NDLI: Biologically Inspired Object Categorization in Cluttered Scenes

Content Provider	IEEE Xplore Digital Library
Author	Peerasathein, T. Myung Woo Gaborski, R.S.
Copyright Year	2008
Description	Author affiliation: Rochester Inst. of Technol., Rochester (Peerasathein, T.)
Abstract	Humans have the ability to recognize objects in a cluttered scene in 100 s of milliseconds. Computer algorithms operate at a much lower performance level compared to humans. Furthermore, it has proven to be particularly difficult to develop algorithms to recognize all objects in a category, such as, all cat faces vs dog faces, because of the large in-class variability. The distinguishing features can vary significantly among different objects in the same class. A similar case can be made for other categories, such as, cars, human faces, etc. In this paper we approach this problem using a model of the human visual system. The human visual system can be divided into two major pathways, commonly called the 'what' and 'where' pathways. The 'what' pathway recognizes an object in a scene, but not its specific location. In this paper we present a biologically inspired hierarchical 'what' neural network that can successfully classify objects into categories.
Starting Page	117
Ending Page	122
File Size	808003
Page Count	6
File Format	PDF
ISBN	9780769530666
ISSN	15505219
DOI	10.1109/AIPR.2007.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Neurons Humans Neural networks Visual system Computer science Feature extraction Gabor filters Pattern recognition Object recognition ventral object categorization human visual system
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

A biologically inspired system for human posture recognition

Biologically inspired model for crater detection

Scale-hierarchical 3D object recognition in cluttered scenes

Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition

A neural 3-D object recognition architecture using optimized Gabor filters

Analysis of biologically inspired model for object recognition

A new neural network model for automatic generation of Gabor-like feature filters

Coarse-coded higher-order neural networks for PSRI object recognition

An object recognition system using self-organising neural networks

Biologically Inspired Object Categorization in Cluttered Scenes

Similar Documents

A biologically inspired system for human posture recognition

Biologically inspired model for crater detection

Scale-hierarchical 3D object recognition in cluttered scenes

Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition

A neural 3-D object recognition architecture using optimized Gabor filters

Analysis of biologically inspired model for object recognition

A new neural network model for automatic generation of Gabor-like feature filters

Coarse-coded higher-order neural networks for PSRI object recognition

An object recognition system using self-organising neural networks

Biologically Inspired Object Categorization in Cluttered Scenes