NDLI: Car Make and Model recognition combining global and local cues

Content Provider	IEEE Xplore Digital Library
Author	AbdelMaseeh, M. Badreldin, I. Abdelkader, M.F. El Saban, M.
Copyright Year	2012
Description	Author affiliation: Microsoft Research, Advanced Tehcnology Labs in Cairo, Egypt (AbdelMaseeh, M.; Badreldin, I.; Abdelkader, M.F.; El Saban, M.)
Abstract	This paper addresses the problem of Car Make and Model recognition as an example of within-category object class recognition. In this problem, it is assumed that the general category of the object is given and the goal is to recognize the object class within the same category. As compared to general object recognition, this problem is more challenging because the variations among classes within the same category are subtle, mostly dominated by the category overall characteristics, and easily missed due to pose and illumination variations. Therefore, this specific problem may not be effectively addressed using generic object recognition approaches. In this paper, we propose a new approach to address this specific problem by combining global and local information and utilizing discriminative information labeled by a human expert. We validate our approach through experiments on recognizing the make and model of sedan cars from single view images.
Starting Page	910
Ending Page	913
File Size	383352
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Shape Feature extraction Object recognition Computer vision Solid modeling Image edge detection Conferences
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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

Local shape features for object recognition

Feature Pairs Connected by Lines for Object Recognition

Combining Geometry and Local Appearance for Object Detection

Bilateral symmetry detection based on scale invariant structure feature

Model-based object recognition using range images by combining morphological feature extraction and geometric hashing

Object Tracking with Ratio Cycles Using Shape and Appearance Cues

Feature-based object recognition — A case study for car model detection

Probabilistic 3D object recognition with both positive and negative evidences

A wavelet based multiscale detection scheme of feature points

Car Make and Model recognition combining global and local cues

Similar Documents

Local shape features for object recognition

Feature Pairs Connected by Lines for Object Recognition

Combining Geometry and Local Appearance for Object Detection

Bilateral symmetry detection based on scale invariant structure feature

Model-based object recognition using range images by combining morphological feature extraction and geometric hashing

Object Tracking with Ratio Cycles Using Shape and Appearance Cues

Feature-based object recognition — A case study for car model detection

Probabilistic 3D object recognition with both positive and negative evidences

A wavelet based multiscale detection scheme of feature points

Car Make and Model recognition combining global and local cues