| 内容提要: |
Abstract—This paper investigates rectangle fitting via optimization approaches. We summarize two basic requirements
for rectangular fitting, leading to a basic model that are nonconvex and difficult to attack. To avoid potential trapping of local
minima, we extend the basic model with centroid and orientation
constraints into a quadratic programming. To achieve reliable
fitting from noisy points, slack variables are introduced to soften
hard constraints. The scalability to problem size are further
addressed by careful selecting only a small fraction of slack
variables. Results on clean dataset, noisy dataset, and practical
data show that our method is able to reliably fit rectangles for
various kinds of data.
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