Harris Keypoint Detector

Overview

The algorithm implements the Harris keypoint detection operator that is commonly used into detect keypoints and infer features of an image.

The standard Harris detector algorithm as described in [1] is applied first. After that, a non-max suppression prunning process is applied to the result to remove multiple or spurious keypoints.

Input	Parameters	Output keypoints
	\begin{align*} \mathit{gradientSize} &= 5 \\ \mathit{blockSize} &= 5 \\ \mathit{strengthThresh} &= 250 \\ \mathit{sensitivity} &= 0.24 \\ \mathit{minNMSDistance} &= 8 \end{align*}

Implementation

1. Compute the spatial gradient of the input using one of the following filters, depending on the value of VPIHarrisKeypointDetectorParams::gradientSize :
   • For gradientSize = 3:

     \begin{align*} \mathit{sobel}_x &= \frac{1}{4} \cdot \begin{bmatrix} 1 \\ 2 \\ 1 \end{bmatrix} \cdot \begin{bmatrix} -1 & 0 & 1 \end{bmatrix} \\ \mathit{sobel}_y &= (\mathit{sobel}_x)^\intercal \end{align*}

   • For gradientSize = 5:

     \begin{align*} \mathit{sobel}_x &= \frac{1}{16} \cdot \begin{bmatrix} 1 \\ 4 \\ 6 \\ 4 \\ 1 \end{bmatrix} \cdot \begin{bmatrix} -1 & -2 & 0 & 2 & 1 \end{bmatrix} \\ \mathit{sobel}_y &= (\mathit{sobel}_x)^\intercal \end{align*}

   • For gradientSize = 7:

     \begin{align*} \mathit{sobel}_x &= \frac{1}{64} \cdot \begin{bmatrix} 1 \\ 6 \\ 15 \\ 20 \\ 15 \\ 6 \\ 1 \end{bmatrix} \cdot \begin{bmatrix} -1 & -4 & -5 & 0 & 5 & 4 & 1 \end{bmatrix} \\ \mathit{sobel}_y &= (\mathit{sobel}_x)^\intercal \end{align*}

2. Compute a gradient covariance matrix (structure tensor) for each pixel within a block window, as described by:

   \[ M = \sum_{p \in B}\begin{bmatrix}I_x^2(p) & I_x(p) I_y(p) \\ I_x(p) I_y(p) & I_y^2(p) \end{bmatrix} \]

   where:

   • p is a pixel coordinate within B, a block window of size 3x3, 5x5 or 7x7.
   • \(I(p)\) is the input image
   • \( I_x(p) = I(p) * \mathit{sobel}_x \)
   • \( I_y(p) = I(p) * \mathit{sobel}_y \)

3. Compute a Harris response score using a sensitivity factor

   \[ R = \mathit{det}(M) - k \cdot \mathit{trace}^2(M ) \]

   where k is the sensitivity factor

4. Applies a threshold-strength criterion, pruning keypoints whose response < VPIHarrisKeypointDetectorParams::strengthThresh.

5. Applies a non-max suppression prunning process.

   This process splits the input image into a 2D cell grid. It selects a single corner with the highest response score inside the cell. If several corners within the cell have the same response score, it selects the bottom-right corner.

Usage

1. Initialization phase
   1. Include the header that defines the box filter function

      #include <vpi/algo/HarrisKeypointDetector.h>

   2. Define the stream on which the algorithm will be executed and the input image

          VPIStream stream = /*...*/;
          VPIImage input = /*...*/;

   3. Create the output arrays that will store the keypoints and their scores

          VPIArray keypoints;
          vpiArrayCreate(8192, VPI_ARRAY_TYPE_KEYPOINT, 0, &keypoints);
       
          VPIArray scores;
          vpiArrayCreate(8192, VPI_ARRAY_TYPE_U32, 0, &scores);

   4. Since this algorithm needs temporary memory buffers, create the payload for it.

          uint32_t w,h;
          vpiImageGetSize(input, &w, &h);
       
          VPIPayload harris;
          vpiCreateHarrisKeypointDetector(stream, w, h, &harris);

2. Processing phase
   1. Fill the configuration structure with parameters for the current algorithm invocation

          VPIHarrisKeypointDetectorParams params;
          params.gradientSize   = 5;
          params.blockSize      = 5;
          params.strengthThresh = 250;
          params.sensitivity    = 0.24;
          params.minNMSDistance = 8;

   2. Submit the algorithm and its parameters to the stream

          vpiSubmitHarrisKeypointDetector(harris, input, keypoints, scores, &params);

   3. Optionally wait until the processing is done

          vpiStreamSync(stream);

3. Cleanup phase
   1. Free resources held by the payload.

          vpiPayloadDestroy(harris);

Limitations and Constraints

Constraints for specific backends superceed the ones specified for all backends.

All Backends

• Input image must have same dimensions as the ones specified during payload creation.
• Only supports Sobel gradient kernels of sizes 3x3, 5x5 and 7x7.
• Output scores and keypoints arrays must have the same capacity.
• Must satisfy \(\mathit{minNMSDistance} \geq 1\).
• The following image types are accepted:
  • VPI_IMAGE_TYPE_Y8
  • VPI_IMAGE_TYPE_Y8I
  • VPI_IMAGE_TYPE_Y16
  • VPI_IMAGE_TYPE_Y16I

PVA

• Not implemented

References

1. C. Harris, M. Stephens (1988), "A Combined Corner and Edge Detector"
   Proceedings of Alvey Vision Conference, pp. 147-151.