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.

C API functions

For list of limitations, constraints and backends that implements the algorithm, consult reference documentation of the following functions:

Function	Description
vpiInitHarrisCornerDetectorParams	Initializes VPIHarrisCornerDetectorParams with default values.
vpiCreateHarrisCornerDetector	Creates a Harris Corner Detector payload.
vpiSubmitHarrisCornerDetector	Submits a Harris Corner Detector operation to the stream.

Usage

Language: C/C++ Python

1. Import VPI module

   import vpi

2. Execute the algorithm on the input image using the CUDA backend. Two VPI arrays are returned, one with keypoint positions, and another with the scores. The keypoints array has type vpi.Type.KEYPOINT and scores array has type vpi.Type.U32.

   with vpi.Backend.CUDA:
       keypoints, scores = input.harriscorners(sensitivity=0.01)

1. Initialization phase
   1. Include the header that defines the Harris corner detector function.

      #include <vpi/algo/HarrisCorners.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_F32, 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.

          int32_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. Initialize the configuration structure with default parameters and set sensitivity to a new value.

          VPIHarrisCornerDetectorParams params;
          vpiInitHarrisCornerDetectorParams(&params);
          params.sensitivity = 0.01;

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

          vpiSubmitHarrisCornerDetector(stream, 0, 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 information, see Harris Corners in the "C API Reference" section of VPI - Vision Programming Interface.

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 Benchmark.

clear filters Device: Jetson AGX Xavier Jetson AGX Orin  -  Streams: 1 2 4 8

References

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