Howtosignalprocessing
- What is generalized cross-correlation?
- How does GCC Phat work?
- What is cross-correlation in image processing?
- What is the difference between cross-correlation and convolution?
What is generalized cross-correlation?
The generalized cross correlation (GCC) is regarded as the most popular approach for estimating the time difference of arrival (TDOA) between the signals received at two sensors. Time delay estimates are obtained by maximizing the GCC output, where the direct-path delay is usually observed as a prominent peak.
How does GCC Phat work?
The function assumes that the signal and reference signal come from a single source. To estimate the delay, gccphat finds the location of the peak of the cross-correlation between sig and refsig . The cross-correlation is computed using the generalized cross-correlation phase transform (GCC-PHAT) algorithm.
What is cross-correlation in image processing?
Cross-Correlation:
Correlation is the process of moving a filter mask often referred to as kernel over the image and computing the sum of products at each location. Correlation is the function of displacement of the filter.
What is the difference between cross-correlation and convolution?
Cross-correlation and convolution are both operations applied to images. Cross-correlation means sliding a kernel (filter) across an image. Convolution means sliding a flipped kernel across an image.
