A Non-linear Generalisation of PDMs using Polynomial Regression

Sozou, Peter D.; Cootes, T. F.; Taylor, Chris; Di-Mauro, E. C.

doi:10.5244/c.8.39

Cited by 23 publications

(17 citation statements)

References 12 publications

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“…1 are examples from a synthetic class of shapes. We have previously described in detail how they are generated [2]. Training a linear PDM on 200 tadpoles, we found that three modes of variation were needed to explain 95% of the variance in the training data [2].…”

Section: Tadpole Datamentioning

confidence: 96%

“…We have recently described [2] a modified class of models known as a Polynomial Regression PDM (PRPDM). The approach is motivated by noting that the eigen-analysis used to extract the modes of variation for a standard PDM can be conceptualised (however implemented) as a process of sequentially fitting modes.…”

Section: The Polynomial Regression Modelmentioning

confidence: 99%

“…When a PRPDM was trained on the tadpole data, using polynomials of degree 2 (i.e quadratics) for each mode, two modes of variation were sufficient to describe 95% of the variance in the training set [2]. A scattergram of the two shape parameters of the PRPDM is shown in Fig.…”

Section: Tadpole Datamentioning

confidence: 99%

“…We have recently [2] described a polynomial regression PDM (PRPDM) which succeeds in capturing limited non-linear shape variability. In this paper we present a new method for modelling non-linear shape variability which involves using a multi-layer perceptron to perform non-linear principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Non-Linear Point Distribution Modelling using a Multi-Layer Perceptron.

Sozou¹,

Cootes²,

Taylor³

et al. 1995

Procedings of the British Machine Vision Conference 1995

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Objects of the same class sometimes exhibit variation in shape. This shape variation has previously been modelled by means of point distribution models (PDMs) in which there is a linear relationship between a set of shape parameters and the positions of points on the shape. A polynomial regression generalization of PDMs, which succeeds in capturing certain forms of non-linear shape variability, has also been described. Here we present a new form of PDM, which uses a multi-layer perceptron to carry out non-linear principal component analysis. We compare the performance of the new model with that of the existing models on two classes of variable shape: one exhibits bending, and the other exhibits complete rotation. The linear PDM fails on both classes of shape; the polynomial regression model succeeds for the first class of shapes but fails for the second; the new multi-layer perceptron model performs well for both classes of shape. The new model is the most general formulation for PDMs which has been proposed to date.

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Section: Tadpole Datamentioning

confidence: 96%

Section: The Polynomial Regression Modelmentioning

confidence: 99%

Section: Tadpole Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non-Linear Point Distribution Modelling using a Multi-Layer Perceptron.

Sozou¹,

Cootes²,

Taylor³

et al. 1995

Procedings of the British Machine Vision Conference 1995

View full text Add to dashboard Cite

show abstract

“…Extensions to cover non-rigid shape deformations involved using a single global shape model in combination with nonlinear PCA techniques [25,26] or a probabilistic model [4], as well as the use of multiple local-linear shape models [7,11]. Other work considered joint linear shape and texture models [3,6,15] to capture underlying dependencies in a principled way.…”

Section: Previous Workmentioning

confidence: 99%

A mixed generative-discriminative framework for pedestrian classification

Enzweiler¹,

Gavrila

2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

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This paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classification performance of a discriminative model. Our generative model captures prior knowledge about the pedestrian class in terms of a number of probabilistic shape and texture models, each attuned to a particular pedestrian pose. Active learning provides the link between the generative and discriminative model, in the sense that the former is selectively sampled such that the training process is guided towards the most informative samples of the latter.In large-scale experiments on real-world datasets of tens of thousands of samples, we demonstrate a significant improvement in classification performance of the combined generative-discriminative approach over the discriminative-only approach (the latter exemplified by a neural network with local receptive fields and a support vector machine using Haar wavelet features).

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Regression Models of Atlas Appearance

Rohlfing

Sullivan

Pfefferbaum

2009

Lecture Notes in Computer Science

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Models of object appearance based on principal components analysis provide powerful and versatile tools in computer vision and medical image analysis. A major shortcoming is that they rely entirely on the training data to extract principal modes of appearance variation and ignore underlying variables (e.g., subject age, gender). This paper introduces an appearance modeling framework based instead on generalized multi-linear regression. The training of regression appearance models is controlled by independent variables. This makes it straightforward to create model instances for specific values of these variables, which is akin to model interpolation. We demonstrate the new framework by creating an appearance model of the human brain from MR images of 36 subjects. Instances of the model created for different ages are compared with average shape atlases created from age-matched sub-populations. Relative tissue volumes vs. age in models are also compared with tissue volumes vs. subject age in the original images. In both experiments, we found excellent agreement between the regression models and the comparison data. We conclude that regression appearance models are a promising new technique for image analysis, with one potential application being the representation of a continuum of mutually consistent, age-specific atlases of the human brain.

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A Non-linear Generalisation of PDMs using Polynomial Regression

Cited by 23 publications

References 12 publications

Non-Linear Point Distribution Modelling using a Multi-Layer Perceptron.

Non-Linear Point Distribution Modelling using a Multi-Layer Perceptron.

A mixed generative-discriminative framework for pedestrian classification

Regression Models of Atlas Appearance

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