A Cosmic Watershed: the WVF Void Detection Technique

On megaparsec scales the Universe is permeated by an intricate filigree of clusters, filaments, sheets and voids, the Cosmic Web. For the understanding of its dynamical and hierarchical history it is crucial to identify objectively its complex morphological components. One of the most characteristic aspects is that of the dominant underdense Voids, the product of a hierarchical process driven by the collapse of minor voids in addition to the merging of large ones. In this study we present an objective void finder technique which involves a minimum of assumptions about the scale, structure and shape of voids. Our void finding method, the Watershed Void Finder (WVF), is based upon the Watershed Transform, a well-known technique for the segmentation of images. Importantly, the technique has the potential to trace the existing manifestations of a void hierarchy. The basic watershed transform is augmented by a variety of correction procedures to remove spurious structure resulting from sampling noise. This study contains a detailed description of the WVF. We demonstrate how it is able to trace and identify, relatively parameter free, voids and their surrounding (filamentary and planar) boundaries. We test the technique on a set of Kinematic Voronoi models, heuristic spatial models for a cellular distribution of matter. Comparison of the WVF segmentations of low noise and high noise Voronoi models with the quantitatively known spatial characteristics of the intrinsic Voronoi tessellation shows that the size and shape of the voids are succesfully retrieved. WVF manages to even reproduce the full void size distribution function.Comment: 24 pages, 15 figures, MNRAS accepted, for full resolution, see http://www.astro.rug.nl/~weygaert/tim1publication/watershed.pd

An Improved Watershed Transform Algorithm For Two-Hand Tracking Under Partial Occlusion

Untuk mencapai interaksi semulajadi dalam persekitaran realiti penambahan, berinteraksi dengan isyarat dua-tangan selalunya dijadikan pilihan yang utama. Namun, interaksi dua-tangan tersebut akan mengakibatkan kedua-dua tangan saling beroklusi dan mengganggu penjejakan isyarat tangan. To achieve a natural interaction in augmented reality environment, two-handed gesture interactions are highly preferred. However, two-handed interactions always result in mutual occlusions which interfere with the hand gesture recognition

Colour morphological sieves for scale-space image processing

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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

P algorithm, a dramatic enhancement of the waterfall transformation

This document has been extended by "Towards a unification of waterfalls, standard and P algorithms", see http://hal-ensmp.archives-ouvertes.fr/hal-00835016.This document describes an efficient enhancement of the waterfall algorithm, a hierarchical segmentation algorithm defined from the watershed transformation. The first part of the document recalls the definition of the waterfall algorithm, its various avatars as well as its links with the geodesic reconstruction. The second part starts by analyzing the different shortcomings of the algorithm and introduces several strategies to palliate them. Two enhancements are presented, the first one named standard algorithm and the second one, P algorithm. The different properties of P algorithm are analyzed. This analysis is detailed in the last part of the document. The performances of the two algorithms, in particular, are addressed and their analogies with perception mechanisms linked to the brightness constancy phenomenon are discussed

Automated characterisation of Deep-sea imagery using Machine Learning: implications for future conservation and mineral extraction

This thesis aimed to develop a methodology using Machine Learning (ML) techniques for the interpretation of deep-sea resources. The deep-sea hosts diverse ecosystems and valuable resources, but potential environmental implications, particularly from mining activities, necessitate effective management strategies. Detailed maps of the sea floor are therefore a necessity, yet such maps have to date only been produced based on manual interpretation which is time consuming and subjective. The study focused on assessing the potential of ML methods to map deep-sea features based on photomosaic and bathymetry data in order to take the first steps in developing an automated, objective, and time-saving technique. This thesis’s method accurately identified and classified features like chimneys at the hydrothermal vent fields, providing insights for resource interpretation and conservation. Integrating ML methods into deep-sea resource management is crucial. The methodology enhances understanding of complex techniques, such as Convolutional Neural Networks (CNN) and Object-Based Image Analysis (OBIA) to overcome a seabed characterization. Simultaneously describing the parameters utilised to achieve a meaningful classification. ML algorithms analyze large data volumes, extract patterns, and predict feature distributions, aiding targeted conservation measures and sustainable resource exploitation. The methodology successfully mapped hydrothermal chimneys in two study areas yet producer accuracies (0,7%) were higher than user accuracies (0,64%), indicating that there were other landforms that shared similar features. The methodology also helps assess potential environmental implications of future mining, supporting informed decision-making and mitigation strategies. It serves also as a foundation for future research to aim at overcoming problems related to incomplete spatial coverage, attempt to better utilize shape and spatial parameters within the OBIA refinement, try to identify more background classes for excluding them from the model, etc.Master's Thesis in Earth ScienceGEOV399MAMN-GEO