Credit Union Division Activities, November 2002, Issue 4

Newsletter for Iowa Credit Union Division

Credit Union Division Activities, January 2003, Issue 5

Newsletter for Iowa Credit Union Division

Credit Union Division Activities, April 2003, Issue 6

Newsletter for Iowa Credit Union Division

Credit Union Division Activities, July 2003, Issue 7

Newsletter for Iowa Credit Union Division

Credit Union Division Activities, Septmeber 2003, Issue 8

Newsletter for Iowa Credit Union Division

Credit Union Division Activities, December 2003, Issue 9

Newsletter for Iowa Credit Union Division

From bargaining solutions to claims rules: a proportional approach

Is it important to negotiate on proportions rather than on numbers? To answer this question, we analyze the behavior of well-known bargaining solutions and the claims rules they induce when they are applied to a "proportionally transformed" bargaining set SP -so-called bargaining-in-proportions set. The idea of applying bargaining solutions to claims problems was already developed in Dagan and Volij (1993). They apply the bargaining solutions over a bargaining set that is the one de ned by the claims and the endowment. A comparison among our results and theirs is provided. Keywords: Bargaining problem, Claims problem, Proportional, Constrained Equal Awards, Constrained Equal Losses, Nash bargaining solution. JEL classi fication: C71, D63, D71.

The Security Blanket, April/May 2002, Issue 8

Newsletter for Information Technology Department

The Security Blanket, September/October 2002, Issue 11

Newsletter for Information Technology Department

The Security Blanket, August 2002, Issue 10

Newsletter for Information Technology Department

The Security Blanket, January/February 2003, Issue 13

Newsletter for Information Technology Department

The Security Blanket, March/April 2003, Issue 14

Newsletter for Information Technology Department

The Security Blanket, November/December 2002, Issue 12

Newsletter for Information Technology Department

Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.