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Hello,
I have developed a very basic yet effective collision avoidance system using contour detection (a Canny filter) and determining free and occupied space in a binary way. The outcome is a frame where the white areas represent free space and the black ones obstacles, colouring the frame in black once the first obstacle contour is detected.

Now I need to find the highest white point cluster and determine its relative position in the frame, as I mapped 10px=1° of relative heading.
However, I would like to exclude the noise-related extremely small peaks, less than a few pixels in width, so that the robot will choose a suitable course to steer.
Do you have any idea on how to do it?
Thanks

A few images to clarify

This is the frame, specifically depicting the two legs of a table. The robot will turn right or proceed in the between if it fits.

Here the example of a peak to be disregarded as due to noise

Hello,
I have developed a very basic yet effective collision avoidance system using contour detection (a Canny filter) and determining free and occupied space in a binary way. way processing frames retrieved from a monocular camera. Frames are first de-noised with a Gaussian filter and a subsequent erode/dilate to smooth the background reflections.
The outcome is a frame where the white areas represent free space and the black ones obstacles, colouring the frame in black once the first obstacle contour is detected.

Now I need to find the highest white point cluster and determine its relative position in the frame, as I mapped 10px=1° of relative heading.
However, I would like to exclude the noise-related extremely small peaks, less than a few pixels in width, so that the robot will choose a suitable course to steer.
Do you have any idea on how to do it?
Thanks

A few images to clarify

This is the frame, specifically depicting the two legs of a table. The robot will turn right or proceed in the between if it fits.

Here the example of a peak to be disregarded as due to noise