Segmentation non supervisée d'images de scènes routières. Une approche multi-critère
Unsupervised Segmentation of Road Images. A Multicriteria Approach
OPEN ACCESS
This paper presents a region-based segmentation algorithm which can be applied to various problems since it does not require a priori knowledge concerning the kind of processed images. This algorithm, based on a split and merge method, gives reliable results both on homogeneous grey level images and on textured images. First, images are divided into rectangular sectors . The splitting algorithm works independently on each sector, and uses a homogeneity criterion based only on grey levels . The merging is then achieved through assigning labels to each region obtained by the splitting step, using extracted feature measurements. We modeled exploited fields (data field and label field) by Markov Random Fields (MRF), the segmentation is then optimally determined using the Iterated Conditional Modes (ICM) . Input data of the merging step are regions obtained by the splitting step and their corresponding features vector. The originality of this algorithm is that texture coefficients are directly computed from these regions. These regions will be elementary sites for the Markov relaxation process . Thus, a region- based segmentation algorithm using texture and grey level is obtained . Results from various images types are presented.
Résumé
Nous présentons ici un algorithme de segmentation en régions pouvant s'appliquer à des problèmes très variés car il ne tient compte d'aucune information a priori sur le type d'images traitées . Il donne de bons résultats aussi bien sur des images possédant des objets homogènes au sens des niveaux de gris que sur des images possédant des régions texturées. C'est un algorithme de type division-fusion. Lors d'une première étape, l'image est découpée en fenêtres, selon une grille . L'algorithme de division travaille alors indépendamment sur chaque fenêtre, et utilise un critère d'homogénéité basé uniquement sur les niveaux de gris. La texture de chacune des régions ainsi obtenues est alors calculée . A chaque région sera associé un vecteur de caractéristiques comprenant des paramètres de luminance, et des paramètres de texture . Les régions ainsi définies jouent alors le rôle de sites élémentaires pour le processus de fusion. Celui-ci est fondé sur la modélisation des champs exploités (champ d'observations et champ d'étiquettes) par des champs de Markov. Nous montrerons les résultats de segmentation obtenus sur divers types d'images.
Segmentation, Region, Texture, Markov
Mots clés
Segmentation, Région, Texture, Markov
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