Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29352
Title: Classifying variable objects using a flexible shape model
Authors: Lanitis, Andreas 
Taylor, Chris J. 
Ahmed, T. 
Cootes, Timothy F. 
Major Field of Science: Engineering and Technology;Agricultural Sciences
Field Category: Computer and Information Sciences;Design
Keywords: Image classification;Face recognition;Image representations;Statistics
Issue Date: 4-Jul-1995
Source: Fifth International Conference on Image Processing and its Applications, 1995, Edinburgh, pp.70-74
Start page: 70
End page: 74
Conference: Fifth International Conference on Image Processing and its Applications 
Abstract: Point Distribution Models (PDMs) are statistical models which represent objects whose shape can vary. A useful feature of PDMs is their ability to capture the shape of variable objects within a training set with a small number of shape parameters. This compact and accurate parametrization can be used for the design of efficient classification systems. In this paper we describe a classification system which uses shape parameters. We have tested the system on classifying hand outlines, face outlines and hand gestures; experimental results are presented.
URI: https://hdl.handle.net/20.500.14279/29352
DOI: 10.1049/cp:19950622
Type: Conference Papers
Affiliation : Wolfson Image Analysis Unit 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

CORE Recommender
Show full item record

SCOPUSTM   
Citations 10

2
checked on Mar 14, 2024

Page view(s) 10

146
Last Week
1
Last month
2
checked on Nov 12, 2024

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons