2,363 research outputs found
Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours
We introduce a robust probabilistic approach
to modeling shape contours based on a low-
dimensional, nonlinear latent variable model.
In contrast to existing techniques that use
objective functions in data space without ex-
plicit noise models, we are able to extract
complex shape variation from noisy data.
Most approaches to learning shape models
slide observed data points around fixed con-
tours and hence, require a correctly labeled
‘reference shape’ to prevent degenerate so-
lutions. In our method, unobserved curves
are reparameterized to explain the fixed data
points, so this problem does not arise. The
proposed algorithms are suitable for use with
arbitrary basis functions and are applicable
to both open and closed shapes; their effec-
tiveness is demonstrated through illustrative
examples, quantitative assessment on bench-
mark data sets and a visualization task
Dynamical models and machine learning for supervised segmentation
This thesis is concerned with the problem of how to outline regions of interest in medical images, when
the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning
and interactivity leads to a common theme of the need to balance conflicting requirements. First,
any machine learning method must strike a balance between how much it can learn and how well it
generalises. Second, interactive methods must balance minimal user demand with maximal user control.
To address the problem of weak boundaries,methods of supervised texture classification are investigated
that do not use explicit texture features. These methods enable prior knowledge about the image to
benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary
tracking, combines these image priors with efficient modes of interaction. We show the benefits of the
texture classifiers over intensity and gradient-based image models, in both classification and boundary
extraction.
To address the problem of irregular region shape, we devise a new type of statistical shape model
(SSM) that does not use explicit boundary features or assume high-level similarity between region
shapes. First, the models are used for shape discrimination, to constrain any segmentation framework
by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation
frameworks to draw shapes from a prior distribution. The generative models also include
novel methods to constrain shape generation according to information from both the image and user
interactions.
The shape models are first evaluated in terms of discrimination capability, and shown to out-perform
other shape descriptors. Experiments also show that the shape models can benefit a standard type of
segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape
models in supervised segmentation frameworks, and evaluate their benefits in user trials
Globally-Coordinated Locally-Linear Modeling of Multi-Dimensional Data
This thesis considers the problem of modeling and analysis of continuous, locally-linear, multi-dimensional spatio-temporal data. Our work extends the previously reported theoretical work on the global coordination model to temporal analysis of continuous, multi-dimensional data. We have developed algorithms for time-varying data analysis and used them in full-scale, real-world applications. The applications demonstrated in this thesis include tracking, synthesis, recognitions and retrieval of dynamic objects based on their shape, appearance and motion. The proposed approach in this thesis has advantages over existing approaches to analyzing complex spatio-temporal data. Experiments show that the new modeling features of our approach improve the performance of existing approaches in many applications. In object tracking, our approach is the first one to track nonlinear appearance variations by using low-dimensional representation of the appearance change in globally-coordinated linear subspaces. In dynamic texture synthesis, we are able to model non-stationary dynamic textures, which cannot be handled by any of the existing approaches. In human motion synthesis, we show that realistic synthesis can be performed without using specific transition points, or key frames
Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other features, and is corrupted by large amounts of noise. To
handle this kind of data, flexible shape models are desired that can accurately
follow the object boundaries. Popular models such as Active Shape and Active
Appearance models lack the necessary flexibility for this task, while recent
approaches such as the Recursive Compositional Models make model
simplifications in order to obtain computational guarantees. This paper
investigates a hierarchical Bayesian model of shape and appearance in a
generative setting. The input data is explained by an object parsing layer,
which is a deformation of a hidden PCA shape model with Gaussian prior. The
paper also introduces a novel efficient inference algorithm that uses informed
data-driven proposals to initialize local searches for the hidden variables.
Applied to the problem of object parsing from structured point clouds such as
edge detection images, the proposed approach obtains state of the art parsing
errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure
Deep Normalizing Flows for State Estimation
Safe and reliable state estimation techniques are a critical component of
next-generation robotic systems. Agents in such systems must be able to reason
about the intentions and trajectories of other agents for safe and efficient
motion planning. However, classical state estimation techniques such as
Gaussian filters often lack the expressive power to represent complex
underlying distributions, especially if the system dynamics are highly
nonlinear or if the interaction outcomes are multi-modal. In this work, we use
normalizing flows to learn an expressive representation of the belief over an
agent's true state. Furthermore, we improve upon existing architectures for
normalizing flows by using more expressive deep neural network architectures to
parameterize the flow. We evaluate our method on two robotic state estimation
tasks and show that our approach outperforms both classical and modern deep
learning-based state estimation baselines.Comment: Accepted to FUSION 202
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