This is based on Harris Corner detection i will handle the implementation but ne

Question

This is based on Harris Corner detection i will handle the implementation but needed some help showing these intermediate steps:

this is supposed to only use Java

1.computing products of derivatives and visualizing the three products using the three channels of a color image;

2. calculating corner response under user specified sensitivity parameter and displaying the results as a

grayscale image;

3. showing the results of thresholding; and

4. performing non-max suppression and displaying the detected corner pixels.

Explanation / Answer

(A)Implementing Harris Corner Detector in VisionCpp

VisionCpp 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.

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 here).

Three color image:

Let's create the some simple operators (multiplication, power of 3, addition, subtraction) that will be used for our next tasks. Those operators are already implemented in VisionCpp, we are creating it again for tutorial purposes, showing that it is easy to create new operators.

(b) Gray scale

We need now to convert our image to Greyscale. VisionCpp has already implemented operators to convert to Greyscale.

Now we are able to compute the products of the derivatives.

And we can apply the formula: R = det(M) - k(trace(M))²

(C) Threshold result

(d) Non-maximum suppression

consider three points...

At q, we have a maximum if the value is larger than those at both p and at r. Interpolate to get these values.

Steps in Corner Detection:

1. For each pixel, the corner operator is applied to obtain a cornerness measure for this pixel.

2. Threshold cornerness map to eliminate weak corners.

3. Apply non-maximal suppression to eliminate points whose cornerness measure is not larger than the cornerness values of all points within a certain distance.

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