Advances in computational modelling offer unprecedented potential for obtaining insights into the mechanics of cell-cell interactions. With the aid of such models, cell-level phenomena such as cell sorting and tissue self-organization are now being understood in terms of forces generated by specific sub-cellular structural components. Three-dimensional systems can behave differently from two-dimensional ones and since models cannot be validated without corresponding data, it is crucial to build accurate three-dimensional models of real cell aggregates. The lack of automated methods to determine which cell outlines in successive images of a confocal stack or time-lapse image set belong to the same cell is an important unsolved problem in the reconstruction process. This thesis addresses this problem through a contour grouping algorithm (CGA) designed to lead to unsupervised three-dimensional reconstructions of biological cells. The CGA associates contours obtained from fluorescently-labeled cell membranes in individual confocal slices using concepts from the fields of machine learning and combinatorics. The feature extraction step results in a set of association metrics. The algorithm then uses a probabilistic grouping step and a greedy-cost optimization step to produce grouped sets of contours. Groupings are representative of imaged cells and are manually evaluated for accuracy. The CGA presented here is able to produce accuracies greater than 96% when properly tuned. Parameter studies show that the algorithm is robust. That is, acceptable results are obtained under moderately varied probabilistic constraints and reasonable cost weightings. Image properties – such as slicing distance, image quality – affect the results. Sources of error are identified and enhancements based on fuzzy-logic and other optimization methods are considered. The successful grouping of cell contours, as realized here, is an important step toward the development of realistic, three-dimensional, cell-based finite element models.

Advances in computational modelling offer unprecedented potential for obtaining insights into the mechanics of cell-cell interactions. With the aid of such models, cell-level phenomena such as cell sorting and tissue self-organization are now being understood in terms of forces generated by specific sub-cellular structural components. Three-dimensional systems can behave differently from two-dimensional ones and since models cannot be validated without corresponding data, it is crucial to build accurate three-dimensional models of real cell aggregates. The lack of automated methods to determine which cell outlines in successive images of a confocal stack or time-lapse image set belong to the same cell is an important unsolved problem in the reconstruction process. This thesis addresses this problem through a contour grouping algorithm (CGA) designed to lead to unsupervised three-dimensional reconstructions of biological cells. The CGA associates contours obtained from fluorescently-labeled cell membranes in individual confocal slices using concepts from the fields of machine learning and combinatorics. The feature extraction step results in a set of association metrics. The algorithm then uses a probabilistic grouping step and a greedy-cost optimization step to produce grouped sets of contours. Groupings are representative of imaged cells and are manually evaluated for accuracy. The CGA presented here is able to produce accuracies greater than 96% when properly tuned. Parameter studies show that the algorithm is robust. That is, acceptable results are obtained under moderately varied probabilistic constraints and reasonable cost weightings. Image properties – such as slicing distance, image quality – affect the results. Sources of error are identified and enhancements based on fuzzy-logic and other optimization methods are considered. The successful grouping of cell contours, as realized here, is an important step toward the development of realistic, three-dimensional, cell-based finite element models.

Understanding machine vision can certainly improve our understanding of artificial intelligence as vision happens to be one of the basic intellectual activities of living beings. Since the notion of computation unifies the concept of a machine, computer vision can be understood as an application of modern approaches for achieving artificial intelligence, like machine learning and cognitive psychology. Computer vision mainly involves processing of different types of sensor data resulting in "perception of machines". Perception of machines plays a very important role in several artificial intelligence applications with sensors. There are numerous practical situations where we acquire sensor data for e.g. from mobile robots, security cameras, service and recreational robots. Making sense of this sensor data is very important so that we have increased automation in using the data. Tools from image processing, shape analysis and probabilistic inferences i.e. learning theory form the artillery for current generation of computer vision researchers. In my thesis I will address some of the most annoying components of two important open problems viz. object recognition and autonomous navigation that remain central in robotic, or in other words computational, intelligence. These problems are concerned with inducing computers, the abilities to recognize and navigate similar to those of humans. Object boundaries are very useful descriptors for recognizing objects. Extracting boundaries from real images has been a notoriously open problem for several decades in the vision community. In the first part I will present novel techniques for extracting object boundaries. The techniques are based on practically successful state-of-the-art Bayesian filtering framework, well founded geometric properties relating boundaries and skeletons and robust high-level shape analyses Acquiring global maps of the environments is crucial for robots to localize and be able to navigate autonomously. Though there has been a lot of progress in achieving autonomous mobility, for e.g. as in DARPA grand-challenges of 2005 and 2007, the mapping problem itself remains to be unsolved which is essential for robust autonomy in hard cases like rescue arenas and collaborative exploration. In the second part I will present techniques for merging maps acquired by multiple and single robots. We developed physics-based energy minimization techniques and also shape based techniques for scalable merging of maps. Our shape based techniques are a product of combining of high-level vision techniques that exploit similarities among maps and strong statistical methods that can handle uncertainties in Bayesian sense. / Computer and Information Science