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Update py_sift_intro.rst #274

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2 changes: 1 addition & 1 deletion source/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.rst
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Expand Up @@ -57,7 +57,7 @@ DoG has higher response for edges, so edges also need to be removed. For this, a

\frac{Tr(H)^2}{Det(H)} < \frac{(r+1)^2}{r} \; \text{where} \; r = \frac{\lambda_1}{\lambda_2}; \; \lambda_1 > \lambda_2

If this ratio is greater than a threshold, called **edgeThreshold** in OpenCV, that keypoint is discarded. It is given as 10 in paper.
If this ratio is greater than a threshold, called **edgeThreshold** in OpenCV, that keypoint is discarded. It is given as 10 in the paper.

So it eliminates any low-contrast keypoints and edge keypoints and what remains is strong interest points.

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