NDLI: Curve representing and matching based on feature points and minimal area threshold

Content Provider	IEEE Xplore Digital Library
Author	Zhang Gui-mei Ren Wei Miao Jun
Copyright Year	2009
Description	Author affiliation: Nanchang Hangkong University, Jiangxi, 330063, China (Zhang Gui-mei; Ren Wei; Miao Jun)
Abstract	A new method for representing and recognizing the contour curve is presented in this paper. First, feature points are employed to describe contour curve preliminarily; Then the sample points of the sub-curve are introduced to describe contour more, they are obtained based on the precision requirement using the given minimal area threshold. A new recognition vector of sample points is defined, and a novel recognition vector matrix is constructed based on the recognition vector of sample points; Last the dissimilarity measure of the corresponding sub-curves is calculated by compared the recognition vector matrix. The curves are recognized by recognizing their each sub-curve. The method match object and model from simple to complex, thus many redundancies calculation are avoided. The experiment results show the algorithm is efficient and feasible.
Starting Page	592
Ending Page	596
File Size	117702
Page Count	5
File Format	PDF
ISBN	9781424436996
DOI	10.1109/CADCG.2009.5246830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feathers Shape Binary trees recognition vector recognition vector matrix feature points Feature extraction Circuit noise Data mining Object recognition Clocks curve representation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An efficient angle-based shape matching approach towards object recognition

Feature-Points Based Shape Matching

Corner-Based Feature for Object Recognition

Vector contour representation for object recognition in neural networks

Object recognition based on modified invariant moments

Compact centroid distance shape descriptor based on object area normalization

View-based object matching

Region based on object recognition in 3D scenes

Model-based shape matching with structural feature grouping

Curve representing and matching based on feature points and minimal area threshold

Similar Documents

An efficient angle-based shape matching approach towards object recognition

Feature-Points Based Shape Matching

Corner-Based Feature for Object Recognition

Vector contour representation for object recognition in neural networks

Object recognition based on modified invariant moments

Compact centroid distance shape descriptor based on object area normalization

View-based object matching

Region based on object recognition in 3D scenes

Model-based shape matching with structural feature grouping

Curve representing and matching based on feature points and minimal area threshold