Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation

Time and again hidden Markov models have been demonstrated to be highly effective in one-dimensional pattern recognition and classification problems such as speech recognition. A great deal of attention is now focussed on 2-D and possibly 3-D applications arising from problems encountered in computer vision in domains such as gesture, face, and handwriting recognition. Despite their widespread usage and numerous successful applications, there are few analytical results which can explain their remarkably good performance and guide researchers in selecting topologies and parameters to improve classification performance

A Corpus-based Approach to the Chinese Word Segmentation

For a society based upon laws and reason, it has become too easy for us to believe that we live in a world without them. And given that our linguistics wisdom was originally motivated by the search for rules, it seems strange that we now consider these rules to be the exceptions and take exceptions as the norm. The current task of contemporary computational linguistics is to describe these exceptions. In particular, it suffices for most language processing needs, to just describe the argument and predicate within an elementary sentence, under the framework of local grammar. Therefore, a corpus-based approach to the Chinese Word Segmentation problem is proposed, as the first step towards a local grammar for the Chinese language. The two main issues with existing lexicon-based approaches are (a) the classification of unknown character sequences, i.e. sequences that are not listed in the lexicon, and (b) the disambiguation of situations where two candidate words overlap. For (a), we propose an automatic method of enriching the lexicon by comparing candidate sequences to occurrences of the same strings in a manually segmented reference corpus, and using methods of machine learning to select the optimal segmentation for them. These methods are developed in the course of the thesis specifically for this task. The possibility of applying these machine learning method will be discussed in NP-extraction and alignment domain. (b) is approached by designing a general processing framework for Chinese text, which will be called multi-level processing. Under this framework, sentences are recursively split into fragments, according to a language-specific, but domainindependent heuristics. The resulting fragments then define the ultimate boundaries between candidate words and therefore resolve any segmentation ambiguity caused by overlapping sequences. A new shallow semantical annotation is also proposed under the frame work of multi-level processing. A word segmentation algorithm based on these principles has been implemented and tested; results of the evaluation are given and compared to the performance of previous approaches as reported in the literature. The first chapter of this thesis discusses the goals of segmentation and introduces some background concepts. The second chapter analyses the current state-of-theart approach to Chinese language segmentation. Chapter 3 proposes a new corpusbased approach to the identification of unknown words. In chapter 4, a new shallow semantical annotation is also proposed under the framework of multi-level processing

Modelling, tracking and generating human interaction behaviours in video

Intelligent virtual characters are becoming increasingly popular in en­ tertainment, educational and simulation software. A virtual charac­ ter is the creation or re-creation of a human being in an image, using computer-generated imagery. It must act and react in the environment, drawing on the disciplines of automated reasoning and planning. Creating characters with human-like behaviours that respond interactively to a real person in a video, is still a serious challenge. There are several major reasons for this. First, human motion is very complex, which makes it particularly difficult to simulate. Second, the human form is also not straightforward to design due to the large number of degrees of freedom of the motion. Third, creating novel contextual movements for virtual characters in real time is a new research area.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Generative probabilistic models for object segmentation

One of the long-standing open problems in machine vision has been the task of ‘object segmentation’, in which an image is partitioned into two sets of pixels: those that belong to the object of interest, and those that do not. A closely related task is that of ‘parts-based object segmentation’, where additionally each of the object’s pixels are labelled as belonging to one of several predetermined parts. There is broad agreement that segmentation is coupled to the task of object recognition. Knowledge of the object’s class can lead to more accurate segmentations, and in turn accurate segmentations can be used to obtain higher recognition rates. In this thesis we focus on one side of this relationship: given the object’s class and its bounding box, how accurately can we segment it? Segmentation is challenging primarily due to the huge amount of variability one sees in images of natural scenes. A large number of factors combine in complex ways to generate the pixel intensities that make up any given image. In this work we approach the problem by developing generative probabilistic models of the objects in question. Not only does this allow us to express notions of variability and uncertainty in a principled way, but also to separate the problems of model design and inference. The thesis makes the following contributions: First, we demonstrate an explicit probabilistic model of images of objects based on a latent Gaussian model of shape. This can be learned from images in an unsupervised fashion. Through experiments on a variety of datasets we demonstrate the advantages of explicitly modelling shape variability. We then focus on the task of constructing more accurate models of shape. We present a type of layered probabilistic model that we call a Shape Boltzmann Machine (SBM) for the task of modelling foreground/background (binary) and parts-based (categorical) shapes. We demonstrate that it constitutes the state-of-the-art and characterises a ‘strong’ model of shape, in that samples from the model look realistic and that it generalises to generate samples that differ from training examples. Finally, we demonstrate how the SBM can be used in conjunction with an appearance model to form a fully generative model of images of objects. We show how parts-based object segmentations can be obtained simply by performing probabilistic inference in this joint model. We apply the model to several challenging datasets and find that its performance is comparable to the state-of-the-art