NDLI: Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset

Content Provider	IEEE Xplore Digital Library
Author	Browatzki, B. Fischer, J. Graf, B. Bulthoff, H.H. Wallraven, C.
Copyright Year	2011
Description	Author affiliation: Korea University, Seoul, Korea (Wallraven, C.) \|\| Fraunhofer IPA, Stuttgart, Germany (Fischer, J.; Graf, B.) \|\| MPI for Biological Cybernetics, Tübingen, Germany (Browatzki, B.; Bulthoff, H.H.)
Abstract	Categorization of objects solely based on shape and appearance is still a largely unresolved issue. With the advent of new sensor technologies, such as consumer-level range sensors, new possibilities for shape processing have become available for a range of new application domains. In the first part of this paper, we introduce a novel, large dataset containing 18 categories of objects found in typical household and office environments—we envision this dataset to be useful in many applications ranging from robotics to computer vision. The second part of the paper presents computational experiments on object categorization with classifiers exploiting both two-dimensional and three-dimensional information. We evaluate categorization performance for both modalities in separate and combined representations and demonstrate the advantages of using range data for object and shape processing skills.
Starting Page	1189
Ending Page	1195
File Size	1457893
Page Count	7
File Format	PDF
ISBN	9781467300629
e-ISBN	9781467300636
DOI	10.1109/ICCVW.2011.6130385
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Liquids Shape Image color analysis Databases Robot sensing systems Three dimensional displays
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

Shape distributions on voxel surfaces for 3D object classification from depth images

Ensemble of shape functions for 3D object classification

3D Object Finding Using Geometrical Constraints on Depth Images

The Partial View Heat Kernel descriptor for 3D object representation

Sill image object categorization using 2D models

Sill Image Object Categorization Using 2D Objects Models

Learning-based depth estimation from 2D images using GIST and saliency

Enhanced automatic 2D–3D conversion using retinex in machine learning framework

Classification of 2D and 3D images using pyramid scale decision voting

Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset

Similar Documents

Shape distributions on voxel surfaces for 3D object classification from depth images

Ensemble of shape functions for 3D object classification

3D Object Finding Using Geometrical Constraints on Depth Images

The Partial View Heat Kernel descriptor for 3D object representation

Sill image object categorization using 2D models

Sill Image Object Categorization Using 2D Objects Models

Learning-based depth estimation from 2D images using GIST and saliency

Enhanced automatic 2D–3D conversion using retinex in machine learning framework

Classification of 2D and 3D images using pyramid scale decision voting

Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset