Computer-Aided Design
On the Detection of the Axes of Symmetry of Symmetric and Almost Symmetric Planar Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of generalized principal axes is rotationally symmetric shapes
Pattern Recognition
Machine vision
Generalized Affine Invariant Image Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Techniques for Assessing Polygonal Approximations of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Rotation Angle Estimator
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the minimum-area encasing rectangle for an arbitrary closed curve
Communications of the ACM
Robot Vision
Robust normalization of silhouettes for recognition applications
Pattern Recognition Letters - Special issue: Discrete geometry for computer imagery (DGCI'2002)
Notes on shape orientation where the standard method does not work
Pattern Recognition
New Measure for Shape Elongation
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Affine-permutation invariance of 2-D shapes
IEEE Transactions on Image Processing
On the Orientability of Shapes
IEEE Transactions on Image Processing
Environmental surface boundary tracking and description using a UAV with vision
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Orientation and anisotropy of multi-component shapes from boundary information
Pattern Recognition
Robust shape normalization of 3D articulated volumetric models
Computer-Aided Design
Hi-index | 0.01 |
The computation of a shape's orientation is a common task in the area of computer vision and image processing, being used for example to define a local frame of reference and is helpful for recognition and registration, robot manipulation, etc. It is usually an initial step or a part of data preprocessing in many image processing and computer vision tasks. Thus, it is important to have a good solution for shape orientation because an unsuitable solution could lead to a big cumulative error at the end of the computing process. There are several approaches to the problem-most of them could be understood as the 'area based' ones, or at least they do not take into account all the boundary points (if a shape orientation measure is based on its encasing rectangle, only the convex hull points count, for example). Thus, the demand for a pure 'boundary based' method, where the orientation of the shape is dependent on the boundary points seems to be very reasonable. Such a method is presented in this paper. We are initially focused on the shapes having polygonal boundaries. We define the orientation of a polygonal shape by the line that maximises the total sum of squared lengths of all the boundary edge projections onto this line. The advantages and limitations of the new method are analysed. Next, we suggested how the method can be adapted in order to be applicable to a wider class than the initial method is. Finally, we introduced another modification of the method in such a way that the modified method can be applied to shapes with arbitrary boundaries. Several illustrative experiments are provided.