Real time object recognition using K-nearest neighbor in parametric eigenspace

Authors:
Myung A. Kang;JongMin Kim
Affiliations:
Dept. Computer Science and Engineering, KwangJu University, Korea;Computer Science and Statistic Graduate School, Chosun University, Korea
Venue:
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Year:
2007

Citing 8
Cited 1

Visual learning and recognition of 3-D objects from appearance

International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Facial Expression Recognition Using a State-Based Model of Spatially-Localised Facial Dynamics

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Three dimensional gesture recognition using modified matching algorithm

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Network-Based face recognition system using multiple images

PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems

A novel human computer interface based on hand gesture recognition using computer vision techniques

Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia

Quantified Score

Hi-index	0.01

Visualization

Abstract

Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in a image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the k-nearest neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images inputted on a continual input image. However, the robustness of recognition strategies using PCA depends on several factors, including illumination. When scene constancy is not secured due to varying illumination conditions, the learning performance the feature detector can be compromised, undermining the recognition quality. This paper proposes a new PCA recognition in which database of objects can be detected under different illuminations between input images and the model images.