Conditioning is an essential operation in knowledge representation and uncertainty modeling. It enables a priori beliefs to be adjusted in response to new information considered to be fully certain. This work focuses on the computation of Fagin and Halpern (FH-)conditioning in the context where uncertain information is represented by weighted or possibilistic logic belief bases. Weighted belief bases are extensions of classical logic belief bases where a weight or degree of belief is associated with each propositional logic formula. This paper proposes a characterization of the syntactic computation of the revision of weighted belief bases in the light of new information, which is in full agreement with the semantics of the FH-conditioning of possibility distributions. We show that the size of the revised belief base is linear with respect to the size of the initial base and that the computational complexity amounts to performing O ( log 2 ( n ) ) calls to the propositional logic satisfiability tests, where n is the number of different degrees of certainty used in the initial belief base. The last section of this paper examines both semantically and syntactically FH-conditioning under uncertain information, within the framework of possibility theory. • Reviews possibilistic logic and the use of weighted belief bases to represent uncertainty. • Introduces FH-conditioning within the framework of possibility theory. • Proposes a syntactic computation of FH-conditioning using three transformation steps. • Extends FH-conditioning to the case of uncertain observations. • Discusses complexity and interpretation of FH-conditioning as belief revision or update.

This paper presents a method for the automatic extraction of the structure of wastewater networks from geographical maps, as well as their representation in the form of graphs. The approach consists first in detecting the different important elementary elements composing a wastewater network, such as the manholes, their identifiers (using optical character recognition, OCR), and the wastewater pipes that connect them. Detecting these elementary elements is a first difficult problem, despite many existing tools, mainly due to the quality of the wastewater network maps used. However, the challenge addressed in this paper is how to select the relevant elementary elements detected, and bring them together to finally extract the wastewater network. One of the main contributions of this paper is to propose an efficient algorithm to solve these selection and assignment problems (e.g., manhole identifiers to manholes represented by circles). We also deal with the situation of isolated nodes to have the most connected clusters possible. The experimental results conducted on real map data show very promising results despite the low quality of the maps.

Conditioning plays an important role in revising uncertain information in light of new evidence. This work focuses on the study of Fagin and Halpern (FH-)conditioning in the context where uncertain information is represented by weighted or possibilistic belief bases. Weighted belief bases are extensions of classical logic belief bases where a weight or degree of belief is associated with each propositional logic formula. This paper proposes a characterization of a syntactic computation of the revision of weighted belief bases (in the light of new information) which is in full agreement with the semantics of the FH- conditioning of possibilistic distributions. We show that the size of the revised belief base is linear with respect to the size of the initial base and that the computational complexity amounts to performing O(log2(n)) calls to the propositional logic satisfiability tests, where n is the number of different degrees of certainty used in the initial belief base.

For several decades, it becomes possible to delineate waterbodies and their dynamics from optical or radar images, that are now available at high spatial and temporal resolutions. We present here an interpolation approach that takes benefits from this waterbodies delineation in endorheic areas. It consists in computing isovalue contour lines to improve topography estimates classically obtained from measurement points only. The approach, based on a minimization problem, uses thin plate spline (TPS) interpolation functions, the coefficients of which are determined along with the unknown water level of each curve. Results obtained on a generated topography show that this approach, applied with three contour-line curves, yields a lower root mean square error (RMSE) using only one measurement point compared to the one obtained with nine points and the classical approach.

The availability of an automatic tool for vine plot detection, delineation, and characterization would be very useful for management purposes. An automatic and recursive process using frequency analysis (with Fourier transform and Gabor filters) has been developed to meet this need. This results in the determination of vine plot boundary determination and accurate estimation of interrow width and row orientation. To foster large-scale applications, tests and validation have been carried out on standard very high spatial resolution remotely sensed data. About 89% of vine plots are detected corresponding to more than 84% of vineyard area, and 64% of them have correct boundaries. Compared with precise on-screen measurements, vine row orientation and interrow width are estimated with an accuracy of 1$^{\circ}$and 3.3 cm, respectively.