32,300 research outputs found
Doctor of Philosophy in Computing
dissertationImage segmentation is the problem of partitioning an image into disjoint segments that are perceptually or semantically homogeneous. As one of the most fundamental computer vision problems, image segmentation is used as a primary step for high-level vision tasks, such as object recognition and image understanding, and has even wider applications in interdisciplinary areas, such as longitudinal brain image analysis. Hierarchical models have gained popularity as a key component in image segmentation frameworks. By imposing structures, a hierarchical model can efficiently utilize features from larger image regions and make optimal inference for final segmentation feasible. We develop a hierarchical merge tree (HMT) model for image segmentation. Motivated by the application in large-scale segmentation of neuronal structures in electron microscopy (EM) images, our model provides a compact representation of region merging hypotheses and utilizes higher order information for efficient segmentation inference. Taking advantage of supervised learning, our model is free from parameter tuning and outperforms previous state-of-the-art methods on both two-dimensional (2D) and three-dimensional EM image data sets without any change. We also extend HMT to the hierarchical merge forest (HMF) model. By identifying region correspondences, HMF utilizes inter-section information to correct intra-section errors and improves 2D EM segmentation accuracy. HMT is a generic segmentation model. We demonstrate this by applying it to natural image segmentation problems. We propose a constrained conditional model formulation with a globally optimal inference algorithm for HMT and an iterative merge tree sampling algorithm that significantly improves its performance. Experimental results show our approach achieves state-of-the-art accuracy for object-independent image segmentation. Finally, we propose a semi-supervised HMT (SSHMT) model to reduce the high demand for labeled data by supervised learning. We introduce a differentiable unsupervised loss term that enforces consistent boundary predictions and develop a Bayesian learning model that combines supervised and unsupervised information. We show that with a very small amount of labeled data, SSHMT consistently performs close to the supervised HMT with full labeled data sets and significantly outperforms HMT trained with the same labeled subsets
Dynamic modelling for image analysis
Image segmentation is an important task in many image analysis applications,
where it is an essential first stage before further analysis is possible. The levelset
method is an implicit approach to image segmentation problems. The main
advantages are that it can handle an unknown number of regions and can deal
with complicated topological changes in a simple and natural way. The research
presented in this thesis is motivated by the need to develop statistical methodologies
for modelling image data through level sets. The fundamental idea is to
combine the level-set method with statistical modelling based on the Bayesian
framework to produce an attractive approach for tackling a wider range of segmentation
problems in image analysis.
A complete framework for a Bayesian level set model is given to allow a wider
interpretation of model components. The proposed model is described based
on a Gaussian likelihood and exponential prior distributions on object area and
boundary length, and an investigation of uncertainty and a sensitivity analysis
are carried out. The model is then generalized using a more robust noise model
and more flexible prior distributions.
A new Bayesian modelling approach to object identification is introduced. The
proposed model is based on the level set method which assumes the implicit
representation of the object outlines as a zero level set contour of a higher dimensional
function. The Markov chain Monte Carlo (MCMC) algorithm is used
to estimate the model parameters, by generating approximate samples from the
posterior distribution. The proposed method is applied to simulated and real
datasets.
A new temporal model is proposed in a Bayesian framework for level-set based
image sequence segmentation. MCMC methods are used to explore the model
and to obtain information about solution behaviour. The proposed method is
applied to simulated image sequences
A Survey of Image Segmentation Based On Multi Region Level Set Method
Abstract−Image segmentation has a long tradition as one of the fundamental problems in computer vision. Level Sets are an important category of modern image segmentation techniques are based on partial differential equations (PDE), i.e. progressive evaluation of the differences among neighboring pixels to find object boundaries. Earlier method used novel level set method (LSM) for image segmentation. This method used edges and region information for segmentation of objects with weak boundaries. This method designed a nonlinear adaptive velocity and a probability-weighted stopping force by using Bayesian rule. However the difficulty of image segmentation methods based on the popular level set framework to handle an arbitrary number of regions. To address this problem the present work proposes Multi Region Level Set Segmentation which handles an arbitrary number of regions. This can be explored with addition of shape prior's considerations. In addition apriori information of these can be incorporated by using Bayesian scheme. While segmenting both known and unknown objects, it allows the evolution of enormous invariant shape priors. The image structures are considered as separate regions, when they are unknown. Then region splitting is used to obtain the number of regions and the initialization of the required level set functions. In the next step, the energy requirement of level set functions is robustly minimized and similar regions are merged in a last step. Experimental result achieves better result when compare with existing system
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks
We present a simple and effective framework for simultaneous semantic
segmentation and instance segmentation with Fully Convolutional Networks
(FCNs). The method, called BiSeg, predicts instance segmentation as a posterior
in Bayesian inference, where semantic segmentation is used as a prior. We
extend the idea of position-sensitive score maps used in recent methods to a
fusion of multiple score maps at different scales and partition modes, and
adopt it as a robust likelihood for instance segmentation inference. As both
Bayesian inference and map fusion are performed per pixel, BiSeg is a fully
convolutional end-to-end solution that inherits all the advantages of FCNs. We
demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.Comment: BMVC201
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
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