Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
A Confidence Measure For Boundary Detection And Object Selection, William A. Barrett, Eric N. Mortensen
A Confidence Measure For Boundary Detection And Object Selection, William A. Barrett, Eric N. Mortensen
Faculty Publications
We introduce a confidence measure that estimates the assurance that a graph arc (or edge) corresponds to an object boundary in an image. A weighted, planar graph is imposed onto the watershed lines of a gradient magnitude image and the confidence measure is a function of the cost of fixed-length paths emanating from and extending to each end of a graph arc. The confidence measure is applied to automate the detection of object boundaries and thereby reduces (often greatly) the time and effort required for object boundary definition within a user-guided image segmentation environment.
Combining Cross-Validation And Confidence To Measure Fitness, Tony R. Martinez, D. Randall Wilson
Combining Cross-Validation And Confidence To Measure Fitness, Tony R. Martinez, D. Randall Wilson
Faculty Publications
Neural network and machine learning algorithms often have parameters that must be tuned for good performance on a particular task. Leave-one-out cross-validation (LCV) accuracy is often used to measure the fitness of a set of parameter values. However, small changes in parameters often have no effect on LCV accuracy. Many learning algorithms can measure the confidence of a classification decision, but often confidence alone is an inappropriate measure of fitness. This paper proposes a combined measure of Cross- Validation and Confidence (CVC) for obtaining a continuous measure of fitness for sets of parameters in learning algorithms. This paper also proposes …