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Understanding Hysteresis Thresholding in Image Processing

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This guide covers the process of hysteresis thresholding, particularly using double thresholds in magnitude images. The method involves setting a high and a low threshold for identifying peaks, ensuring that only significant features are passed to the final output. If the magnitude exceeds the high threshold, it is marked for the final output. When the magnitude falls between the high and low thresholds, adjacency to existing final outputs is checked. Additionally, an iterative yet inefficient algorithm approach to apply double thresholds is presented for illustration purposes.

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Understanding Hysteresis Thresholding in Image Processing

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  1. Canny Part Three Also called Hysteresis Thresholding

  2. Double Thresholds • Will be applied to Magnitude image, but only to places that have shown up as peaks • Will have two thresholds, a HIGH and a LOW • If Mag exceeds HI, definitely pass to Final • If Mag is between HI and LO, then check if geographically adjacent to a position that is in Final; if yes, then pass to Final

  3. Double Thresholds cont’d • Will be applied to Magnitude image, but only to places that have shown up as peaks • Will have two thresholds, a HIGH and a LOW • If Mag exceeds HI, definitely pass to Final • If Mag is between HI and LO, then check if geographically adjacent to a position that is in Final; if yes, then pass to Final • If Maglower than LO, definitely not in final

  4. Double Thresholds cont’d • The typical way to write this is using Recursion. • Simply scan the image, looking only at Peaks, and at each Peak, ask if Mag exceeds HI; if No, do nothing (go on to next peak); if Yes, then call a recursive procedure on each of the 8 neighbors • The recursive procedure must use LO to determine if it should call itself again on the 8 neighbors of the peak it was given.

  5. Double Thresholds cont’d • Since we do assume that all students in class know how to write recursion, here is an iterative, simple-to-follow, but inefficient procedure: For i, For j if peaks(ij) == ON if mag(ij)> HI peaks(ij) = OFF, final(ij) = ON else if mag(ij)< LO peaks(ij)=final(ij)= OFF.

  6. Simple, inefficient cont’d Moretodo=ON While moretodo==ON moretodo= OFF For i, For j if peaks(ij) == ON For p (-1 to +1), For q (-1 to +1) if final(i+p,j+q) == ON peaks(ij) = OFF, final(ij) = ON, moretodo=ON

  7. Simple, inefficient cont’d inefficient Case: LLLLLLLL L L MMMMMMMMMMMMMMM L M M L M HHHH M L M M M L M M M L MMMMMMMM M M M M M MMMMMMMMMMMMMMMMMM Thankfully, most M-chains are small.

  8. Canny Part Four Automatically get HI (and hence LO)

  9. Auto get HI Use Percent as input Then apply to histogram of scaled mags In the histogram of scaled mags, find the Point that exceeds Percent of all, mark that as HI. Then, LO is 0.35 of HI

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