Extraction of Unfoliaged Trees from Terrestrial Image Sequences

This thesis presents a generative statistical approach for the fully automatic three-dimensional (3D) extraction and reconstruction of unfoliaged deciduous trees from wide-baseline image sequences. Tree models improve the realism of 3D Geoinformation systems (GIS) by adding a natural touch. Unfoliaged trees are, however, difficult to reconstruct from images due to partially weak contrast, background clutter, occlusions, and particularly the possibly varying order of branches in images from different viewpoints. The proposed approach combines generative modeling by L-systems and statistical maximum a posteriori (MAP) estimation for the extraction of the 3D branching structure of trees. Background estimation is conducted by means of mathematical (gray scale) morphology as basis for generative modeling. A Gaussian likelihood function based on intensity differences is employed to evaluate the hypotheses. A mechanism has been devised to control the sampling sequence of multiple parameters in the Markov Chain considering their characteristics and the performance in the previous step. A tree is classified into three typical branching types after the extraction of the first level of branches and more specific Production Rules of L-systems are used accordingly. Generic prior distributions for parameters are refined based on already extracted branches in a Bayesian framework and integrated into the MAP estimation. By these means most of the branching structure besides tiny twigs can be reconstructed. Results are presented in the form of VRML (Virtual Reality Modeling Language) models demonstrating the potential of the approach as well as its current shortcomings.Diese Dissertationsschrift stellt einen generativen statistischen Ansatz für die vollautomatische drei-dimensionale (3D) Extraktion und Rekonstruktion unbelaubter Laubbäume aus Bildsequenzen mit großer Basis vor. Modelle für Bäume verbessern den Realismus von 3D Geoinformationssystemen (GIS), indem sie Letzteren eine natürliche Note geben. Wegen z.T. schwachem Kontrast, Störobjekten im Hintergrund, Verdeckungen und insbesondere der möglicherweise unterschiedlichen Ordnung der Äste in Bildern von verschiedenen Blickpunkten sind unbelaubte Bäume aber schwierig zu rekonstruieren. Der vorliegende Ansatz kombiniert generative Modellierung mittels L-Systemen und statistische Maximum A Posteriori (MAP) Schätzung für die Extraktion der 3D Verzweigungsstruktur von Bäumen. Hintergrund-Schätzung wird auf Grundlage von mathematischer (Grauwert) Morphologie als Basis für die generative Modellierung durchgeführt. Für die Bewertung der Hypothesen wird eine Gaußsche Likelihood-Funktion basierend auf Intensitätsunterschieden benutzt. Es wurde ein Mechanismus entworfen, der die Reihenfolge der Verwendung mehrerer Parameter für die Markoff-Kette basierend auf deren Charakteristik und Performance im letzten Schritt kontrolliert. Ein Baum wird nach der Extraktion der ersten Stufe von Ästen in drei typische Verzweigungstypen klassifiziert und es werden entsprechend Produktionsregeln von spezifischen L-Systemen verwendet. Basierend auf bereits extrahierten Ästen werden generische Prior-Verteilungen für die Parameter in einem Bayes’schen Rahmen verfeinert und in die MAP Schätzung integriert. Damit kann ein großer Teil der Verzweigungsstruktur außer kleinen Ästen extrahiert werden. Die Ergebnisse werden als VRML (Virtual Reality Modeling Language) Modelle dargestellt. Sie zeigen das Potenzial aber auch die noch vorhandenen Defizite des Ansatzes

Neuronal morphologies: the shapes of thoughts

The mammalian brain, one of the most fascinating systems in nature, is a complex biological structure that has kept scientists busy for over a century. Many of the brain's mysteries have been unraveled due to the enormous efforts of the scientific community, but yet many questions remain unsolved. The detailed drawings of Ramon y Cajal revealed the hidden structure of the brain, identifying the neurons as its fundamental structural and functional units. Although a significant amount of experimental reconstructions have been gathered over the past years, neuronal morphologies still remain one of the unsolved riddles of the brain. Why is neuronal diversity important for the functionality of the brain and how do neuronal morphologies ''shape'' our thoughts? To address these questions one needs to characterize the various shapes of neuronal morphologies. Traditionally, this task has been performed by using a set of morphological features, such as total length, branch orders and asymmetry. However, these features focus on a specific morphological aspect thereby causing a significant information loss from the original structure. Inspired by algebraic topology, I have conceived a topological descriptor of neuronal trees that couples the topology of a tree with the geometric features of its structure, retaining more details of the original morphology than traditional morphometrics. This descriptor has proved to be very powerful in discriminating several neuronal types into concrete groups based on morphological grounds, and has lead to the discovery of two distinct classes of pyramidal cells in the human cortex. In addition, the Topological Morphology Descriptor is important for the generation of artificial cells whose morphologies remain faithful to the biological ones. Neurons of the same morphological type have similar topological and geometric characteristics, therefore appearing to be highly structured. However, it is still unknown to what extent the complex neuronal morphology is shaped by the genetic information of an organism and to what extent it arises from stochastic processes. To study the impact of randomness and structure of neuronal morphologies on the connectivity of the network they form, I compared the properties of networks that arise from different artificially generated morphologies, ranging from random walks to constrained branching structures, against those of biological networks and computational reconstructions built from biological morphologies. Surprisingly, networks that are generated from almost random morphologies share a lot of common properties with biological networks, such as the spatial clustering of connections and the common neighbor effect, indicating that stochastic processes that take place during development, contribute significantly to the observed neuronal shapes. This thesis resolves a number of the mysteries of neuronal morphologies and questions our beliefs about the role of randomness in the formation of the brain. Thus, it brings us closer to understanding the fundamental differences among morphologies, and how randomness and structure are combined together to generate one of the most complex biological systems

Биологическая продуктивность лесообразующих видов в климатическом контексте Евразии

В монографии впервые разработаны всеобщие аллометрические модели фитомассы деревьев лесообразующих родов Евразии, применимые для наземной и лидарной таксации и дающие возможность оценки фитомассы и углеродных пулов на уровне древостоев по данным перечета деревьев на пробных площадях в лесах Евразии. Разработаны регрессионные модели для нескольких уровней продукционных показателей лесообразующих родов Евразии, чувствительные к изменению температур и осадков и доказывающие всеобщность характера действия закона лимитирующего фактора Либиха-Шелфорда на биологическую продуктивность лесообразующих видов (родов) на трансконтинентальном уровне. Показана возможность и перспективность использования закономерностей изменения показателей биологической продуктивности деревьев и древостоев, полученных в территориальных градиентах температур и осадков, для прогнозирования названных показателей при предполагаемых темпоральных (временны х) климатических изменениях. Для специалистов в области разработки и прогнозирования последствий изменения климата на лесные экосистемы, разработки систем лесного мониторинга и экологических программ разного уровня, а также для преподавателей, аспирантов и студентов по специальности «Лесное хозяйство