Imagej piv tutorial4/7/2024 The three check boxes under the correlation threshold are rather experimental options. In the advanced dialog, you can load a previous PIV data before beginning the 1st pass PIV, which permit one to do more than 3 PIV iteration. Note that this correlation value threshold doesn't work in the conventional cross-correlation mod, since the correlation value will change from image to image and is not normalized. Setting a high threshold value will leave only vectors resulting from highly matched images, while other vectors with correlation value lower than the threshold will be interpolated by surrounding values (replaced by the median value of the 8 neighbors). Since in the normalized correlation coefficient algorithm the correlation value is ranged from 1 (exact match) to -1 (zero correlation), we can use this value to filter out the result obtained from images with insufficient feature for matching. The correlation threshold value defines to what extent we should keep the correlation result. In the basic dialog, the spacing between each interrogation window (the spacing of vectors in PIV result) is automatically set as half the interrogation window size. Setting the searching window equal to the interrogation window will turn the PIV program into conventional cross-correlation mode (less recommended, but you don't need to install the OpenCV library). Otherwise, search against twice as large as the interrogation window would be fine. If you know the expected maximum displacement value, you can set the search window size as (interrogation window + 2*maximum displacement). The search window size should always be larger or equal to the interrogation window. Using 1/4 the image dimension for the 1st interrogation window could be a good starting point. Please refer to the installation section for more detail.īy default, you can do 3 passes of PIV, with decreasing interrogation window size. It is highly recommended to use this plugin with the template matching method in order to obtain better result. Second, the normalized algorithm is less sensitive to background intensity variation and allows one to define a threshold to distinguish high and low image correlations, so that the correlation made on small interrogation window presenting insufficient features which tends to be erroneous can be easily filtered out and replaced by interpolated value. The PIV information from previous round only served as a guidance for determining the correlation peak. It has several advantages: first, the interrogation window is compared against a larger searching window, ensuring the feature (image under the interrogation window) will be presented in the searching area. The second method employs the template matching method using normalized correlation coefficient algorithm. However, if this window preshift value was not correct, then the subsequent PIV could be made on two irrelevant interrogation windows and cause erroneous result. Each PIV result will serve as window preshift for the next PIV iteration, so that large displacement could still be catch when small interrogation window was used. As a result, only interrogation window with a power of 2 size will be accepted. It is a rather primitive implementation using the ImageJ's built-in FHT.conjugateMultiply function. The first one is the most conventional cross-correlation method. This iterative PIV program implements two methods for image correlation.
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