This paper addresses issues that arise in the design of a rotation-invariant content-based image retrieval system. In our proposed procedure, we first construct a steerable multivariate sub-Gaussian model, which associates the fractional lower-order moments (FLOMs) of an image, transformed via a steerable pyramid, with those of its rotated versions. The feature extraction step consists of estim...

Since their appearance, cartoons and their creators took interest in social and political facts and figures. Often a more direct witness than a text, cartoons were quickly transformed from their initial entertaining role, to a tool to attack oppressors and reveal social injustices. To easily communicate with their public, they had to share the same codes and experiences that lived their audienc...

Separating an image into cartoon and texture components comes useful in image processing applications such as image compression, image segmentation, image inpainting. Yves Meyer’s influential cartoon texture decomposition model involves deriving an energy functional by choosing appropriate spaces and functionals. Minimizers of the derived energy functional are cartoon and texture components of ...

Comer, Mary L. Ph.D., Purdue University, December 1995. Multiresolution Image Processing Techniques with Applications in Texture Segmentation and Nonlinear Filtering. Major Professor: Edward J. Delp. We present a new algorithm for segmentation of textured images using a multiresolution Bayesian approach. The algorithm uses a multiresolution Gaussian autoregressive (MGAR) model for the pyramid r...

A pyramid of n-dimensional generalized maps is a hierarchical data structure. It can be used, for instance, in order to represent an irregular pyramid of n-dimensional images. A pyramid of generalized maps can be built by successively removing and/or contracting cells of any dimension. In this paper, we define generalized orbits, which extend the classical notion of receptive fields. Generalize...

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a convlayer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph. ...