Boundary based shape orientation
Pattern Recognition
Boundary based shape orientation
Pattern Recognition
An Alternative Approach to Computing Shape Orientation with an Application to Compound Shapes
International Journal of Computer Vision
A Hu moment invariant as a shape circularity measure
Pattern Recognition
A unifying approach to moment-based shape orientation and symmetry classification
IEEE Transactions on Image Processing
A definition for orientation for multiple component shapes
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Curvature weighted gradient based shape orientation
Pattern Recognition
Shapes as empirical distributions
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Using diagonals of orthogonal projection matrices for affine invariant contour matching
Image and Vision Computing
Boundary based orientation of polygonal shapes
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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Shapes provide a rich set of clues on the identity and topological properties of an object. In many imaging environments, however, the same object appears to have different shapes due to distortions such as translation, rotation, reflection, scaling, or skewing. Further, the order by which the object's feature points are scanned changes, i.e., the order of the pixels may be permuted. Relating two-dimensional shapes of the same object distorted by different affine and permutation transformations is a challenge. We introduce a shape invariant that we refer to as the intrinsic shape of an object and describe an algorithm, BLAISER, to recover it. The intrinsic shape is invariant to affine-permutation distortions. It is a uniquely defined representative of the equivalence class of all affine-permutation distortions of the same object. BLAISER computes the intrinsic shape from any arbitrarily affine-permutation distorted image of the object, without prior knowledge regarding the distortions or the undistorted shape of the object. The critical step of BLAISER is the determination of the shape orientation and we provide a detailed discussion on this topic. The operations of BLAISER are based on low-order moments of the input shape and, thus, robust to error and noise. Examples illustrate the performance of the algorithm.