Ratio of 9/7 to 0.7
时间: 2023-05-29 18:01:37 浏览: 58
We can write 0.7 as a fraction with a denominator of 1:
0.7 = 7/10
Now we can write the ratio of 9/7 to 0.7 as a fraction:
(9/7) / (7/10)
To divide fractions, we multiply by the reciprocal of the second fraction:
(9/7) * (10/7)
Simplifying:
(9*10) / (7*7) = 90 / 49
So the ratio of 9/7 to 0.7 is 90/49.
相关问题
opencv Ratio Test
The ratio test in OpenCV is a method for filtering out false matches in feature matching algorithms.
In feature matching, keypoints are detected in two images and their descriptors are calculated. These descriptors are then matched to find corresponding keypoints in both images. However, some matches may be incorrect due to noise, occlusion, or other factors.
The ratio test helps filter out these incorrect matches. It works by comparing the distance between the best and second-best matches for each keypoint. If the distance ratio is below a certain threshold, the match is considered correct. If the ratio is above the threshold, the match is discarded.
The threshold value is typically set to 0.7, meaning that the distance to the second-best match should be at least 1.4 times greater than the distance to the best match.
Here's an example code snippet for implementing the ratio test in OpenCV:
```
# detect keypoints and compute descriptors for both images
kp1, des1 = detector.compute(img1, None)
kp2, des2 = detector.compute(img2, None)
# match descriptors using a matcher object
matches = matcher.knnMatch(des1, des2, k=2)
# apply ratio test to filter out false matches
good_matches = []
for m, n in matches:
if m.distance < 0.7 * n.distance:
good_matches.append(m)
```
In this code, `detector` is a keypoint detector object and `matcher` is a descriptor matcher object. The `knnMatch` function returns a list of the two best matches for each descriptor in the first image (`des1`) with descriptors in the second image (`des2`). The ratio test is then applied to filter out false matches, and the remaining matches are stored in `good_matches`.
Match descriptors using nearest neighbor with ratio test
Nearest neighbor matching with ratio test is a simple and effective technique to filter out incorrect matches between feature descriptors. It works by comparing the distances between a descriptor in the first image and its two nearest neighbors in the second image. If the ratio of the distances is below a certain threshold, the match is considered correct.
The ratio test is used to ensure that the nearest neighbor match is significantly closer to the descriptor than the second nearest neighbor. The threshold value for the ratio test is usually set between 0.7 and 0.8, depending on the specific application.
Here is an example of how to perform nearest neighbor matching with ratio test in MATLAB:
```matlab
% Load feature descriptors for two images
descriptors1 = load('descriptors1.mat');
descriptors2 = load('descriptors2.mat');
% Find nearest neighbors using knnsearch
idx = knnsearch(descriptors2, descriptors1, 'K', 2);
% Apply ratio test
threshold = 0.8;
matches = [];
for i = 1:size(descriptors1, 1)
ratio = norm(descriptors1(i,:) - descriptors2(idx(i,1),:)) / norm(descriptors1(i,:) - descriptors2(idx(i,2),:));
if ratio < threshold
matches = [matches; i, idx(i,1)];
end
end
% Visualize matches
figure;
showMatchedFeatures(image1, image2, points1(matches(:,1),:), points2(matches(:,2),:));
```
In this example, the knnsearch function is used to find the two nearest neighbors of each descriptor in the second image for each descriptor in the first image. Then, for each descriptor in the first image, the ratio of the distances between the descriptor and its two nearest neighbors in the second image is computed. If the ratio is below the threshold, the match is considered correct and added to the matches matrix. Finally, the matched keypoints are visualized using the showMatchedFeatures function.