surf

I’m currently working on pattern recognition using SURF in OpenCV. What do I have so far: I’ve written a program in C# where I can select a source-image and a template which I want to find. After that I transfer both pictures into a C++-dll where I’ve implemented a program using the OpenCV-SURFdetector, which returns all the keypoints and matches back to my C#-program where I try to draw a rectangle around my matches. Now my question: Is there a common measure of accuracy in pattern recognition? Like for example number of matches in proportion to the number of keypoints in the template? Or

I need some help in training a SVM for an Android app. I have a set of images in different classes (12 classes) and got all descriptors from them. I managed to get the same amount of descriptors for each image. What I need is to train a SVM for my android application with those descriptors. I'm not sure if I should train it in the Android emulator or write a C++ program to train the SVM and then load it in my app (if I use the OpenCV's lib for windows to train the SVM and then save it, will the lib I'm using for Android recognize the saved SVM file?). I guess I shouldn't train the SVM with

I am doing an ancient coins recognition system using matlab. What I have done so far is: convert to grayscale remove noise using Gaussian filter contrast enhancement edge detection using canny edge detector. Now I want to extract feature for classification. Features I thought to select are roundness, area, colour, SIFT and SURF. My problem is how I can apply SIFT and SURF algorithms to my project. I couldn't find built-in functions for both. you can find a matlab implementation of SIFT features here: http://www.cs.ubc.ca/~lowe/keypoints/ You can find SIFT as a C implementation with MATLAB

I'm trying to classify objects using SURF and kNN. The code work well however it occasionally crashes and shows 'Segmentation Fault'. I'm not sure whether I did something wrong but I'm pretty sure that it is corrected. Here is the input file in case that you want to reproduce the issue. Link to download the dataset import numpy as np import cv2 import sys trainfile = ['/home/nuntipat/Documents/Dataset/Bank/Training/15_20_front.jpg' , '/home/nuntipat/Documents/Dataset/Bank/Training/15_50_front.jpg' , '/home/nuntipat/Documents/Dataset/Bank/Training/15_100_front.jpg' , '/home/nuntipat/Documents

I'm trying the assess the correctness of my SURF descriptor implementation with the de facto standard framework by Mikolajczyk et. al . I'm using OpenCV to detect and describe SURF features, and use the same feature positions as input to my descriptor implementation. To evaluate descriptor performance, the framework requires to evaluate detector repeatability first. Unfortunately, the repeatability test expects a list of feature positions along with ellipse parameters defining the size and orientation of an image region around each feature. However, OpenCV's SURF detector only provides feature

I am using BOW in opencv for clustering the features of variable size. However one thing is not clear from the documentation of the opencv and also i am unable to find the reason for this question: assume: dictionary size = 100. I use surf to compute the features, and each image has variable size descriptors e.g.: 128 x 34, 128 x 63, etc. Now in BOW each of them are clustered and I get a fixed descriptor size of 128 x 100 for a image. I know 100 is the cluster center created using kmeans clustering. But I am confused in that, if image has 128 x 63 descriptors, than how come it clusters into