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Shape descriptors can be classified by their invariance with respect to the transformations allowed in the associated shape definition. Many descriptors are invariant with respect to congruency, meaning that congruent shapes (shapes that could be translated, rotated and mirrored) will have the same descriptor (for example moment or spherical harmonic based descriptors or Procrustes analysis ...
To generalize the Hough algorithm to non-analytic curves, Ballard defines the following parameters for a generalized shape: a={y,s,θ} where y is a reference origin for the shape, θ is its orientation, and s = (s x, s y) describes two orthogonal scale factors. An algorithm can compute the best set of parameters for a given shape from edge ...
The Caltech 101 data set was used to train and test several computer vision recognition and classification algorithms. The first paper to use Caltech 101 was an incremental Bayesian approach to one-shot learning, [ 4 ] an attempt to classify an object using only a few examples, by building on prior knowledge of other classes.
The Hough transform is a feature extraction technique used in image analysis, computer vision, pattern recognition, and digital image processing. [1] [2] The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.
The snakes model is popular in computer vision, and snakes are widely used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching. A snake is an energy minimizing, deformable spline influenced by constraint and image forces that pull it towards object contours and internal forces that resist ...
Spectral shape analysis relies on the spectrum (eigenvalues and/or eigenfunctions) of the Laplace–Beltrami operator to compare and analyze geometric shapes. Since the spectrum of the Laplace–Beltrami operator is invariant under isometries, it is well suited for the analysis or retrieval of non-rigid shapes, i.e. bendable objects such as humans, animals, plants, etc.
Finally, shape contexts use circular bins, similar to those used in C-HOG blocks, but only tabulate votes on the basis of edge presence, making no distinction with regards to orientation. Shape contexts were originally used in 2001 by Belongie, Malik, and Puzicha. The testing commenced on two different data sets.
The shape context is intended to be a way of describing shapes that allows for measuring shape similarity and the recovering of point correspondences. [1] The basic idea is to pick n points on the contours of a shape. For each point p i on the shape, consider the n − 1 vectors obtained by connecting p i to all other points. The set of all ...