Jean-Marc Boï

A discrete model for color naming

The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many different disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identified by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly defined by fitting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the different categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELAB color space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate the membership values of any other point in the color space. Model validation is performed both directly, through the comparison of the predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), and indirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in both cases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semantically meaningful color-based segmentation map.

Medical image segmentation using texture directional features

Medical image segmentation can often be performed through tissue texture analysis. One of the most recent and interesting ideas to do that is to take into account the distribution of local maximum orders. We have followed up this idea by using directional maximums and we have applied it to tissue differentiation. Two problems are emerging now: one is the identification of a given texture (labeling) and another one is the characterization of the different areas within images (segmentation). In this paper, we present our new approach for texture representation and analysis, and we point out the advances and problems involved in the image segmentation process.

Arc of ellipse detection for video image registration

In this paper, we give a detailed presentation of a robust algorithm for detecting arcs of ellipse in a binary image. The characterization of such arcs of ellipse enables the identification between some video image elements and the corresponding landmarks in a 3D model of the scene to be represented. This algorithm is based on a classical ellipse property that enables its parameters separation. It provides interesting results even in noisy images or when these arcs are small and partially hidden.

A Robust 3-D IVUS Transducer Tracking Using Single-Plane Cineangiography

During an intravascular ultrasound (IVUS) intervention, a catheter with an ultrasound transducer is introduced in the body through a blood vessel, and then, pulled back to image a sequence of vessel cross sections. Unfortunately, there is no 3-D information about the position and orientation of these cross-section planes, which makes them less informative. To position the IVUS images in space, some researchers have proposed complex stereoscopic procedures relying on biplane angiography to get two X-ray image sequences of the IVUS transducer trajectory along the catheter. To simplify this procedure, we and others have elaborated algorithms to recover the transducer 3-D trajectory with only a single view X-ray image sequence. In this paper, we present an improved method that provides both automated 2-D and 3-D transducer tracking based on pullback speed as a priori information. The proposed algorithm is robust to erratic pullback speed and is more accurate than the previous single-plane 3-D tracking methods.

Contour segmentation using Hough transform

In many 2D situations, contours can be efficiently represented as line segments or short circle arcs sequences. Therefore, we propose an original use of the Hough transform for detecting such primitives. Rather than searching directly for circle arcs, we have chosen to use the Hough transform for evaluating the relevance of each edge point and the curvature center associated with it. A specific parametrisation, which takes into account the discrete aspect of the problem, provides an homogeneous Hough space and bounds its spatial complexity. Then, circle arcs are obtained by gathering points which are close and have equivalent curvatures. An application to medical imaging is presented to highlight the significance of this method.

AUTOMATIC LANDMARK DETECTION AND VALIDATION IN SOCCER VIDEO SEQUENCES

Landmarks are specific points that can be identified to provide efficient matching processes. Many works have been developed for detecting automatically such landmarks in images: our purpose is not to propose a new approach for such a detection but to validate the detected landmarks in a given context that is the 2D to 3D registration of soccer video sequences. The originality of our approach is that it globally takes into consideration the color and the spatial coherence of the field to provide such a validation. This process is a part of the SIMULFOOT project whose objective is the 3D reconstruction of the scene (players, referees, ball) and its animation as a support for cognitive studies and strategy analysis.

Texture analysis and scientific visualization

In this paper, we propose a new formalism that enables to take into account image textural features in a very robust and selective way. This approach also permits to visualize these features so experts can efficiently supervise an image segmentation process based on texture analysis. The texture concept has been studied through different approaches. One of them is based on the notion of ordered local extrema and is very promising. Unfortunately, this approach does not take in charge texture directionality; and the mathematical morphology formalism, on which it is based, does not enable extensions to this feature. This led us to design a new formalism for texture representation which is able to include directionality features. It produces a representation of texture relevant features in the form of a surface z = f(x,y). The visualization of this surface gives experts sufficient information to discriminate different textures.