New Mexico Patent of the Month – April 2021
When analyzing digital images and videos, many methods require 2-D convolutions and cross-correlations. These analysis methods are commonly used in feature extraction, template matching, pattern recognition and more. These can be slow processes and reduce the efficiency of analysis. UNM Rainforest Innovations have designed a system capable of rapid execution of both 2D convolutions and cross-correlations.
Traditional methods to speed up the processes build their systems on 2D FFTs (Fast Fourier Transform) which analyze the frequency spectrum of 2D signal data. These methods are better direct approaches but unfortunately require some complex hardware and are not easily scalable. UNM Rainforest Innovations have designed their system using Discrete Periodic Radon Transform (DPRT) instead. In brief, an image is provided along with a convolution kernel. Convolution is a mathematical way of combining two signals to form a third. The DPRT of both the image and kernel are computed. The system then computes one or more 1D circular convolutions with the two DPRTs. This process is repeated over the whole image, row by row.
This approach provides scalability as the hardware is more easily modified to control the number of row-processors within. With the speed and scalability of the DPRT approach, the 2D convolutions and cross-correlations are as fast as possible based on the available resources. Which means as resources increase, the speed does too.
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