Add to ORKGThis paper presents a new framework for shape modeling and analysis. A shape instance is described by a curvature-based shape descriptor. A Profile Hidden Markov Model (PHMM) is then built on such descriptors to represent a class of simi-lar shapes. PHMMs are a particular type of Hidden Markov Models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions, andmissing parts. The sparseness of the PHMM structure also provides effi-cient inference and learning algorithms for shape modeling and analysis. Our experimental results on corpus callosum images show the effectiveness and robustness of this new frame-work. Index Terms — Image shape an...
We present a technique using Markov models with spectral features for recognizing 2D shapes. We anal...
Abstract. Characterizing the geometric conformation of object complexes requires the description of ...
We study the problem of detecting the shape anomalies In this paper. Our shape anomaly detection alg...
An ideal shape model should be both invariant to global transformations and robust to local distorti...
This paper is concerned with an application of Hidden Markov Models (HMMs) to the generation of sha...
In this paper we propose a new approach for surface representation. Generative models are exploited ...
In this paper, we analyse the properties of the standard circular hidden Markov model (HMM) on 2D sh...
Given the shape information of an object, can we find visually meaningful "n " objects in ...
Hidden State Shape Models (HSSMs) [2], a variant of Hidden Markov Models (HMMs) [9], were proposed t...
This study deals with the shape recognition problem using the Hidden Markov Model (HMM). In many pat...
The goal of this paper is to present a weighted likelihood discriminant for minimum error shape clas...
We present a technique using Markov models with spectral features for recognizing 2D shapes. We anal...
Shape descriptions and the corresponding matching techniques must be robust to noise and invariant ...
We present an integrated approach in modelling, extracting, detecting and classifying deformable con...
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key ...
We present a technique using Markov models with spectral features for recognizing 2D shapes. We anal...
Abstract. Characterizing the geometric conformation of object complexes requires the description of ...
We study the problem of detecting the shape anomalies In this paper. Our shape anomaly detection alg...
An ideal shape model should be both invariant to global transformations and robust to local distorti...
This paper is concerned with an application of Hidden Markov Models (HMMs) to the generation of sha...
In this paper we propose a new approach for surface representation. Generative models are exploited ...
In this paper, we analyse the properties of the standard circular hidden Markov model (HMM) on 2D sh...
Given the shape information of an object, can we find visually meaningful "n " objects in ...
Hidden State Shape Models (HSSMs) [2], a variant of Hidden Markov Models (HMMs) [9], were proposed t...
This study deals with the shape recognition problem using the Hidden Markov Model (HMM). In many pat...
The goal of this paper is to present a weighted likelihood discriminant for minimum error shape clas...
We present a technique using Markov models with spectral features for recognizing 2D shapes. We anal...
Shape descriptions and the corresponding matching techniques must be robust to noise and invariant ...
We present an integrated approach in modelling, extracting, detecting and classifying deformable con...
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key ...
We present a technique using Markov models with spectral features for recognizing 2D shapes. We anal...
Abstract. Characterizing the geometric conformation of object complexes requires the description of ...
We study the problem of detecting the shape anomalies In this paper. Our shape anomaly detection alg...