Synth Se Et Segmentation Markovienne D Images Sur La Base D Informations Propres La Texture

Synthèse et segmentation markovienne d'images sur la base d'informations propres à la texture

De nouveaux algorithmes de synthèse et de segmentation de textures et de segmentation d'images bruitées sont étudiés. L'approche bayésienne utilisant les champs aléatoires de Markov a été adaptée pour modéliser la texture. Pour la synthèse de texture un ensemble de paramètres composé de filtres adaptés et des variances des images filtrées correspondantes est extrait lors d'une phase d'apprentissage. Ces paramètres sont ensuite utilisés dans un modèle markovien pour la synthèse de texture. On a pu ainsi tester la pertinence du modèle et des paramètres choisis. Ces derniers ont donc ensuite été utilisés avec succès dans un algorithme de segmentation bayésienne d'images texturées. L'originalité de ces algorithmes est que l'étape d'estimation ne dépend pas du nombre de niveaux de gris de la texture. L'algorithme de synthèse ainsi présenté est limité à des textures homogènes ayant de 2 à 4 niveaux de gris, tandis que l'algorithme de segmentation a été appliqué à des textures homogènes à 256 niveaux de gris. Un autre algorithme de segmentation d'images bruitées est étudié. Le modèle utilisé est aussi markovien. De bons résultats ont été obtenus sur une image réelle.

Archives Internationales de Photogrammetrie Et de Teledetection

Pixel and Patch Based Texture Synthesis Using Image Segmentation

[Truncated abstract] Texture exists all around us and serves as an important visual cue for the human visual system. Captured within an image, we identify texture by its recognisable visual pattern. It carries extensive information and plays an important role in our interpretation of a visual scene. The subject of this thesis is texture synthesis, which is de ned as the creation of a new texture that shares the fundamental visual characteristics of an existing texture such that the new image and the original are perceptually similar. Textures are used in computer graphics, computer-aided design, image processing and visualisation to produce realistic recreations of what we see in the world. For example, the texture on an object communicates its shape and surface properties in a 3D scene. Humans can discriminate between two textures and decide on their similarity in an instant, yet, achieving this algorithmically is not a simple process. Textures range in complexity and developing an approach that consistently synthe- sises this immense range is a dfficult problem to solve and motivates this research. Typically, texture synthesis methods aim to replicate texture by transferring the recognisable repeated patterns from the sample texture to synthesised output. Feature transferal can be achieved by matching pixels or patches from the sample to the output. As a result, two main approaches, pixel-based and patch-based, have es- tablished themselves in the active eld of texture synthesis. This thesis contributes to the present knowledge by introducing two novel texture synthesis methods. Both methods use image segmentation to improve synthesis results. ... The sample is segmented and the boundaries of the middle patch are confined to follow segment boundaries. This prevents texture features from being cut o prematurely, a common artifact of patch-based results, and eliminates the need for patch boundary comparisons that most other patch- based synthesis methods employ. Since no user input is required, this method is simple and straight-forward to run. The tiling of pre-computed tile pairs allows outputs that are relatively large to the sample size to be generated quickly. Output results show great success for textures with stochastic and semi-stochastic clustered features but future work is needed to suit more highly structured textures. Lastly these two texture synthesis methods are applied to the areas of image restoration and image replacement. These two areas of image processing involve replacing parts of an image with synthesised texture and are often referred to as constrained texture synthesis. Images can contain a large amount of complex information, therefore replacing parts of an image while maintaining image fidelity is a difficult problem to solve. The texture synthesis approaches and constrained synthesis implementations proposed in this thesis achieve successful results comparable with present methods.