Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Geometric invariants and object recognition
International Journal of Computer Vision
A note on the least squares fitting of ellipses
Pattern Recognition Letters
A Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Packing Convex Polygons into Rectangular Boxes
JCDCG '00 Revised Papers from the Japanese Conference on Discrete and Computational Geometry
Cutting a Country for Smallest Square Fit
ISAAC '02 Proceedings of the 13th International Symposium on Algorithms and Computation
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Reconstruction of Sewer Shaft Profiles from Fisheye-Lens Camera Images
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Representation and reasoning about general solid rectangles
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, we introduce a new approach for fitting of a bounding rectangle to closed regions. In this approach the coordinates of the vertices are computed directly using a closed-form solution. This approach is based on simple coordinate geometry and uses the boundary points of regions. Using a least-square approach we determine the directions of major and minor axes of the object, which gives the orientation of the object. The four vertexes of the bounding rectangle are computed by pair wise solving the four straight lines. Examples from synthetic data and some real-life data show that the approach is both accurate and efficient.