelligence o[2] web. missouri. edu/ hantx/ECE8001/notes/Lect7 mean shift. pdf Camshift v 0.a
Image processing and computer vision Chapter 7: Mean-shift and Cam-shift Ref ⚫[1] Dorin Comaniciu, Peter Meer,"Mean Shift: A Robust Approach Toward Feature Space Analysis"Volume 24 , Issue 5 (May 2002),IEEE Transactions on Pattern Analysis and Machine Intelligence ⚫[2] web.missouri.edu/~hantx/ECE8001/notes/Lect7_mean_shift.pdf Camshift v.0.a 1
INtroduction Kernel density I Kernel choices Peak finding I Mean-shift Cam-shift What is Mean-shift? Find the peak of a probability function by the change of the mean of the data Applications Non-rigid object tracking Segmentation Camshift v 0.a
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift What is Mean-shift? • Find the peak of a probability function by the change of the mean of the data • Applications: – Non-rigid object tracking – Segmentation Camshift v.0.a 2
INtroduction Kernel density I Kernel choices Peak finding I Mean-shift Cam-shift Applications: segmentation of regions of images in a movie Use color to segment the image into logical regions for analysIS If the regions are moving mean-shift is useful Camshift v 0.a .https://www.youtube.com/watch?v=rdtun7a6h08
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift Applications: segmentation of regions of images in a movie • Use color to segment the image into logical regions for analysis. • If the regions are moving , mean-shift is useful. Camshift v.0.a 3 •https://www.youtube.com/watch?v=rDTun7A6HO8
INtroduction Kernel density I Kernel choices Peak finding I Mean-shift Cam-shift Application: tracking non- rigid object Human tracking http://ww.youtubecom/watch?v=zltjpfpp9hy Camshift v.0.a
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift Application: tracking non-rigid object • Human tracking Camshift v.0.a 4 http://www.youtube.com/watch?v=zLtjPfPP9HY
INtroduction Kernel density I Kernel choices Peak finding I Mean-shift Cam-shift Intuition: find the mode by mean shift Target: Find the modes (peaks in a set of sample data The mode of a continuous probability distribution is the peak There may be multiple peaks The method used is called mean-shift MIX By finding the shift of the mean, we can find the 8a s mode (peak) It can be used to segment an image into logical regions. e.g. within each region, the color is the same Camshift v 0.a 5
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift Intuition: find the mode by mean-shift • Target : Find the modes (peaks) in a set of sample data. – The mode of a continuous probability distribution is the peak. – There may be multiple peaks. • The method used is called mean-shift. – By finding the shift of the mean, we can find the mode (peak) • It can be used to segment an image into logical regions. (e.g. within each region, the color is the same.) Camshift v.0.a 5