I am implementing a Harris corner detector for educational purposes but I'm stuck at the harris response part. Basically, what I am doing, is:.
Harris Detector: Summary. Average intensity change in direction u,v can be expressed as a bilinear form:. Describe a point in terms of eigenvalues of M: measure of corner response. A good (corner) point should have a large intensity change in all directions, i.e. R should be large positive R=detM −k traceM 2 E u,v ≃u vM u v.
Compute image intensity gradients in x- and y-direction. Blur output of (1).
Compute Harris response over output of (2). Suppress non-maximas in output of (3) in a 3x3-neighborhood and threshold output1 and 2 seem to work fine; however, I get very small values as the Harris response, and no point does reach the threshold. Input is a standard outdoor photography.Ix, Iy = intensityGradients(img);g = fspecial('gaussian');Ix = imfilter(Ix, g);Iy = imfilter(Iy, g);H = harrisResponse(Ix, Iy);.function K = harrisResponse(Ix, Iy)max = 0;sy, sx = size(Ix);K = zeros(sy, sx);for i = 1:sx,for j = 1:sy,H = Ix(j,i). Ix(j,i), Ix(j,i).
Iy(j,i)Ix(j,i). Iy(j,i), Iy(j,i). Iy(j,i);K(j,i) = det(H) / trace(H);if K(j,i) max,max = K(j,i);endendendmaxendFor the sample picture, max ends up being 6.4163e-018 which seems far too low. A corner in Harris corner detection is defined as 'the highest value pixel in a region' (usually 3X3 or 5x5) so your comment about no point reaching a 'threshold' seems strange to me.
The solution that I implemented with python, it works for me I hope you find what you are looking for import numpy as npimport matplotlib.pyplot as pltfrom PIL.Image import.from scipy import ndimagedef imap1(im):print('testing the picture.'
Implementing Harris Corner Detector in VisionCppVisionCpp is a powerful tool to write Computer Vision algorithms for high performance computing. One really famous algorithm which has many applications is the Harris Corner detector 1.
Corners are important keypoints in a image. Those points can be used to detect objects, classify images, create panoramic scenes and generate 3D reconstruction.The Harris Corner detector is based on the derivatives of the image. Below we show briefly what are the formulae to implement the Harris Corner detector:!Harris Corner Detection formulaWhere:. Ix: Derivative in x direction. (Filter used for the derivative in the image below).
Iy: Derivative in y direction. (Filter used for the derivative in the image below). w(x,y): summation mask between neighbour pixelAfter computing the Matrix M, a score can be calculated for each window, to determine if it can possibly contain a corner using the formula below.
Where det in the determinant of a matrix and trace is the sum of the diagonal elements.This algorithm is applied to each pixel, so it is possible to use the features of VisionCpp to implement it in parallel.Let's go step by step how to implement the algorithm in VisionCpp (The full file which implements the Harris Corner Detector can be seen ). In all VisionCpp applications, first we need o decide our OpenCL-accelerator device which will execute our tree.
// apply a threshold // threshold parameter for the Harris float threshold = 4. 0f;auto threshnode = visioncpp::terminal( staticcast(threshold));auto harrisTresh = visioncpp::pointoperation(harris, threshnode);// convert to unsigned char for displaying purposes auto display = visioncpp::pointoperation(harrisTresh);. By assigning our display object to the terminal node of the graph, we tell the system to write the result of the graph to the display. // display result cv::imshow( 'Harris ', outputImage);. In the image 2 below it is possible to visualize the Harris corner detector applied to a chessboard.Reference ImageHarris Corner DetectorConclusionVisionCpp is a computer vision framework which allows the user to create their own operators for developing high-level applications.
Like many computer vision algorithms, the Harris Corner detector can be implemented as an operation that can be applied to each pixel in parallel. The tree-based model enables fusion of kernels by the compiler to produce highly-optimized kernels to be executed on OpenCL-accelerated platforms, enabling the creation of high-performance applications for computer vision.References1 Harris, C., and M. 'A Combined Corner and Edge Detector.' Proceedings of the 4th Alvey Vision Conference.