I have a set of binarized pictures (like the following one) of the water surface in a small portion of flume, taken during a laboratory experiment. The water surface is represented by the thin curved line, but as you can see the picture is "corrupted" by other big white patches representing light reflections on the flume glass walls, that I could not get rid of.
I am working on a script that tries to detect the position of the water surface and fits it with a spline. I tried to remove the extra patches by filtering them by area (i.e. by removing the patches with pixel area lower than a threshold) before fitting the spline; but I am stuck because for many pictures the area of the water surface patch is roughly the same as those of the extra patches, so they are both removed by the filter. Moreover, occasionally the extra patches intersect the surface patch, as is the case in the picture below.
Can you help me figuring out a method to fit a curve to identify the surface, that is as less sensitive as possible to the presence of the extra patches? Thank you.I have a set of binarized pictures (like the following one) of the water surface in a small portion of flume, taken during a laboratory experiment. The water surface is represented by the thin curved line, but as you can see the picture is "corrupted" by other big white patches representing light reflections on the flume glass walls, that I could not get rid of.
I am working on a script that tries to detect the position of the water surface and fits it with a spline. I tried to remove the extra patches by filtering them by area (i.e. by removing the patches with pixel area lower than a threshold) before fitting the spline; but I am stuck because for many pictures the area of the water surface patch is roughly the same as those of the extra patches, so they are both removed by the filter. Moreover, occasionally the extra patches intersect the surface patch, as is the case in the picture below.
Can you help me figuring out a method to fit a curve to identify the surface, that is as less sensitive as possible to the presence of the extra patches? Thank you. I have a set of binarized pictures (like the following one) of the water surface in a small portion of flume, taken during a laboratory experiment. The water surface is represented by the thin curved line, but as you can see the picture is "corrupted" by other big white patches representing light reflections on the flume glass walls, that I could not get rid of.
I am working on a script that tries to detect the position of the water surface and fits it with a spline. I tried to remove the extra patches by filtering them by area (i.e. by removing the patches with pixel area lower than a threshold) before fitting the spline; but I am stuck because for many pictures the area of the water surface patch is roughly the same as those of the extra patches, so they are both removed by the filter. Moreover, occasionally the extra patches intersect the surface patch, as is the case in the picture below.
Can you help me figuring out a method to fit a curve to identify the surface, that is as less sensitive as possible to the presence of the extra patches? Thank you. image processing, digital image processing MATLAB Answers — New Questions

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