How is knnMatching done with a k value greater than 1 when there is only one feature vector per image that is being compared? Where are the neighbours coming from? I can understand if there was a FLANN database.
Each feature is a vector and there are many features in an image. So knn tries to find the top k features that are most similar (ie shortest Euclidean distance)
Determination of copy-paste forgery in an image by SIFT keypoint method a. Identification of key points with SIFT in an image that has been copied and pasted b. Identification of matches from these key points c. Elimination of false matches d. Enclosing the copied and pasted regions in the square e. Demonstration of fraud detection performances when scaling, adding noise, rotating operations are applied to the fake image how can i do?
Code and Doc: kevinwoodrobotics.com/product/opencv-python-feature-matching/
OpenCV Python Playlist Code and Doc: kevinwoodrobotics.com/product/opencv-python-tutorials-full-playlist/
How is knnMatching done with a k value greater than 1 when there is only one feature vector per image that is being compared? Where are the neighbours coming from? I can understand if there was a FLANN database.
Each feature is a vector and there are many features in an image. So knn tries to find the top k features that are most similar (ie shortest Euclidean distance)
Determination of copy-paste forgery in an image by SIFT keypoint method
a. Identification of key points with SIFT in an image that has been copied and pasted
b. Identification of matches from these key points
c. Elimination of false matches
d. Enclosing the copied and pasted regions in the square
e. Demonstration of fraud detection performances when scaling, adding noise, rotating operations are applied to the fake image
how can i do?
Great questions. It’s a bit involved to answer all this over a comment. If you’d like more help you can email me.