A contour-oriented approach to shape analysis
A contour-oriented approach to shape analysis
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
An application of wavelet-based affine-invariant representation
Pattern Recognition Letters
Geometric and Illumination Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dyadic Wavelet Affine Invariant Function for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Recognizing Planar Objects Using Invariant Image Features
Recognizing Planar Objects Using Invariant Image Features
Affine invariants for object recognition using the wavelet transform
Pattern Recognition Letters
Wavelet and ridgelet transforms for pattern recognition and denoising
Wavelet and ridgelet transforms for pattern recognition and denoising
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
Shape retrieval using triangle-area representation and dynamic space warping
Pattern Recognition
Planar shape representation and matching under projective transformation
Computer Vision and Image Understanding
| Hi-index | 0.00 |
In this paper, a multiscale wavelet-based algorithm formatching stand-alone shapes is developed. The algorithmuses the Dyadic Wavelet Transform (DWT) to decompose ashapeýs boundary into multi-scale levels. Features are extractedby calculating the curve moment invariants of theapproximation coefficients. If the measured dissimilarity issmall, then the shapes are globally similar. Local similarityis investigated by calculating the normalized cross correlationof the 1-D triangle area representation of the detailcoefficients. The presented algorithm not only finds similarshapes, but it also can easily distinguish between seeminglysimilar shapes. The algorithm is invariant to the affine transformationand to the starting point variation of the shapecontour.