Adjunctions on the lattice of hierarchies

24 pagesHierarchical segmentation produces not a fixed partition but a series of nested partitions, also called hierarchy. The structure of a hierarchy is univocally expressed by an ultrametric 1/2-distance. The lattice structure of hierarchies is equivalent with the lattice structure of their ultrametric 1/2-distances. The hierarchies form a complete sup- and inf- generated lattice on which an adjunction can be defined

Adjunctions on the lattice of dendrograms and hierarchies

56 pagesMorphological image processing uses two types of trees. The min-tree represents the relations between the regional minima and the various lakes during flooding. As the level of flooding increases in the various lakes, the flooded domain becomes larger. A second type of tree is used in segmentation and is mainly associated to the watershed transform. The watershed of a topographic surface constitutes a partition of its support. If the relief is flooded, then for increasing levels of floodings, catchment basins merge. The relation of the catchment basins during flooding also obeys a tree structure. We start by an axiomatic definition of each type of tree, min and max tree being governed by a single axiom ; for nested catchment basins, a second axiom is required. There is a one to one correspondance between the trees and an ultrametric half distance, as soon one introduces a total order compatible with the inclusion. Hierarchies obey a complete lattice structure, on which several adjunctions are defined, leading to the construction of morphological filters. Hierarchies are particular useful for interactive image segmentation, as they constitute a compact representation of all contours of the image, structured in a way that interesting contours are easily extracted. The last part extends the classical connections and partial connections to the multiscale case and introduces taxonomies

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

Climbing: A Unified Approach for Global Constraints on Hierarchical Segmentation

International audienceThe paper deals with global constraints for hierarchical segmentations. The proposed framework associates, with an input image, a hierarchy of segmentations and an energy, and the subsequent optimization problem. It is the first paper that compiles the different global constraints and unifies them as Climbing energies. The transition from global optimization to local optimization is attained by the h-increasingness property, which allows to compare parent and child partition energies in hierarchies. The laws of composition of such energies are established and examples are given over the Berkeley Dataset for colour and texture segmentation

Segmentation pyramidale localisée dans un ruban fermé

- Nous montrons comment un processus de segmentation peut être utilisé localement sur des régions d'intérêt d'une image, délimitées grossièrement par l'utilisateur. L'algorithme de la pyramide irrégulière convient particulièrement bien à cette approche : une propagation concurrente d'étiquettes de racines dans un graphe de régions permet de classifier les pixels à l'intérieur ou à l'extérieur de l'objet considéré

Improving Academic Natural Language Processing Infrastructures Utilizing Cluster Computation

In light of widespread digitization endeavors and ever-growing textual data generation, developing efficient academic Natural Language Processing (NLP) infrastructures, which can deal with large amounts of data, is of particular importance. Novel computation technologies allow tools that support big data and heavy computation while performing timely and cost-effective data processing. This development has led researchers to demand that knowledge be extracted from ever-increasing textual data before it is outdated. Cluster computation is a modern technology for handling big data efficiently. It provides distribution of computing and data over a number of machines in a cluster, as well as efficient use of resources, which are key requirements to process big data in a timely manner. It also assures applications’ high availability and fault tolerance, which are fundamental concerns when dealing with vast amounts of data. In addition, it provides load balancing of data during the execution of tasks, which results in optimal use of resources and enhances efficiency. Data-oriented parallelization is an effective solution to enable the currently available academic NLP infrastructures to process big data. This approach offers a solution to parallelize the NLP tools which comprise identical non-complicated tasks without the expense of changing NLP algorithms. This thesis presents the adaption of cluster computation technology to academic NLP infrastructures to address the notable features that are essential to process vast quantities of text materials efficiently, in terms of both resources and time. Apache Spark on top of Apache Hadoop and its ecosystem have been utilized to develop a set of NLP tools that provide a distributed environment to execute the NLP tasks. Many experiments were conducted to assess the functionality of the designated strategy. This thesis shows that using cluster computation technology and data-oriented parallelization enables academic NLP infrastructures to execute large amounts of textual data in a timely manner while improving the performance of the NLP tools. Moreover, these experiments provide information that brings a more realistic and transparent estimation of workflows’ costs (required hardware resources) and execution time, along with the fastest, optimum, or feasible resource configuration for each individual workflow. This knowledge can be employed by users to trade-off between run-time, size of data, and hardware, and it enables them to design a strategy for data storage, duration of data retention, and delivery time. This has the potential to enhance researchers’ satisfaction when using academic NLP infrastructures. The thesis also shows that a cluster computation approach provides the capacity to adapt NLP services with JIT delivery systems. The proposed strategy assures the reliability and predictability of the services, which are the main characteristics of the services in JIT delivery systems. Defining the relevant parameters, recording the behavior of the services, and analyzing the generated data resulted in the provision of knowledge that can be utilized to create a service catalog—a fundamental requirement for the services in JIT delivery systems—for each service offered. This knowledge also helps to generate the performance profiles for each item mentioned in the service catalog and to update them continuously to cover new experiments and improve service quality