Dynamic Measurement of Computer Generated Image Segmentations

Authors:
Martin D. Levine;Ahmed M. Nazif
Affiliations:
Computer Vision and Robotics Laboratory, Department of Electrical Engineering, McGill University, Montreal, P.Q., Canada.;Department of Electrical Engineering, University of Cairo, Cairo, Egypt.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1985

Citing 0
Cited 19

Three-dimensional image segmentation using a split, merge and group approach

Pattern Recognition Letters
Image segmentation based on merging of sub-optimal segmentations

Pattern Recognition Letters
On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures

Pattern Recognition Letters
Image segmentation evaluation: A survey of unsupervised methods

Computer Vision and Image Understanding
A hidden Markov model-based character extraction method

Pattern Recognition
Short communication: An evaluation metric for image segmentation of multiple objects

Image and Vision Computing
Image thresholding based on Ali-Silvey distance measures

Pattern Recognition
Multilevel image segmentation with adaptive image context based thresholding

Applied Soft Computing
Unsupervised measures for parameter selection of binarization algorithms

Pattern Recognition
A knowledge-based system for extracting text-lines from mixed and overlapping text/graphics compound document images

Expert Systems with Applications: An International Journal
Two-dimensional minimum local cross-entropy thresholding based on co-occurrence matrix

Computers and Electrical Engineering
Analytical assessment of intelligent segmentation techniques for cortical tissues of MR brain images: a comparative study

Artificial Intelligence Review
Combining global and local threshold to binarize document of images

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
A subjective method for image segmentation evaluation

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding

Pattern Recognition Letters
Image multi-thresholding by combining the lattice Boltzmann model and a localized level set algorithm

Neurocomputing
Image segmentation using Atanassov's intuitionistic fuzzy sets

Expert Systems with Applications: An International Journal
Fast multilevel thresholding for image segmentation through a multiphase level set method

Signal Processing
Automatic iterative algorithm for image segmentation using a modified pulse-coupled neural network

Neurocomputing

Quantified Score

Hi-index	0.15

Visualization

Abstract

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.