An alternate method of determining high-quality feature matches is the ratio test proposed by David Lowe in his paper on SIFT (page 20 for an explanation). This test rejects poor matches by computing the ratio between the best and second-best match. If the ratio is below some threshold, the match is discarded as being low-quality. … Read more
So i think you are on the right track w/r/t your step 1 (apply some algorithm to the image, which converts it into a set of features). This project is more challenging that most ML problems because here you will actually have to create your training data set from the raw data (the individual frames … Read more
I would try to use a skeleton representation of the image. The problem with your canny, here, is that it basically results in two lines because of the width of the line. Then I would apply the Hough transform on it.
Although you already choose BRISK, you might find FREAK interesting. Author claims to have better results than BRISK and ORB. I should also add that ORB is scale-invariant but has some problems in that area. So I would still recommend it for someone to try it. The FREAK source code is compatible with OpenCV (how … Read more
There is the new API CSS.supports. Supported in most browsers except IE. console.log( // CSS.supports(property, value) 1, CSS.supports(“text-decoration-style”, “blink”), 2, CSS.supports(“display”, “flex”), 3, CSS.supports(‘–foo’, ‘red’), 4, CSS.supports(‘(–foo: red)’), // CSS.supports(DOMstring) 5, CSS.supports(“( transform-origin: 5% 5% )”), 6, CSS.supports(“( transform-style: preserve ) or ( -moz-transform-style: preserve ) or ” + “( -o-transform-style: preserve ) or ( … Read more
You should research the existing devices and read the Android Compatibility Definition Document (CDD) and decide for yourself. I have spent some considerable time trying to figure out this problem for myself. The posters above are correct that Android already powers some non-touch devices and will power Google TV in the near future, but as … Read more
You can use hog class in opencv as follows HOGDescriptor hog; vector<float> ders; vector<Point> locs; This function computes the hog features for you hog.compute(grayImg, ders, Size(32, 32), Size(0, 0), locs); The HOG features computed for grayImg are stored in ders vector to make it into a matrix, which can be used later for training. Mat … Read more
I have a couple of suggestions that might provide some interesting results although I am not certain about either. If the cross is always near the center of your image and always lies on a planar surface you could try to find a homography between the camera and the plane upon which the cross lies. … Read more
A feature detector is an algorithm which takes an image and outputs locations (i.e. pixel coordinates) of significant areas in your image. An example of this is a corner detector, which outputs the locations of corners in your image but does not tell you any other information about the features detected. A feature descriptor is … Read more
Feature detection In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the … Read more