Human action recognition in videos is one of the key problems in computer vision. Inspired by image classification models, techniques based on bags of visual words have become one of the most effective approaches to solve this problem. The most usual way to engage an interest point in a bag of words is by means of the closest word found in a previously trained codebook. However, the quality of representation decreases when interest points have different visual words at similar distances or when we map noisy interest points. The aim of this paper is to present a new encoding procedure to engage the interest points in a bag of visual words improving the quality of the representation. The encoding that we propose tries to map only the relevant interest points detected in the scene. We experimentally show that using the new encoding method we can significantly improve the classification ratio.

In this paper we propose a new supervised active contour model evolving with Haralick texture features. This model is divided in two stages. First, we use a supervised step where the user defines an ideal segmentation on a learning image. A linear programming model, modeling the behavior of the active contour, is then used to determine the weights of the Haralick features leading to the optimal segmentation. In a second step, a texture-oriented active contour based on the Chan-Vese model is launched on several test images with the learned weights and the closest segmentations to the one defined on the learning image is determined. Results of our method are presented on medical echographic images.

A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.

We describe a 3D+T active surface model for segmentation and tracking of the left ventricular endocardium within 3D MRI sequences of the cardiac cycle. In order to perform segmentation and tracking simultaneously, the surface structure is divided through both time and space, and is therefore handled as an array of planar active contours, interconnected between adjacent slices and frames, providing spatial and temporal consistency. In a given frame, the stacking of slice contours constitute a 3D triangular mesh with a cylindrical topology. Extraction of ventricle border is performed by means of energy minimization, using a combination of boundary-based term and a new computationally efficient region-based term.

This paper presents a new approach to the mesh segmentation based on watershed transformation. We propose an original method to compute markers from the topological information of the mesh. The skeleton of the mesh allows the interpretation of the meaningful parts and the watershed transformation builds the boundaries of theses parts. Our method, which combines the patch-type and the part-type segmentation approaches, is particularly well adapted to the problematic of meaningful part segmentation.

We introduce a narrow band region approach in explicit de-formable models for 2D and 3D image segmentation. Embedding a region term into the evolution process, we derive a general formulation which is applied both on a 2D parametric contour and a 3D triangular mesh. Evolution of deformable models is performed by means of energy minimization using the computationally efficient greedy algorithm. The use of a region energy related to the vicinity of the evolving surface overcomes limitations of edge-based active models while remaining time effective. Experiments with segmentation quality assessment are carried out on medical images.

Deformable models, such as the discrete active contour and surface, imply the use of iterative evolution methods to perform 2D and 3D image segmentation. Among the several existing evolution methods, we focus on the greedy algorithm, which minimizes an energy functional, and the physics-based method, which applies forces in order to solve a dynamic differential equation. In this paper, we compare the greedy and physics-based approaches applied on 2D and 3D models, as regards overall speed and segmentation quality, quantified with an evaluating function mainly based on the mean distance between the model and the desired shape.

In this paper, we study the ability of the cooperation of two-color pixel classification schemes (Bayesian and K-means classification) with color watershed. Using color pixel classification alone does not sufficiently accurately extract color regions so we suggest to use a strategy based on three steps: simplification, classification, and color watershed. Color watershed is based on a new aggregation function using local and global criteria. The strategy is performed on microscopic images. Quantitative measures are used to evaluate the resulting segmentations according to a learning set of reference images.

This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simplifies their structure and improves the classification. Moreover, the learning step automatically determines the number of hidden neurons. The discussion is illustrated by tests on databases from the UCI machine learning database repository. The experimental results show that this architecture can achieve a faster learning, simpler neural networks and an improved performance in classification.

We are applying neural networks to the problem of handwritten signature verification. Our system is working on checks, so we can only use the static information (the image). This static information is used in three representations: geometrical parameters, outline and image. Our system is composed of several neural networks which cooperate together during the learning and decision phases. The performances in generalization, obtained with a large-scale database of 6000 signatures from real checks on random forgeries, are False Acceptance Rate (FAR)=2% and False Rejection Rate (FRR)=4%.