Name
Abstract | ||
|---|---|---|
This paper investigates rectangle fitting via optimization approaches. We summarize two basic requirements for rectangular fitting, leading to a basic model that are non-convex 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MMSP.2015.7340875 | 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) |
Keywords | Field | DocType |
quadratic programming,rectangle fitting,centroid constraints,orientation constraints | Slack variable,Mathematical optimization,Computer science,Rectangle,Maxima and minima,Quadratic programming,Centroid,Scalability | Conference |
ISSN | Citations | PageRank |
2163-3517 | 1 | 0.36 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyu Yang | 1 | 274 | 31.04 |
| Zhongyu Jiang | 2 | 6 | 2.12 |