A model-based approach to recovering the structure of a plant from images

We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, with no manual intervention

Pemodelan pertumbuhan Zea Mays L. menggunakan Sthochastic L-System

L-Systems memiliki fleksibilitas dalam mensimulasikan struktur dan proses pengembangan pertumbuhan tanaman secara visual dan realistik. Penelitian ini bertujuan untuk memodelkan pertumbuhan tanaman jagung menggunakan L-Systems dan memvisualisasikan model pertumbuhan tanaman jagung tersebut dari kecil hingga dewasa dalam ruang dimensi tiga. Penelitian dilakukan dalam tiga tahap yang diawali dari identifikasi kebutuhan data tehadap pertumbuhan tanaman jagung (Zea Mays L.). Tahap kedua, membangun model secara manual yang meliputi identifikasi dan penentuan komponen L-Systems (huruf, aksioma, dan aturan produksi). Tahap ketiga, melakukan simulasi dan visualisasi model pertumbuhan tanaman jagung yang telah didapat menggunakan processing dengan bahasa java dalam ruang dimensi tiga. Ketiga tahapan tersebut menghasilkan model Stochastic L-Systems dari pertumbuhan tanaman jagung dalam ruang dimensi tiga. Visualisasi model tanaman jagung yang telah dihasilkan pada penelitian ini lebih menekankan pada penyempurnaan model yang dilakukan pada penelitian sebelumnya terutama pada pewarnaan, pembentukan batang, dan adanya tulang daun pada tanaman jagung setiap iterasinya. Model tanaman jagung divisualisasikan mulai dari kecil hingga dewasa (fase vegetatif) yang memiliki tulang daun dan kelengkungan daun berbeda dari daun bawah sampai pada daun atas. Tanaman jagung yang divisualisasikan hanya terbatas sampai 8 iterasi saja yang sudah mampu mewakili pertumbuhan tanaman jagung pada fase vegetati

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

Tree Topology Estimation

<p>Tree-like structures are fundamental in nature. A wide variety of two-dimensional imaging techniques allow us to image trees. However, an image of a tree typically includes spurious branch crossings and the original relationships of ancestry among edges may be lost. We present a methodology for estimating the most likely topology of a rooted, directed, three-dimensional tree given a single two-dimensional image of it. We regularize this inverse problem via a prior parametric tree-growth model that realistically captures the morphology of a wide variety of trees. We show that the problem of estimating the optimal tree has linear complexity if ancestry is known, but is NP-hard if it is lost. For the latter case, we present both a greedy approximation algorithm and a heuristic search algorithm that effectively explore the space of possible trees. Experimental results on retinal vessel, plant root, and synthetic tree datasets show that our methodology is both accurate and efficient.</p>Dissertatio