Alain Diou

Calibration of a three-dimensional reconstruction system using a structured light source

We present a method for calibrating a range finder system composed of a camera and a structured light source. The system is used to reconstruct the three-dimensional (3-D) surface of an object. This is achieved by projecting a pattern, represented by a set of regularly spaced spots, on the surface of the object using the structured light source. An image of the illuminated object is next taken and by analyzing the distortion of the projected pattern, the 3-D surface of the object can be reconstructed. This reconstruction operation can be envisaged only if the system is calibrated. Instead of using a classical calibration method, which is based on the determination of the matrices that characterize the intrinsic and extrinsic parameters of the system, we propose a fast and easy to set up methodology, consisting of taking a sequence of images of a plane in translation on which a set of regularly spaced spots is projected using the structured light projection system. Next, a relation- ship between the position of the plane and the coordinates of the spots in the image is established. Using this relationship, we are able to deter- mine the 3-D coordinates of a set of points on the objects surface know- ing the 2-D coordinates of the spots in the image of the object taken by the range finder system. Finally, from the 3-D coordinates of the set of points, the 3-D surface of the object is reconstructed.

The Hough transform-a new approach

The Hough transform is presently witnessing an increasing Interest (over 200 references in 1995), due to its capacity of finding the parameters of miscellaneous shapes in an image (or signal), for example straight lines, circles, or mote generally conics. The authors propose here an analytical approach which permits the calculation of the theoretical Hough transform on standard images, in the case of the research of straight lines and show its practical application.

Multi-scale analysis of shell growth increments using wavelet transform

Abstract Shell increments contain information related to the evolution of the environment in which the organism grew during its biomineralization. To extract the information from variations in shell topography, a new and promising technique is presented, involving multi-scale analysis of the shell topography using a B-spline wavelet transform. An accurate non-contact optical system, based on laser triangulation, is used to map the shell surface. The resulting range image is treated as a grey-level image by using a multi-resolution approach based on the generalization of the cascade algorithm. This method allows reconstruction of non-subsampled images that correspond to the projection onto the space of the chosen scale of detail. This new approach provides an efficient tool for analyzing multi-scale information contained in growth increment rings and/or within quasi-periodic features. In conclusion, this approach can be applied to any 3D object, in order to extract features such as rhythmic information, color variations or object envelope.

Detecting parabolas in ultrasound B-scan images with Genetic-Based Inverse Voting Hough Transform

In this paper we propose a Genetic-Based Inverse Voting Hough Transform (GBIVHT) method for detecting parabolic shapes iii B-scan images obtained by the ultrasonic Time Of Flight Diffraction inspection technique. These parabolic shapes are characteristics of the presence of crack defects in the engineering structure under inspection. In our method, the local peak detection problem in the parameters space of conventional Hough Transform is converted into a parameter optimization problem that operates directly on the ultrasound B-scan image. The optimization is done using the well-known Genetic Algorithms. Our main goals are an accurate detection of the parabolas while circumventing the computational complexity and huge storage problem tied to conventional Hough Transform.

Multiscale analysis of range image: its use for growth increment characterization

A new image-processing approach for object analysis in life and earth sciences is presented. This approach is based on a multireso- lution algorithm in image processing. A clamshell surface has been digi- tized using a noncontact optical sensor based on laser triangulation. The 3-D surface obtained constitutes an image that can be characterized by multiresolution analysis. The application of this method to the study of a bivalve shell surface (Unio sp., Recent Atlantic, Holocene) allowed the various growth increments and their potential relationship with environ- mental constraints to be measured. The algorithm used in this paper is based on the wavelet transform theory.

Contribution to a marker-free system for human motion analysis

This paper presents a novel approach to human gait analysis using a marker-free system. The devised acquisition system is composed of three synchronized and calibrated charge coupled device cameras. The aim of this work is to recognize in gray level image sequences the leg of a walking human and to reconstruct it in the three-dimensional space. An articulated threedimensional (3D) model of the human body, based on the use of tapered superquadric curves, is first introduced. A motion-based segmentation, using morphological operators, is then applied to the image sequences in order to extract the boundaries of the leg in motion. A reconstruction process, based on the use of a least median of squares regression is next performed, to determine the location of the human body in the 3D space. Finally, a spatial coherence is imposed on the reconstructed curves in order to better fit the anatomy of the leg and to take into account the articulated model. Each stage of the proposed methodology has been tested both on synthetic images and on real world images of walking humans. The obtained results suggest that this approach is quite promising and should be useful in the study of the gait.

Crack defect detection and localization using genetic-based inverse voting Hough transform

We propose a genetic-based inverse voting Hough transform (GBIVHT) method to detect buried crack defects in engineering structures. The method is applied to B-scan images obtained according to the ultrasonic time of flight diffraction technique. In these image representations of the ultrasound data, crack defects are characterized by multiple arcs of diffraction that can be approximated by a parabolic model. Thus, the crack defect detection problem in non-destructive inspection of engineering structures is transformed into a parabola detection and localization on B-scan images. In the proposed GBIVHT method, the local peak detection problem of conventional HT is converted into a parameter optimization problem that operates directly on the B-scan images. The optimization task is done using the well-known genetic algorithms. Our main goals are an accurate detection of the parabolas while circumventing the computational complexity and huge storage problem tied to conventional HT.

A Model to Characterize the D-T Layer of ICF Targets by Backlit Optical Shadowgraphy

Abstract A numerical model is presented in order to modelize the bright ring that appears in backlit optical shadowgraphy on a transparent hollow sphere with a solid deuterium-tritium layer inside. This novel model is based on computational calculations applied to the problem of the targets used in inertial confinement fusion. The model takes into account the influences of the optical imaging system (numerical aperture, source divergence, camera resolution, etc.) and the effect of the capsule itself, diameter, thickness, and refractive index, and allows one to analyze the inner surface of a capsule in terms of thickness and roughness.

Adaptivity with near-orthogonality constraint for high compression rates in Lifting Scheme framework

Since few years, Lifting Scheme has proven its utility in compression field. It permits to easily create fast, reversible, separable or no, not necessarily linear, multiresolution analysis for sound, image, video or even 3D graphics. An interesting feature of lifting scheme is the ability to build adaptive transforms for compression, more easily than with other decompositions. Many works have already be done in this subject, especially in lossless or near-lossless compression framework : better compression than with usually used methods can be obtained. However, most of the techniques used in adaptive near-lossless compression can not be extended to higher lossy compression rates, even in the simplest cases. Indeed, this is due to the quantization error introduced before coding, which has not controlled propagation through inverse transform. Authors have put their interest to the classical Lifting Scheme, with linear convolution filters, but they studied criterions to maintain a high level of adaptivity and a good error propagation through inverse transform. This article aims to present relatively simple criterion to obtain filters able to build image and video compression with high compression rate, tested here with the Spiht coder. For this, upgrade and predict filters are simultaneously adapted thanks to a constrained least-square method. The constraint consists in a near-orthogonality inequality, letting sufficiently high level of adaptivity. Some compression results are given, illustrating relevance of this method, even with short filters.

Methods cooperation for multiresolution motion estimation

For a medical application, we are interested in an estimation of optical flow on a patients face, particularly around the eyes. Among the methods of optical flow estimation, gradient estimation and block matching are the main methods. However, the gradient-based approach can only be applied for small displacements (one or two pixels). Gener- ally, the process of block matching leads to good results only if the searching strategy is judiciously selected. Our approach is based on a Markov random field model, combined with an algorithm of block match- ing in a multiresolution scheme. The multiresolution approach allows de- tection of a large range of speeds. The large displacements are detected on coarse scales and small displacements are detected successively on finer scales in a coarse to fine strategy. The Markov random fields allow the initialization and control of motion estimation across all scales. The tracking of motion is achieved by a block matching algorithm. This method gives the optical flow, whatever the amplitude of motion is, if pertaining to the range defined by the multiresolution approach. The re- sults clearly show the complement of Markov random field estimation and block matching across the scales.