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

KEY-FRAME APPEARANCE ANALYSIS FOR VIDEO SURVEILLANCE

Tracking moving objects is a commonly used approach for understanding surveillance video. However, by focusing on only a few key-frames, it is possible to effectively perform tasks such as image segmentation, recognition, object detection, and so on. In this dissertation we describe several methods for appearance analysis of key-frames, which includes region-based background subtraction, a new method for recognizing persons based on their overall extrinsic appearance, regardless of their (upright) pose, and appearance-based local change detection. To encode the spatial information into an appearance model, we introduce a new feature, path-length, which is defined as the normalized length of the shortest path in the silhouette. The method of appearance recognition uses kernel density estimation (KDE) of probabilities associated with color/path-length profiles and the Kullback-Leibler (KL) distance to compare such profiles with possible models. When there are more than one profile to match in one frame, we adopt multiple matching algorithm enforcing a 1-to-1 constraint to improve performance. Through a comprehensive set of experiments, we show that with suitable normalization of color variables this method is robust under conditions varying viewpoints, complex illumination, and multiple cameras. Using probabilities from KDE we also show that it is possible to easily spot changes in appearance, for instance caused by carried packages. Lastly, an approach for constructing a gallery of people observed in a video stream is described. We consider two scenarios that require determining the number and identity of participants: outdoor surveillance and meeting rooms. In these applications face identification is typically not feasible due to the low resolution across the face. The proposed approach automatically computes an appearance model based on the clothing of people and employs this model in constructing and matching the gallery of participants. In the meeting room scenario we exploit the fact that the relative locations of subjects are likely to remain unchanged for the whole sequence to construct more a compact gallery