US 2026/0127751 A1 · Systems and Methods for Performing Optical Flow Using GPU Tensor Processing Cores

Abstract

The present disclosure relates to machine vision systems and methods for performing optical flow calculations. Machine vision systems in accordance with many embodiments of the invention use GPU tensor processing cores to perform one-dimensional Discrete Fourier Transform (DFT) calculations using real DFT matrices, enabling efficient separable window correlation for optical flow. In one embodiment, the machine vision system includes: a camera; a processor; a processor comprising tensor processing cores; and a memory containing instructions. Executing the instructions using the processors causes the machine vision system to: obtain a pair of sequential images from the camera; identify windows in the images; perform optical flow calculations using separable window correlation, wherein the separable window correlation calculations comprise performing one-dimensional discrete Fourier transform (DFT) calculations using the tensor processing cores, and wherein the one-dimensional DFT calculations are performed on the tensor processing cores using real DFT matrices; and output optical flow information.

Claims20

Dependent claims

A machine vision system, comprising:

a camera;
a processor;
a processor comprising tensor processing cores;
and
a memory containing instructions that, when executed by the processor, cause the machine vision system to:
obtain a pair of sequential input images from the camera;
identify windows in the input images;
perform optical flow calculations using separable window correlation, where:
the separable window correlation calculations comprise performing one-dimensional Discrete Fourier Transform (DFT) calculations using the tensor processing cores, and
the one-dimensional DFT calculations are performed on the tensor processing cores using real Discrete Fourier Transform matrices;
and
output optical flow information for the input images.

9 more elements

A method for performing optical flow in a machine vision system, the method comprising:

obtaining, using a processor, a pair of sequential input images;
identifying, using the processor, windows in the input images;
performing, using a processor incorporating at least one tensor processing core, optical flow calculations using separable window correlation, wherein the separable window correlation calculations comprise:
obtaining, using the processor incorporating the at least one tensor processing core, one-dimensional discrete Fourier transforms (DFTs) of columns and rows of a window in a first image using a real DFT matrix;
obtaining, using the processor incorporating the at least one tensor processing core, one-dimensional DFTs of columns and rows of a corresponding window in a second image using the real DFT matrix;
reconstructing, using the processor incorporating the at least one tensor processing core, complex Fourier space values for the 2D discrete Fourier transform from outputs obtained using the real DFT matrices;
performing, using the processor incorporating the at least one tensor processing core, elementwise multiply-conjugate operations with respect to the reconstructed complex Fourier space values;
converting, using the processor incorporating the at least one tensor processing core, complex products to real values to obtain a real value matrix;
obtaining, using the processor incorporating the at least one tensor processing core, one-dimensional inverse discrete Fourier transforms (IDFTs) of rows and columns of the real value matrix;
and
determining, using the processor incorporating the at least one tensor processing core, subpixel peaks based upon output of the one-dimensional IDFTs;
and
outputting, using the processor, optical flow information for the input images.

10 more elements

Dependencies are inferred from the claim text.

Systems and Methods for Performing Optical Flow Using GPU Tensor Processing Cores

Life of this patent

Systems and Methods for Performing Optical Flow Using GPU Tensor Processing Cores

Life of this patent

Abstract

Claims20