Couplage d'un problème de classification et d'estimation de densité par des noyaux gaussiens

La classification et l'estimation de densité d'un nuage de points issu du tirage d'un mélange de lois sont deux problèmes intimement liés. En effet la connaissance de la densité induit une classification naturelle dans laquelle le nombre de classe est connu (et correspond au nombre de modes), d'autre part la connaissance de la classification permet de localiser dans l'espace les points correspondant a chacune des composantes du mélange et simplifie le problème de l'estimation de densité. Dans la pratique aucune de ses deux données n'est disponible. Dans ce papier on propose une méthode permettant de résoudre conjointement ces deux problèmes

Clustering flyash particles using image processing techniques

Fly ash is one of the residues generated in combustion, and comprises the fine particles that rise with the flue gases. In the US about 43% is recycled and is often used to supplement Portland cement in concrete production. Fly ash can improve the concrete's mechanical properties and decrease cost. Depending upon the source and makeup of the coal being burned, the components of fly ash vary considerably. These variations affect the quality of the final product. Accordingly it is important for cement manufacturers to know the amount and type of the components in these particles.The objective of this project is segmentation of images of fly-ash particles acquired using a Micro computed Tomography (uCT) imaging device. A set of grayscale images is produced, with each image representing a particular slice of the particle. The desired segmentation operation should identify particles and label regions of a given image based on "similarity", as perceived by human observers. Two techniques are proposed for segmenting different phases of material in these images.The first technique uses Contrast Stretching and Histogram Matching and is based solely on the gray scale value of the pixels in the image slices. But some of the segmented regions, although having the same gray value, contain a different composition of material and show up with a porous texture in uCT images. Distinguishing these regions by using only the Gray value produces inaccurate results.In the second proposed technique, Circular Gabor Filters (CGF) are used to segment the regions of impurity with porous textures in the cross section of the particle. We have also proposed a technique for designing the CGF such that when applied to the gray scale images, the filter passes the porous regions of components accurately, while blocking non-porous regions.By combining these techniques, we have developed a program that is able to segment different types and regions of impurities in the cross sections of a flyash particle based on their gray values and textures. Using AMIRA software we then create 3D models of these particles, presenting the locations and sizes of different phases of material

Watershed-based unsupervised clustering

In this paper, a novel general purpose clustering algorithm is presented, based on the watershed algorithm. The proposed approach defines a density function on a suitable lattice, whose cell dimension is carefully estimated from the data. The clustering is then performed using the well-known watershed algorithm, paying particular attention to the boundary situations. The main characteristic of this method is the capability to determine automatically the number of clusters from the data, resulting in a completely unsupervised approach. Experimental evaluation on synthetic data shows that the proposed approach is able to accurately estimate the number of the classes and to cluster data effectively