Color image segmentation by unsupervised 2D histogram clustering and Dempster-Shafer region merging

In this paper, a color image segmentation method based on a new approach called bimarginal is proposed.To overcome the drawbacks of the classical marginal approaches, color components are considered in pairs in order to have a partial view of their inner correlation. Working with color images, the three possible combinations are considered as three independant information sources. Each pairwise component combination is firstly analyzed according to an unsupervised morphologic clustering which looks for the dominant colors of a 2D histogram. This leads to obtain three segmentation maps combined by intersection after being simplified. The intersection process itself producing an over-segmentation of the image, a pairwise region merging is done according to a similarity criterion with the Dempster-Shafer theory up to a termination criterion. To fully automate the segmentation, an energy function is proposed to quantify the segmentation quality. The latter acts as a performance indicator and is used all over the segmentation to tune its parameters.Dans cet article nous proposons une méthode de segmentation d'images couleur selon une nouvelle approche que nous appelons bi-marginale. Afin de pallier les défauts des approches marginales classiques, nous considérons les composantes couleur deux à deux afin d'avoir une vue partielle de leur corrélation. Travaillant selon cette vision bi-composante, nous considérons les trois combinaisons possible comme trois sources d'informations indépendantes. Chaque information bi-composante est tout d'abord analysée selon un schéma de coalescence morphologique non supervisé qui recherche les couleurs dominantes d'un histogramme bidimensionnel. Cela permet de construire trois cartes de segmentation distinctes qui sont combinées par intersection après avoir été simplifiées. L'intersection produisant une sur-segmentation, une fusion des régions deux à deux est opérée selon un critère de similarité et selon la combinaison de Dempster-Shafer jusqu'à un critère de terminaison. Afin d'automatiser la méthode de segmentation, une mesure d'énergie est proposée afin de quantifier la qualité d'une segmentation, celle-ci sert tout au long de la méthode proposée comme indicateur de performance de la segmentation afin d'en régler les différents paramètres

Nonlocal Multiscale Hierarchical Decomposition on Graphs

International audienceThe decomposition of images into their meaningful components is one of the major tasks in computer vision. Tadmor, Nezzar and Vese [1] have proposed a general approach for multiscale hierarchical decomposition of images. On the basis of this work, we propose a multiscale hierarchical decomposition of functions on graphs. The decomposition is based on a discrete variational framework that makes it possible to process arbitrary discrete data sets with the natural introduction of nonlocal interactions. This leads to an approach that can be used for the decomposition of images, meshes, or arbitrary data sets by taking advantage of the graph structure. To have a fully automatic decomposition, the issue of parameter selection is fully addressed. We illustrate our approach with numerous decomposition results on images, meshes, and point clouds and show the benefits

Random subwindows and extremely randomized trees for image classification in cell biology

Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks. Results: We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose. Conclusion: Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems

Cell Microscopic Segmentation with Spiking Neuron Networks

International audienceSpiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy for cell microscopic image segmentation. Results obtained confirm the validity of the approach. The strategy is performed on cytological color images. Quantitative measures are used to evaluate the resulting segmentations

Image Processing with Spiking Neuron Networks

International audienceArtificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation.Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this chapter, we present how SNN can be applied with efficacy in image clustering, segmentation and edge detection. Results obtained confirm the validity of the approach