Multiscale method for feature preserving compression

Requirements fora good shape representation lead to descriptors that are object centered and that have the notion of scale. These representations usually take the form of shape skeletons at multiple detail levels. Classical tool for skeleton extraction is the grassfire equation, in which the process is lossless and the equation can be run backwards in order to obtain shape boundary from the shape skeleton. Many complicated strategies have been devised to assign significance to skeletal points in order to arrive at the skeleton scale space; A recent alternative approach is to introduce regularization directly to the skeleton extraction process, by combining diffusion with grassfire. Very recently, techniques: similar in spirit, which combine nonlinear smoothing of the shape boundary with the grassfire, in order to extract an axis based description, are presented independently. When diffusion is introduced into the formulation, inverse equation is no longer stable. This is the issue we will be addressing in the context of the method presented by Tari and Shah far extraction of nested symmetries from arbitrary images in arbitrary dimension. The basic tool used in the method is a specific distance function which is the steady-state solution of an elliptic boundary value problem. We present an inverse equation and show how one may obtain the whole distance surface from a sparse representation, providing a means for determining the shape boundary from the shape skeleton. The presented technique can be used for feature-preserving compression.


Multiple linear regression model with stochastic design variables
İslam, Muhammed Qamarul (Informa UK Limited, 2010-01-01)
In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.
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Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
Comparison of generative and discriminative techniques for object detection and classification
Ulusoy, İlkay (2004-01-01)
Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features is modelled. Generative and discriminative methods have very different characteristics, as well as complementary strengths and weaknesses. In this chapter we introduce new generative and discriminative models for object detection and classification based on weakly labelled training data. We use thes...
Local symmetries of shapes in arbitrary dimension
Tarı, Zehra Sibel (null; 1998-12-01)
Motivated by a need to define an object-centered reference system determined by the most salient characteristics of the shape, many methods have been proposed, all of which directly or indirectly involve an axis about which the shape is locally symmetric. Recently, a function v, called `the edge strength function', has been successfully used to determine efficiently the axes of local symmetries of 2-d shapes. The level curves of v are interpreted as successively smoother versions of the initial shape bounda...
Nested local symmetry set
Tarı, Zehra Sibel (Elsevier BV, 2000-08-01)
A local-symmetry-based representation for shapes in arbitrary dimensions and a method for its computation are presented. The method depends on analyzing the Hessian of a specific boundaryness function, v, which is computed as the minimizer of an energy functional. The method is basically a generalized ridge finding scheme in which the ridges are defined in terms of the orbit of the gradient vector del v under the action of the Hessian of v. Once the ridges are determined, the local extrema of the magnitude ...
Citation Formats
Z. S. Tarı, “Multiscale method for feature preserving compression,” 1998, vol. 3304, Accessed: 00, 2020. [Online]. Available: