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The Catoptric Surface research project is a pioneering exploration of controlling daylight effects within built environments. In this thesis, we focus on the mirror position detection problem, which plays a vital role in achieving dynamic control over the direction of reflected light within a space. To address the challenge of mirror position detection, we employ computer vision techniques, specifically edge detection and the RANdom SAmple Consensus (RANSAC) algorithm. Edge detection is utilized to identify significant changes in intensity or color, corresponding to object boundaries, while RANSAC is applied for ellipse fitting. By iteratively selecting minimal subsets of points and fitting ellipses that meet geometric constraints, we attempt to accurately determine the position and geometry of mirrors in the catoptric array.
We evaluate two different RANSAC libraries for ellipse fitting, and our findings show that the skimage library in Python provides superior results compared to other alternatives. Additionally, we leverage the multiprocessing package to enable parallel processing, improving the efficiency of mirror detection.
We conclude that it is possible to detect single steps of mirror movement, however, reliable operation is highly sensitive to parameter settings within the computational pipeline.
Chandler Ahrens, Neal Patwari