3D model based stereo reconstruction using coupled Markov random fields

Projet SYNTIMA lot of methods have been proposed in the field of stereo-reconstruction. We address here the problem of model-based tridimensional reconstruction from an alreadysegmented and matched stereo pair. This work is a continuation of the work presented, concerning the reconstruction problem. We study here a method based on Markov random fields, which allows the a priori segmentation and matching to be refined during the reconstruction of the 3D surfaces. A new segmentation and matching is then produced which respects the 3D coherence (or equivalently the disparity coherence) of each segmented region-pair. In this first approach, we use simple segmentation energies for each image (without line processes), plus a coupling term between the left and right images, associating planes (as surface primitives) with each region pair. This is the justification for using coupled Markov random fields. We present results on synthetic and real images. These preliminary results allow us to assess the feasability of a hierarchical stereo reconstruction method with no a priori segmentation

Applying Evolutionary Optimisation to Robot Obstacle Avoidance

This paper presents an artificial evolutionbased method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved following a biologically inspired scheme and give a fast, low-cost solution to the obstacle detection problem in mobile robotics

A Genetic algorithm for the detection of 2D geometric primitives in images

We investigate the use of genetic algorithms (GAs) in the framework of image primitives extraction (such as segments, circles, ellipses or quadrilaterals). This approach completes the well-known Hough transform, in the sense that GAs are efficient when the Hough approach becomes too expensive in memory, i.e. when we search for complex primitives having more than 3 or 4 parameters. Indeeda GA is a stochastic technique, relatively slow, but which provides with an efficient tool to search in a high dimensional space. The philosophy of the method is very similar to the Hough transform, which is to search an optimum in a parameter space. However, we will see that the implementation is different. The idea of using a GA for that purpose is not new, Roth and Levine have proposed a method for 2D and 3D primitives in 1992. For the detection of 2D primitives, we re-implement that method and improve it mainly in three ways : by using distance images instead of directly using contour images, which tends to smoothen the function to optimize, by using a GA-sharing technique, to detect several image primitives in the same step, by applying some recent theoretical results on GAs (about mutation probabilities) to reduce convergence time

Bitwise Regularity Coefficients as a Tool for Deception Analysis of a Genetic Algorithm

Projet FRACTALESWe present in this paper a theoretical analysis that relates an irregularity measure of a fitness function to the so-called GA-deception. This approach is a con­ti­nua­tion of a work that has presented a deception a­na­ly­sis of Hölder functions. The analysis developed here is a generalization of this work in two ways¸: we first use a «bitwise regularity» instead of a Hölder exponent as a basis for our deception analysis, second, we perform a similar deception analysis of a GA with uniform crossover. We finally propose to use the bitwise regularity coefficients in order to analyze the influence of a chromosome encoding on the GA efficiency, and present experiments with bits permutations and Gray encoding

Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3d tomography

International audienceWe present and analyse the behaviour of specialised operators designed for cooperative coevolution strategy in the framework of 3D tomographic PET reconstruction. The basis is a simple cooperative co-evolution scheme (the "fly algorithm"), which embeds the searched solution in the whole population, letting each individual be only a part of the solution. An individual, or fly, is a 3D point that emits positrons. Using a cooperative co-evolution scheme to optimize the position of positrons, the population of flies evolves so that the data estimated from flies matches measured data. The final population approximates the radioactivity concentration. In this paper, three operators are proposed, threshold selection, mitosis and dual mutation, and their impact on the algorithm efficiency is experimentally analysed on a controlled test-case. Their extension to other cooperative co-evolution schemes is discussed

MRI Gastric Images Processing using a Multiobjective Fly Algorithm

In this study, we combine computer vision and visualisation/data exploration to analyse magnetic resonance imaging (MRI) data and detect garden peas inside the stomach. It is a preliminary objective of a larger project that aims to understand the kinetics of gastric emptying. We propose to perform the image analysis task as a multi-objective optimisation. A set of 7 equally important objectives are proposed to characterise peas. We rely on a cooperation co-evolution algorithm called 'Fly Algorithm' implemented using NSGA-II. The Fly Algorithm is a specific case of the 'Parisian Approach' where the solution of an optimisation problem is represented as a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical evolutionary algorithms (EAs). NSGA-II is a popular EA used to solve multi-objective optimisation problems. The output of the optimisation is a succession of datasets that progressively approximate the Pareto front, which needs to be understood and explored by the end-user. Using interactive Information Visualisation (InfoVis) and clustering techniques, peas are then semi-automatically segmented

Evolutionary multifractal signal/image denoising

This chapter investigates the use of Evolutionary techniques for multifractal signal/image denoising. Two strategies are considered: using evolution as a pure stochastic optimiser, or using interactive evolution for a meta-optimisation task. Both strategies are complementary as they allow to address dierent aspects of signal/image denoising

Some Remarks on the Optimization of Hölder Functions with Genetic Algorithms

Projet FRACTALESWe investigate the problem of Hölder functions optimization using Genetic Algorithms (GA). We first derive a relation between the Hölder exponent of the function, the sampling rate, and the accuracy of the optimum localization, both in the domain and the range of the function. This relation holds for any optimization method which work on sampled search spaces. We then present a finer analysis in the case of the use of a GA, which is based on a deceptivity analysis. Our approach uses a decomposition on the Haar basis, which reflects in a natural way the Hölder structure of the function. It allows to relate the deceptivity, the exponent and some parameters of the GA (including the sampling precision). These results provide some indications which may help to make the convergence of a GA easier

Pointwise Regularity of Fitness Landscapes and the Performance of a Simple ES

International audienceWe present a theoretical and experimental analysis of the influence of the pointwise irregularity of the fitness function on the behavior of an (1+1)ES. Previous work on this subject suggests that the performance of an EA strongly depends on the irregularity of the fitness function. Several irregularity measures have been derived for discrete search spaces, in order to numerically characterize this type of difficulty for EA. These characterizations are mainly based on H¨older exponents. Previous studies used however a global characterization of fitness regularity (the global H¨older exponent), with experimental validations being conducted on test functions with uniform regularity. This is extended here in two ways: Results are now stated for continuous search spaces, and pointwise instead of global irregularity is considered. In addition, we present a way to modify the genetic topology to accommodate for variable regularity: The mutation radius, which controls the size of the neighbourhood of a point, is allowed to vary according to the pointwise irregularity of the fitness function. These results are explained through a simple theoretical analysis which gives a relation between the pointwise H¨older exponent and the optimal mutation radius. Several questions connected to on-line measurements and usage of regularity in EAs are raised

Optimization of fractal : function using genetic algorithms

In this work, we investigate the difficult problem of the optimization of fractal functions. We first derive some relations between the local scaling exponents of the functions, the sampling rate and the accuracy of the localization of the optimum, both in the domain and the range of the functions. We then apply these ideas to the resolution of the inverse problem for iterated function system (IFS) using a genetic algorithm. In the conditions of study (2D problem for sets), the optimization process yields the optimum with a good precision and within a tractable computing time