surf

for my master thesis, i am running some test on the SIFT SURF en FAST algoritms for logo detection on smartphones. when i simply time the detection, description en matching for some methods i get the following results. For a SURF detector and SURF descriptor: 180 keypoints found 1,994 seconds keypoint calculation time (SURF) 4,516 seconds description time (SURF) 0.282 seconds matching time (SURF) when I use a FAST detector in stead of the SURF detector 319 keypoints found 0.023 seconds keypoint calculation time (FAST) 1.295 seconds description time (SURF) 0.397 seconds matching time (SURF) The

I am trying to detect an object from the following code involving surf detector, I do not want to draw matches, I want to draw a rectangle around the detected object, but somehow I am unable to get correct Homography, please can anyone point out where I am going wrong. #include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/calib3d/calib3d.hpp" using namespace cv; int main() { Mat object = imread( "sample.jpeg", CV_LOAD_IMAGE_GRAYSCALE );

I want to recognize Vehicles(Cars, Bikes etc.) from a static image. I was thinking of using SURF to get me useful keypoints and descriptors and then train a MLP(Multi Layer Perceptron) Neural Network. However I don't know what will be the input to the Neural Network and what it's output will be so that I can identify which portion of the image a vehicle is located(Probably a rectangle drawn around it). I know that SURF can return useful keypoints in the image along with their descriptors(I have done this). The keypoints have angles and each keypoint corresponds to a 64 or 128 long Vector as

I'm trying to use OpenCV to extract SURF descriptors from an image. I'm using OpenCV 2.4 and Python 2.7, but am struggling to find any documentation that provides any information about how to use the functions. I've been able to use the following code to extract features, but I can't find any sensible way to extract descriptors: import cv2 img = cv2.imread("im1.jpg") img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) surf = cv2.FeatureDetector_create('SURF') detector = cv2.GridAdaptedFeatureDetector(surf, 50) # max number of features fs = detector.detect(img2) The code I tried for extracting

I'm writing both descriptors (SurfDescriptorExtractor output) and keypoints (SurfFeatureDetector output) to an XML file. Before writing keypoints (std::vector) conversion to Mat is done ( following this: convert keypoints to mat or save them to text file opencv ). For descriptors isn't neccesary, they're Mat already. So both are saved as Mat, there's no problem on reading either. But when using a FlannBasedMatcher, and then drawMatches, this method asks for keypoint data. The question is: how would you convert Mat to Keypoint's vector, and which would be the best approach? This is how the

I'm implementing an algorithm in OpenCV that I've designed in MATLAB. I'm writing a unit test for the SURF feature extractor in OpenCV, and I want to compare the output of MATLAB's extracted SURF features to OpenCV. This issue is, using the same parameters for both MATLAB and OpenCV extractors I'm getting different numbers of features. How is this possible? Are there different ways to implement SURF? For MATLAB ( http://www.mathworks.com/help/vision/ref/detectsurffeatures.html ) I'm using: MetricThresh: 200 NumOctaves: 3 NumScaleLevels: 4 SURFSize: 64 For OpenCV I'm using: HessianThreshold:

I want to do a 3-d plot with mayavi2 using mayavi.mlab.surf(). This function has an argument called warp_scale that can be used to scale the z axis, I'm looking for something similar but for the x and y axes. I can do this manually by multiplying the x and y arrays and then using the ranges argument in mayavi.mlab.axes() to correct the axes labels, however I'm looking for a more direct approach like that of warp_scale. Thanks! when "m" is your surface object: m.actor.actor.scale = (0.1, 1.0, 1.0) http://osdir.com/ml/python.enthought.devel/2006-11/msg00067.html I was looking for the same