Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Method of Normalization to Determine Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Affine Invariant Image Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformation invariants in object recognition
Computer Vision and Image Understanding
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Reliable and Efficient Pattern Matching Using an Affine Invariant Metric
International Journal of Computer Vision
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Shape Matching: Similarity Measures and Algorithms
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Evaluation of Nine Similarity Measures Used in Rigid Registration
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Integral Invariants for Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context
Journal of Mathematical Imaging and Vision
Active object recognition based on Fourier descriptors clustering
Pattern Recognition Letters
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Shape signature matching for object identification invariant to image transformations and occlusion
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Image registration and object recognition using affine invariants and convex hulls
IEEE Transactions on Image Processing
A new geometric descriptor for symbols with affine deformations
Pattern Recognition Letters
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One of the most usual strategies for tackling the 3D object recognition problem consists of representing the objects by their appearance. 3D recognition can therefore be converted into a 2D shape recognition matter. This paper is focused on carrying out an in depth qualitative and quantitative analysis with regard to the performance of 2D shape recognition methods when they are used to solve 3D object recognition problems. Well known shape descriptors (contour and regions) and 2D similarities measurements (deterministic and stochastic) are thus combined to evaluate a wide range of solutions. In order to quantify the efficiency of each approach we propose three parameters: Hard Recognition Rate (Hr), Weak Recognition Rate (Wr) and Ambiguous Recognition Rate (Ar). These parameters therefore open the evaluation to active recognition methods which deal with uncertainty. Up to 42 combined methods have been tested on two different experimental platforms using public database models. A detailed report of the results and a discussion, including detailed remarks and recommendations, are presented at the end of the paper.