Publication

An Adaptive Total Variation Model for Image Segmentation

Pierre Vandergheynst
2005
Reports

Abstract

In our previous work, tracking the iso-level sets through total variation scale-space proved to be a very efficient tool for unsupervised segmentation. Stepping on these results, we propose a new segmentation approach in a unified total variation framework. The main idea is to use the total variation energy at each scale to drive the region merging process. We show that this total variation formulation, which was originally proposed for restoration and enhancement, is also well suited for segmentation. In addition, this energy functional can be derived from a Bayesian principle using a Markov random field prior. We demonstrate the effectiveness of our method on gray scale, noisy, color and texture images.

Official source

https://infoscience.epfl.ch/entities/publication/deaa4b02-83c7-437b-a574-acdc7814fa5b

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Pierre Vandergheynst
2005
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/deaa4b02-83c7-437b-a574-acdc7814fa5b

About this result

Related concepts (32)

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.

Object co-segmentation

In computer vision, object co-segmentation is a special case of , which is defined as jointly segmenting semantically similar objects in multiple images or video frames. It is often challenging to extract segmentation masks of a target/object from a noisy collection of images or video frames, which involves object discovery coupled with . A noisy collection implies that the object/target is present sporadically in a set of images or the object/target disappears intermittently throughout the video of interest.

Medical image computing

Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images.

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