Publication

Multiresolution Segmentation of Natural Images: From linear to Non-Linear Scale-Space Representations

Pierre Vandergheynst, Oscar Divorra Escoda
2004
Journal Articles

Abstract

In this paper, we introduce a framework that merges classical ideas borrowed from scale-space and multi-resolution segmentation with non-linear partial differential equations. A non-linear scale-space stack is constructed by means of an appropriate diffusion equation. This stack is analyzed and a tree of coherent segments is constructed based on relationships between different scale layers. Pruning this tree proves to be a very efficient tool for unsupervised segmentation of different classes of images (e.g. natural, medical ...). This technique is light on the computational point of view and can be extended to non-scalar data in a straightforward manner.

Official source

https://infoscience.epfl.ch/entities/publication/23a1af68-e085-4119-976a-3df8f35f5819

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Pierre Vandergheynst, Oscar Divorra Escoda
2004
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/23a1af68-e085-4119-976a-3df8f35f5819

About this result

Related concepts (32)

Scale space

Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the scale-space representation, parametrized by the size of the smoothing kernel used for suppressing fine-scale structures.

Image segmentation

In and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

Partial differential equation

In mathematics, a partial differential equation (PDE) is an equation which computes a function between various partial derivatives of a multivariable function. The function is often thought of as an "unknown" to be solved for, similar to how x is thought of as an unknown number to be solved for in an algebraic equation like x2 − 3x + 2 = 0. However, it is usually impossible to write down explicit formulas for solutions of partial differential equations.

Related publications (32)

Multiresolution Segmentation of Natural Images: From linear to Non-Linear Scale-Space Representations

Chat with Graph Search

Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models

Diffusion Processes on Implicit Manifolds

Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries