E-Thesis 87 views 57 downloads
Adaptive Learning for Segmentation and Detection / Jingjing Deng
Swansea University Author: Deng, Jingjing Eddy.
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Segmentation and detection are two fundamental problems in computer vision and medical image analysis, they are intrinsically interlinked by the nature of machine learning based classification, especially supervised learning methods. Many automatic segmentation methods have been proposed which heavi...
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Segmentation and detection are two fundamental problems in computer vision and medical image analysis, they are intrinsically interlinked by the nature of machine learning based classification, especially supervised learning methods. Many automatic segmentation methods have been proposed which heavily rely on hand-crafted discriminative features for specific geometry and powerful classifier for delinearating the foreground object and background region. The aimof this thesis is to investigate the adaptive schemes that can be used to derive efficient interactive segmentation methods for medical imaging applications, and adaptive detection methods for addressing generic computer vision problems. In this thesis, we consider adaptive learning as a progressive learning process that gradually builds the model given sequential supervision from user interactions. The learning process could be either adaptive re-training for smallscale models and datasets or adaptive fine-tuning for medium-large scale. In addition, adaptive learning is considered as a progressive learning process that gradually subdivides a big and difficult problem into a set of smaller but easier problems, where a final solution can be found via combining individual solvers consecutively. We first show that when discriminative features are readily available, the adaptive learning scheme can lead to an efficient interactive method for segmenting the coronary artery, where promising segmentation results can be achieved with limited user intervention. We then present a more general interactive segmentation method that integrates a CNN based cascade classifier and a parametric implicit shape representation. The features are self-learnt during the supervised training process, no hand-crafting is required.Then, the segmentation can be obtained via imposing a piecewise constant constraint to thedetection result through the proposed shape representation using region based deformation.Finally, we show the adaptive learning scheme can also be used to address the face detection problem in an unconstrained environment, where two CNN based cascade detectors are proposed. Qualitative and quantitative evaluations of proposed methods are reported, and show theefficiency of adaptive schemes for addressing segmentation and detection problems in general. Continues...
A selection of third party content is redacted or is partially redacted from this thesis.Pages: 26, 31, 33, 35, 36, 38, 110, 111, 112, 113, 124, 126.
Segmentation, Image Analysis, Computer Science
College of Science