Multi-object segmentation using coupled nonparametric shape and relative pose priors

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes

Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation

Brain tissue and structure segmentation in magnetic resonance (MR) images is a fundamental problem in clinical studies of brain structure and function. Due to limitations such as low contrast, partial volume effects, and field inhomogeneities, the delineation of subcortical (basal ganglia) structures such as caudate nucleus, putamen, and thalamus from white matter, gray matter and cerebrospinal fluid (CSF) is a very challenging problem. This thesis presents a new method for simultaneous segmentation of multiple brain structures. We formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and relative poses of the structures of interest. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our coupled shape priors are learned through nonparametric multivariate kernel density estimation based on training data. Relative pose priors are modeled via standard moments. Given this framework, the segmentation problems turns into an optimization problem, which we solve using active contours. We present experimental results on synthetic data as well as on a rich set of real MR images demonstrating the effectiveness of the proposed method in segmenting basal ganglia structures as well as improvements it provides over existing approaches

Domain-specific Architectures for Data-intensive Applications

Graphs' versatile ability to represent diverse relationships, make them effective for a wide range of applications. For instance, search engines use graph-based applications to provide high-quality search results. Medical centers use them to aid in patient diagnosis. Most recently, graphs are also being employed to support the management of viral pandemics. Looking forward, they are showing promise of being critical in unlocking several other opportunities, including combating the spread of fake content in social networks, detecting and preventing fraudulent online transactions in a timely fashion, and in ensuring collision avoidance in autonomous vehicle navigation, to name a few. Unfortunately, all these applications require more computational power than what can be provided by conventional computing systems. The key reason is that graph applications present large working sets that fail to fit in the small on-chip storage of existing computing systems, while at the same time they access data in seemingly unpredictable patterns, thus cannot draw benefit from traditional on-chip storage. In this dissertation, we set out to address the performance limitations of existing computing systems so to enable emerging graph applications like those described above. To achieve this, we identified three key strategies: 1) specializing memory architecture, 2) processing data near its storage, and 3) message coalescing in the network. Based on these strategies, this dissertation develops several solutions: OMEGA, which employs specialized on-chip storage units, with co-located specialized compute engines to accelerate the computation; MessageFusion, which coalesces messages in the interconnect; and Centaur, providing an architecture that optimizes the processing of infrequently-accessed data. Overall, these solutions provide 2x in performance improvements, with negligible hardware overheads, across a wide range of applications. Finally, we demonstrate the applicability of our strategies to other data-intensive domains, by exploring an acceleration solution for MapReduce applications, which achieves a 4x performance speedup, also with negligible area and power overheads.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163186/1/abrahad_1.pd

Unsupervised Selection and Estimation of Non-Gaussian Mixtures for High Dimensional Data Analysis

Lately, the enormous generation of databases in almost every aspect of life has created a great demand for new, powerful tools for turning data into useful information. Therefore, researchers were encouraged to explore and develop new machine learning ideas and methods. Mixture models are one of the machine learning techniques receiving considerable attention due to their ability to handle efficiently and effectively multidimensional data. Generally, four critical issues have to be addressed when adopting mixture models in high dimensional spaces: (1) choice of the probability density functions, (2) estimation of the mixture parameters, (3) automatic determination of the number of components M in the mixture, and (4) determination of what features best discriminate among the different components. The main goal of this thesis is to summarize all these challenging interrelated problems in one unified model. In most of the applications, the Gaussian density is used in mixture modeling of data. Although a Gaussian mixture may provide a reasonable approximation to many real-world distributions, it is certainly not always the best approximation especially in computer vision and image processing applications where we often deal with non-Gaussian data. Therefore, we propose to use three highly flexible distributions: the generalized Gaussian distribution (GGD), the asymmetric Gaussian distribution (AGD), and the asymmetric generalized Gaussian distribution (AGGD). We are motivated by the fact that these distributions are able to fit many distributional shapes and then can be considered as a useful class of flexible models to address several problems and applications involving measurements and features having well-known marked deviation from the Gaussian shape. Recently, researches have shown that model selection and parameter learning are highly dependent and should be performed simultaneously. For this purpose, many approaches have been suggested. The vast majority of these approaches can be classified, from a computational point of view, into two classes: deterministic and stochastic methods. Deterministic methods estimate the model parameters for a set of candidate models using the Expectation-Maximization (EM) framework, then choose the model that maximizes a model selection criterion. Stochastic methods such as Markov chain Monte Carlo (MCMC) can be used in order to sample from the full a posteriori distribution with M considered unknown. Hence, in this thesis, we propose three learning techniques capable of automatically determining model complexity while learning its parameters. First, we incorporate a Minimum Message Length (MML) penalty in the model learning step performed using the EM algorithm. Our second approach employs the Rival Penalized EM (RPEM) algorithm which is able to select an appropriate number of densities by fading out the redundant densities from a density mixture. Last but not least, we incorporate the nonparametric aspect of mixture models by assuming a countably infinite number of components and using Markov Chain Monte Carlo (MCMC) simulations for the estimation of the posterior distributions. Hence, the difficulty of choosing the appropriate number of clusters is sidestepped by assuming that there are an infinite number of mixture components. Another essential issue in the case of statistical modeling in general and finite mixtures in particular is feature selection (i.e. identification of the relevant or discriminative features describing the data) especially in the case of high-dimensional data. Indeed, feature selection has been shown to be a crucial step in several image processing, computer vision and pattern recognition applications not only because it speeds up learning but also because it improves model accuracy and generalization. Moreover, the learning of the mixture parameters ( i.e. both model selection and parameters estimation) is greatly affected by the quality of the features used. Hence, in this thesis, we are trying to solve the feature selection problem in unsupervised learning by casting it as an estimation problem, thus avoiding any combinatorial search. Finally, the effectiveness of our approaches is evaluated by applying them to different computer vision and image processing applications

An object-based approach to retrieval of image and video content

Promising new directions have been opened up for content-based visual retrieval in recent years. Object-based retrieval which allows users to manipulate video objects as part of their searching and browsing interaction, is one of these. It is the purpose of this thesis to constitute itself as a part of a larger stream of research that investigates visual objects as a possible approach to advancing the use of semantics in content-based visual retrieval. The notion of using objects in video retrieval has been seen as desirable for some years, but only very recently has technology started to allow even very basic object-location functions on video. The main hurdles to greater use of objects in video retrieval are the overhead of object segmentation on large amounts of video and the issue of whether objects can actually be used efficiently for multimedia retrieval. Despite this, there are already some examples of work which supports retrieval based on video objects. This thesis investigates an object-based approach to content-based visual retrieval. The main research contributions of this work are a study of shot boundary detection on compressed domain video where a fast detection approach is proposed and evaluated, and a study on the use of objects in interactive image retrieval. An object-based retrieval framework is developed in order to investigate object-based retrieval on a corpus of natural image and video. This framework contains the entire processing chain required to analyse, index and interactively retrieve images and video via object-to-object matching. The experimental results indicate that object-based searching consistently outperforms image-based search using low-level features. This result goes some way towards validating the approach of allowing users to select objects as a basis for searching video archives when the information need dictates it as appropriate

Higher level techniques for the artistic rendering of images and video

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