A Nash-game approach to joint image restoration and segmentation

International audienceWe propose a game theory approach to simultaneously restore and segment noisy images. We define two players: one is restoration, with the image intensity as strategy, and the other is segmentation with contours as strategy. Cost functions are the classical relevant ones for restoration and segmentation, respectively. The two players play a static game with complete information, and we consider as solution to the game the so-called Nash Equilibrium. For the computation of this equilibrium we present an iterative method with relaxation. The results of numerical experiments performed on some real images show the relevance and efficiency of the proposed algorithm

Improved Mumford-Shah Functional for Coupled Edge-Preserving Regularization and Image Segmentation

An improved Mumford-Shah functional for coupled edge-preserving regularization and image segmentation is presented. A nonlinear smooth constraint function is introduced that can induce edge-preserving regularization thus also facilitate the coupled image segmentation. The formulation of the functional is considered from the level set perspective, so that explicit boundary contours and edge-preserving regularization are both addressed naturally. To reduce computational cost, a modified additive operator splitting (AOS) algorithm is developed to address diffusion equations defined on irregular domains and multi-initial scheme is used to speed up the convergence rate. Experimental results by our approach are provided and compared with that of Mumford-Shah functional and other edge-preserving approach, and the results show the effectiveness of the proposed method.</p

Diffusion-Based Image Segmentation Methods

Import 23/07/2015Image segmentation is an important task in image analysis and computer vision. A difficult problem in image segmentation is to decide whether two image points belong to one or to two image segments. The decision to this question can be based on measuring the distance between the points. Measuring this distance is the main topic of the thesis. We especially focus on the techniques that are based on the spectral decomposition and on the diffusion processes since they may be regarded as sophisticated and promising. In this work, however, we firstly show that they are not always good in the given context. This claim is supported by the theoretical considerations as well as by the extensive computational simulations. On the basis of these observations, we continue with the proposition of several new distance measures that try to remedy the problems that have been discovered. The new methods can be divided into two groups. The first group contains three methods that are based on the diffusion processes and are inspired by the diffusion distance. The second group consists of only one method that combines the resistance and the geodesic distance. We describe the new methods from the theoretical point of view; the results of testing are presented as well. The results show that the methods have certain good properties and may be useful in image segmentation.Segmentace obrazu je důležitým úkolem v analýze obrazu a počítačovém vidění. Težkým úkolem v segmentaci obrazu je rozhodnutí, zdali dva body náleží do jednoho nebo dvou segmentů. Odpověď na tuto otázku může být založena na měření vzdálenosti mezi body. Měření vzdálenosti je hlavním tématem této práce. Speciálně se zaměřujeme na techniky, které jsou založeny na spektrálním rozkladu a na difuzních procesech, které se považují za sofistikované a slibné. V této práci však nejprve ukazujeme, že nejsou vždy dobré v daném kontextu. Toto tvrzení je podpořeno z teoretického pohledu a také širokými výpočetními simulacemi. Na základě těchto pozorování pokračujeme v navržení několika nových měření vzdálenosti, které se snaží napravit objevené problémy. Nové metody mohou být rozděleny do dvou skupin. První skupina obsahuje tři metody, které jsou založeny na difuzních procesech a jsou inspirovány difuzní vzdáleností. Druhá skupina obsahuje pouze jednu metodu, která kombinuje resistivní a geodetickou vzdálenost. Metody popisujeme z teoretického pohledu. Prezentujeme též výsledky testování. Výsledky ukazují, že metody mají určité dobré vlastnosti a mohou být užitečné v segmentaci obrazu.Prezenční460 - Katedra informatikyvyhově