Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hidden Markov Model approach for appearance-based 3D object recognition
Pattern Recognition Letters
Cyclic Viterbi Score for Linear Hidden Markov Models
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
2D Shape Classification Using Multifractional Brownian Motion
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Component-based discriminative classification for hidden Markov models
Pattern Recognition
Clustering-Based Construction of Hidden Markov Models for Generative Kernels
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A new HMM-based ensemble generation method for numeral recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Cyclic linear hidden Markov models for shape classification
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
3D medical volume segmentation using hybrid multiresolution statistical approaches
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
Detecting instances of shape classes that exhibit variable structure
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Given the shape information of an object, can we find visually meaningful 驴n驴 objects in an image database, which is ranked from the most similar to the nth similar one? The answer to this question depends on the complexity of the images in the database and the complexity of the objects in the query.This study represents a robust shape descriptor, which compares a given object to the objects in an image database and identifies 驴n驴 shapes, ranked from the most similar to the least similar one, in the database. The intended shape descriptor is based on the circular Hidden Markov Model (HMM) proposed by the authors of this study. Circular HMM is both ergodic and temporal. It is insensitive to size changes. Since it has no starting and terminating state, it is insensitive to the starting point of the shape boundary. The experiments, performed on 100 test shapes, indicate excellent result.