Fast and robust quantification method to measure glass liquid flow speed (viscosity) using low signal-to-noise videos

A system comprising:

• a computing device comprising one or more processors and at least one memory component, the at least one memory component storing instructions that, when executed by the one or more processors, cause the computing device to:
• access a plurality of images from a video;
• crop an area of interest from at least one of the plurality of images;
• concatenate the cropped area of interest from each of the plurality of images into a single concatenated image;
• preprocess the single concatenated image to improve image quality;
• perform template matching on the preprocessed single concatenated image using a user-defined template to identify a plurality of matching locations;
• determine boundary indexes from the plurality of matching locations;
• crop a region from the preprocessed single concatenated image based on the boundary indexes, the cropped region containing a useful signal;
• calculate a plurality of intensity gradients in the cropped region;
• identify a pixel with a maximum gradient for a plurality of column indexes;
• determine an average row index for columns with multiple pixels having the maximum gradient;
• perform iterative linear regression on row and column indexes of selected pixels to determine a slope of a line fitting the selected pixels;
• and
• quantify a flow speed of glass liquid based on a final slope of the line after substantial convergence of the iterative linear regression.

A system for quantifying glass liquid flow speed from a video, the system comprising:

• a computing device comprising a processor and memory, the memory storing instructions that, when executed by the processor, cause the computing device to:
• access a plurality of images from the video;
• crop a center column from each image;
• concatenate the cropped center columns to form a preprocessed concatenated image;
• preprocess by applying sharpening, histogram equalization, segmentation, and thresholding to the concatenated image to improve image quality;
• perform template matching on the preprocessed concatenated image using a diagonal line template to identify a plurality of matching locations;
• determine boundary indexes of the matching locations;
• crop an area from the preprocessed concatenated image based on the boundary indexes, the cropped area containing a signal;
• calculate column-wise gray level intensity gradients in the cropped area;
• identify a pixel with a maximum gradient for a plurality of column indexes;
• determine an average row index for columns with multiple pixels having the maximum gradient;
• apply an iterative linear regression model with noise removal in each iteration on row and column indexes of selected pixels to determine a slope of a line fitting the selected pixels;
• remove pixels having an absolute distance from a regression line greater than two standard deviations of distances between each pixel and the regression line;
• and
• quantify a flow speed of molten glass based on a final slope of the line after substantial convergence of the iterative linear regression model,
• wherein the final slope represents the flow speed, the flow speed is inversely proportional to a viscosity of the molten glass, and a difference between slopes of two consecutive linear regressions is less than 0.05.