Natural Image Statistics for Natural Image Segmentation

Authors:
Matthias Heiler;Christoph Schnörr
Affiliations:
-;-
Venue:
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Year:
2003

Citing 0
Cited 4

Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation

International Journal of Computer Vision
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

International Journal of Computer Vision
Variational region-based segmentation using multiple texture statistics

IEEE Transactions on Image Processing
TouchCut: Fast image and video segmentation using single-touch interaction

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

Building on recent progress in modeling 拢lter responsestatistics of natural images we integrate a statistical modelinto a variational framework for image segmentation. Incorporatedin a sound probabilistic distance measure themodel drives level sets toward meaningful segmentationsof complex textures and natural scenes. Since each regioncomprises two model parameters only the approachis computationally ef拢cient and enables the application ofvariational segmentation to a considerably larger class ofreal-world images. We validate the statistical basis of ourapproach on thousands of natural images and demonstratethat our model outperforms recent variational segmentationmethods based on second-order statistics.