Eric Desjardin

Unsupervised Visual Data Mining Using Self-organizing Maps and a Data-driven Color Mapping

This paper presents a new approach for visually mining multivariate datasets and especially large ones. This unsupervised approach proposes to mix a SOM approach and a pixel-oriented visualization. The map is considered as a set of connected pixels, the space filling is driven by the SOM algorithm, and the color of each pixel is computed directly from data using an approach proposed by Blanchard et al. The method visually summarizes the data and helps in understanding its inner structure.

Anteriority index for managing fuzzy dates in archæological GIS

During the exploitation of an archæological geographical information system, experts need to evaluate the anteriority in pairs of dates which are uncertain and inaccurate, and consequently represented by fuzzy numbers. To build their hypotheses, they need to have an assessment, taking value in [0, 1], of the relation “lower than” between two FNs. We answer the experts’ need of evaluation by constructing an anteriority index based on the Kerre index. Two applications, which constitute a step in the evaluation of the evolution of Reims during the domination of the Roman Empire, illustrate the use of the anteriority index.

Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

Image segmentation is generally performed in a “one image, one algorithm” paradigm. However, it is sometimes required to consider several images of a same scene, or to carry out several (or several occurrences of a same) algorithm(s) to fully capture relevant information. To solve the induced segmentation fusion issues, various strategies have been already investigated for allowing a consensus between several segmentation outputs. This article proposes a contribution to segmentation fusion, with a specific focus on the “n images” part of the paradigm. Its main originality is to act on the segmentation research space, i.e., to work at an earlier stage than standard segmentation fusion approaches. To this end, an algorithmic framework is developed to build a binary partition tree in a collaborative fashion, from several images, thus allowing to obtain a unified hierarchical segmentation space. This framework is, in particular, designed to embed consensus policies inherited from the machine learning domain. Application examples proposed in remote sensing emphasise the potential usefulness of our approach for satellite image processing.

Imperfect Spatiotemporal Information Analysis in a GIS: Application to Archæological Information Completion Hypothesis

While Geographical Information System (GIS) is a classic in geography, we can denote a growing interest for its use in archaeology. This science, dealing with the past, partial discoveries and hypotheses, has to handle spatiotemporal information which is often incomplete and imprecise or uncertain. So, one needs to focus on the management of imperfection. The aim of this chapter is to expose a way to integrate the archaeological knowledge imperfection from the modeling of data to its graphical visualization in a spatiotemporal analysis process. The first goal of our approach is to propose valuated completion hypothesis along the time. In order to obtain it, we use a pattern recognition method derived from the Hough transform in accordance with the chosen data modeling.We apply our method in an archaeological GIS devoted to Roman street excavation in Reims.

Binary Partition Tree construction from multiple features for image segmentation

In the context of digital image processing and analysis, the Binary Partition Tree (BPT) is a classical data-structure for the hierarchical modelling of images at different scales. BPTs belong both to the families of graph-based models and morphological hierarchies. They constitute an efficient way to define sets of nested partitions of image support, that further provide knowledge-guided reduced research spaces for optimization-based segmentation procedures. Basically, a BPT is built in a mono-feature way, i.e., for one given image, and one given metric, by merging pairs of connected image regions that are similar in the induced feature space. We propose in this work a generalization of the BPT construction framework, allowing to embed multiple features. The cornerstone of our approach relies on a collaborative strategy enabling to establish a consensus between different metrics, thus allowing to obtain a unified hierarchical segmentation space. In particular, this provides alternatives to the complex issue of arbitrary metric construction from several – possibly non-comparable – features. To reach that goal, we first revisit the BPT construction algorithm to describe it in a fully graph-based formalism. Then, we present the structural and algorithmic evolutions and impacts when embedding multiple features in BPT construction. We also discuss different ways to tackle the induced memory and time complexity issues raised by this generalized framework. Final experiments illustrate how this multi-feature framework can be used to build BPTs from multiple metrics computed through the (potentially multiple) image content(s), in particular in the context of remote sensing.

Reconstruct street network from imprecise excavation data using fuzzy Hough transforms

This paper proposes an approach for handling multivariate data in an archaeological Geographical Information System (GIS), providing a new tool to archaeologists and historians. Our method extracts potential objects of known shapes in a geographical database (GDB) devoted to archaeological excavations. In this work, archaeological information is organized according to three components: location, date and a shape parameter, in a context where data are imprecise and lacunar. To manage these aspects, a three-step methodology was developed using fuzzy sets modeling and adapting the fuzzy Hough transform. This methodology is applied in order to define the appropriate tool for a GDB of Roman street remains in Reims, France. The defined queries return an estimation of the possible presence of streets during a fuzzy time interval given by experts on the Roman period in Reims.

Management of multiple and imperfect sources in the context of a territorial community environmental system

The work presented in this paper is a part of Observox, a community environmental information system for the monitoring of agricultural practices and their pressure on water resources in the Vesle basin, Champagne-Ardenne, France. The construction of Observox is the result of several research projects, and it is based on a methodology involving the actors concerned by the issue of water quality. Furthermore such a system requires the use of information provided by multiple sources which are usually imperfect. To provide the most honest indicators to the systems users, we integrate the notion of information quality by a degree of confidence. Thus we present the use of two main frameworks for imperfect knowledge management in the environmental information system, the fuzzy logic for propagating imprecision and belief functions for merging classifications.