Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/55771
Type: | Report |
Title: | Object detection by global contour shape |
Author: | Schindler, Konrad Suter, David |
Publisher: | Monash University |
Issue Date: | 2006 |
Series/Report no.: | Technical Report; MECSE-28-2006 |
School/Discipline: | School of Computer Science |
Statement of Responsibility: | Konrad Schindler and David Suter |
Abstract: | We present a method for object detection based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against nonparametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 91% at 0.2 false positives per image on a challenging data set. |
Published version: | http://www.ecse.monash.edu.au/techrep/reports/ |
Appears in Collections: | Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.