Harris Corner Detector

Overview

This algorithm implements the Harris keypoint detection operator that is commonly used to 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 pruning 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} &= 20 \\ \mathit{sensitivity} &= 0.01 \\ \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 VPIHarrisCornerDetectorParams::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 <= VPIHarrisCornerDetectorParams::strengthThresh.

5. Applies a non-max suppression pruning 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/HarrisCornerDetector.h>

   2. Define the input image object.

          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 on the CUDA backend.

          uint32_t w, h;
          vpiImageGetSize(input, &w, &h);
       
          VPIPayload harris;
          vpiCreateHarrisCornerDetector(VPI_BACKEND_CUDA, w, h, &harris);

   5. Create the stream where the algorithm will be submitted for execution.

          VPIStream stream;
          vpiStreamCreate(0, &stream);

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

          VPIHarrisCornerDetectorParams params;
          params.gradientSize   = 5;
          params.blockSize      = 5;
          params.strengthThresh = 20;
          params.sensitivity    = 0.01;
          params.minNMSDistance = 8;

   2. Submit the algorithm and its parameters to the stream. It'll be executed by the CUDA backend associated with the payload.

          vpiSubmitHarrisCornerDetector(stream, harris, input, keypoints, scores, &params);

   3. Optionally, wait until the processing is done.

          vpiStreamSync(stream);

3. Cleanup phase
   1. Free resources held by the stream, the payload, the input image and the output arrays.

          vpiStreamDestroy(stream);
          vpiPayloadDestroy(harris);
          vpiImageDestroy(input);
          vpiArrayDestroy(keypoints);
          vpiArrayDestroy(scores);

For more details, consult the Harris Corners Detector API reference.

Limitations and Constraints

Constraints for specific backends supersede 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_FORMAT_U8
  • VPI_IMAGE_FORMAT_S8
  • VPI_IMAGE_FORMAT_U16
  • VPI_IMAGE_FORMAT_S16
• On 16-bit inputs, the pixel values must be restricted to 12-bit, or else overflows in score calculation will occur. In this case some keypoints might be invalid.

PVA

• Only available on Jetson Xavier devices.
• Only supports VPI_IMAGE_FORMAT_S16.
• Output keypoints and scores array capacity must be 8192.
• Only accepts \(\mathit{minNMSDistance} = 8\).

VIC

• Not implemented.

Performance

For information on how to use the performance table below, see Algorithm Performance Tables.
Before comparing measurements, consult Comparing Algorithm Elapsed Times.
For further information on how performance was benchmarked, see Performance Measurement.

clear filters Device: Jetson AGX Xavier Jetson TX2 Jetson Nano  -  Streams: 1 4 8

References

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