Computer Vision Problems in 3D Plant Phenotyping

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis. First, we present a novel system for automated and non-invasive/non-contact plant growth measurement. We exploit the recent advancements of sophisticated robotic technologies and near infrared laser scanners to build a 3D imaging system and use state-of-the-art Computer Vision algorithms to fully automate growth measurement. We have set up a gantry robot system having 7 degrees of freedom hanging from the roof of a growth chamber. The payload is a range scanner, which can measure dense depth maps (raw 3D coordinate points in mm) on the surface of an object (the plant). The scanner can be moved around the plant to scan from different viewpoints by programming the robot with a specific trajectory. The sequence of overlapping images can be aligned to obtain a full 3D structure of the plant in raw point cloud format, which can be triangulated to obtain a smooth surface (triangular mesh), enclosing the original plant. We show the capability of the system to capture the well known diurnal pattern of plant growth computed from the surface area and volume of the plant meshes for a number of plant species. Second, we propose a technique to detect branch junctions in plant point cloud data. We demonstrate that using these junctions as feature points, the correspondence estimation can be formulated as a subgraph matching problem, and better matching results than state-of-the-art can be achieved. Also, this idea removes the requirement of a priori knowledge about rotational angles between adjacent scanning viewpoints imposed by the original registration algorithm for complex plant data. Before, this angle information had to be approximately known. Third, we present an algorithm to classify partially occluded leaves by their contours. In general, partial contour matching is a NP-hard problem. We propose a suboptimal matching solution and show that our method outperforms state-of-the-art on 3 public leaf datasets. We anticipate using this algorithm to track growing segmented leaves in our plant range data, even when a leaf becomes partially occluded by other plant matter over time. Finally, we perform some experiments to demonstrate the capability and limitations of the system and highlight the future research directions for Computer Vision based plant phenotyping

The Aggregated Country-Product Network.

<p><b>a)</b> Visualizes the country-product network for 2005. Services are given in blue and goods in red. Country nodes are colored according to the continents they belong, Europe is in green, Asia in purple, Africa in yellow, and nodes representing American countries are in cyan. <b>b)</b> Shows the <i>M</i> matrix. Here rows represent countries, ordered accordingly to their diversity, while columns represent products ordered according their ubiquity.</p

Evolution of services and goods complexity (1995–2010).

<p><b>a)</b> Product ranking estimated through MR. <b>b)</b> Same as a) only estimated through M-FCM. <b>a-b</b> Services: blue, goods: red.</p

Summary for the different clustering algorithms used in this paper showing their computational approach and complexity, where <i>v</i> is the number of nodes in the graph being clustered, and <i>e</i> is the corresponding number of edges.

<p>Summary for the different clustering algorithms used in this paper showing their computational approach and complexity, where <i>v</i> is the number of nodes in the graph being clustered, and <i>e</i> is the corresponding number of edges.</p

State diagram for the SIR model.

<p>The diagram shows the dynamics of a single node. A susceptible node can become infected by contacting its infected neighbors, with transmission probability . On the other hand, infected nodes spontaneously recover with probability and they remain permanently immune to the infection. The contact-induced transition mechanism is represented by a curvy arrow, whereas the spontaneous transition mechanism is represented by a less curvy arrow.</p

Passagierstroeme im Linien- und Gelegenheitsluftverkehr der deutschen Verkehrsflughaefen, 1968-1984 Ergaenzende Statistik zu den Veroeffentlichungen des Statistischen Bundesamtes

Bibliothek Weltwirtschaft Kiel C132,016 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Exploring Function Prediction in Protein Interaction Networks via Clustering Methods

<div><p>Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.</p></div

Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using timeBGLL, at different threshold values (<i>ω</i>).

<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotation for each graph representation using timeBGLL, at different threshold values (<i>ω</i>).</p

An illustration of the two mechanisms of state change of a node.

<p>The number of different states that exist in the network is . Solid colored arrows indicate successful state transmissions, i.e. infectious links, and dashed lines indicate an unsuccessful state transmission. The probabilities of the realized transmission events are depicted next to each line. Solid gray lines indicate that the nodes have not been in contact at the given time step; a spontaneous transition has taken place instead. From top to bottom panel, descriptions go as follows. Panel 1: node changes its state spontaneously to state 2 after previously having been in state 1. The probability of state change with this mechanism is . Panel 2: Node does not make a spontaneous transition, and changes its state as a result of getting infected with state 3 from its neighbors. Note that a neighbor in state 2 also makes successful transmission, however, node chooses state 3 transmitted from one of the other two successful transmissions. The probability of state change with this mechanism is , where . Panel 3: node changes its state as a result of getting infected with state 4 from its neighbors, a contact which stimulates it to adopt state 2. The probability of state change with this mechanism is , where and . Panel 4: node maintains its state since none of the two mechanisms of state change caused it to make a transition. The probability of this event is the product of the probability that no spontaneous transition occurs and no state is transmitted upon contact with the neighbors , where .</p

Snapshots of one sample execution of the model on a lattice network with periodic boundary conditions of 65536 () nodes.

<p>The snapshots were taken at different time steps, as indicated below each individual snapshot. Cyclic-like behavior is clearly seen.</p