Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The Illumination-Invariant Recognition of 3D Objects Using Local Color Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Human Faces with a Gaussian Mixture Distribution-Based Face Model
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
A Bayesian 3-D Search Engine Using Adaptive Views Clustering
IEEE Transactions on Multimedia
Principal components null space analysis for image and video classification
IEEE Transactions on Image Processing
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In this paper, a new method within the framework of principal component analysis is proposed to retrieve 3-D objects. Each object is represented by a set of 2-D images acquired from uniformly distributed viewpoints. The proposed scheme models the distribution of object images using Gaussian clusters. Clusters are created using K-means clustering algorithm. Principal component analysis based nested eigenspaces are obtained for K clusters. The eigenspaces corresponding to each cluster are used for reconfiguring the cluster and for object classification. The signature of every image in the training set is computed by projecting it onto the eigenspaces. The similarity between a query image and signatures is evaluated using the L1 distance measure for the purpose of object recognition. The eigenspaces and signatures constitute the object descriptors. The benchmark datasets such as Princeton Shape Benchmark and Amsterdam Library of Object Images are used to validate the proposed scheme. Experimental comparisons are provided to illustrate the efficacy of the proposed scheme with state-of-the-art 3-D retrieval approaches.