You could try this: http://code.google.com/p/aforge/ It includes a comparison analysis that will give you a score. There are many other great imaging features of all types included as well. // The class also can be used to get similarity level between two image of the same size, which can be useful to get information about … Read more
There is a discussion of image similarity algorithms at stack overflow. Since you don’t need to detect warped or flipped images, the histogram approach may be sufficient providing the image crop isn’t too severe.
There is a standard problem in vision called text detection in images. it is quite different to OCR. OCR concerms itself with what it says, while text detection is about determining if there is text in the image. Adi Shavit’s third link is a method to address this problem. You can look on google scholar … Read more
You could try to use local features like SIFT here: http://en.wikipedia.org/wiki/Scale-invariant_feature_transform It should work because logo shape is usually constant, so extracted features shall match well. The workflow will be like this: Detect corners (e.g. Harris corner detector) – for Nike logo they are two sharp ends. Compute descriptors (like SIFT – 128D integer vector) … Read more
Here’s some C99 source implementing the traditional approach (based on OpenCV doco): #include “cv.h” #include “highgui.h” #include <stdio.h> #ifndef M_PI #define M_PI 3.14159265358979323846 #endif // // We need this to be high enough to get rid of things that are too small too // have a definite shape. Otherwise, they will end up as ellipse … Read more
These are simply ideas I’ve had thinking about the problem, never tried it but I like thinking about problems like this! Before you begin Consider normalising the pictures, if one is a higher resolution than the other, consider the option that one of them is a compressed version of the other, therefore scaling the resolution … Read more
I had the same issue. I didn’t need to download anything else to solve this. CV2 had everything I needed. Instead of trying to figure out where the .xml files are and hard coding the values, I used a property given by cv2. From OP face_cascade = cv2.CascadeClassifier(‘haarcascade_frontalface_default.xml’) eye_cascade = cv2.CascadeClassifier(‘haarcascade_eye.xml’) Becomes face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades … Read more