Yvon Voisin

A comparative survey on invisible structured light

This paper proposes a comparative survey on techniques of vision based on invisible structured lighting. We have classified them in three distinct families: InfraRed Structured Light (IRSL), Imperceptible Structured Light (ISL) and Filtered Structured Light (FSL). For each of them, definition, minimal configuration and main applications found in the literature are given. Then, we compare them regarding to several criteria: required equipment, light pattern coding, color analysis, texture analysis, motion analysis, security, use in non-controlled environment. The description is IRSL, ISL and FSL sensors will permit to sum up these techniques; the comparison will permit to evaluate performances and efficiency of each of them. We think that this study could be useful to researchers that are looking for a compromise between stereovision and structured light vision, combining the processing tools extent of the former with the point matching reliability and simplicity of processing of the latter.

Toward a 3D Multispectral Scanner: An Application to Multimedia

A stereoscopic system based on a multispectral camera and an LCD projector uses multispectral information for 3D object reconstruction. By linking 3D points to a curve representing the spectral reflectance, the system gives a physical representation of the matter thats independent from illuminant, observer, and acquisition devices

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.

Saliency for Spectral Image Analysis

We introduce a new feature extraction model for purposes of image comparison, visualization and interpretation. We define the notion of spectral saliency, as the extent to which a certain group of pixels stands out in an image and in terms of reflectance, rather than in terms of colorimetric attributes as it is the case in traditional saliency studies. The model takes as an input a multi- or hyper-spectral image with any dimensionality, any range of wavelengths, and it uses a series of dedicated feature extractions to output a single saliency map. We also present a local analysis of the image spectrum allowing to produce such maps in color, thus depicting not only which objects are salients, but also in which range of wavelengths. A variety of applications can be derived from the resulting maps, particularly under the scope of visualization, such as the saliency-driven evaluation of dimensionality reduction techniques. Results show that spectral saliency provides valuable information, which do not correlate neither with visual saliency, second-order statistics nor with naturalness, but serve however well for visualization-related applications.

A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a red-green-blue composite. Band selection is achieved by means of information measures at the first, second, and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informative content.

Improving color correction across camera and illumination changes by contextual sample selection

In many tasks of machine vision applications, it is important that recorded colors remain constant, in the real world scene, even under changes of the illuminants and the cameras. Contrary to the human vision system, a machine vision system exhibits inadequate adaptability to the variation of lighting conditions. Automatic white bal- ance control available in commercial cameras is not sufficient to pro- vide reproducible color classification. We address this problem of color constancy on a large image database acquired with varying digi- tal cameras and lighting conditions. A device-independent color repre- sentation may be obtained by applying a chromatic adaptation transform, from a calibrated color checker pattern included in the field of view. Instead of using the standard Macbeth color checker, we suggest selecting judicious colors to design a customized pattern from contextual information. A comparative study demonstrates that this approach ensures a stronger constancy of the colors-of- interest before vision control thus enabling a wide variety of applica- tions.

An adaptive-PCA algorithm for reflectance estimation from color images

This paper deals with the problem of spectral reflectance estimation from color camera outputs. Because the reconstruction of such functions is an inverse problem, stabilizing the reconstruction process is highly desirable. One way to do this is to decompose reflectance function on a basis functions like PCA. The present work proposes an algorithm making PCA adaptive in reflectance estimation from a color camera output. We propose to adapt the PCA basis derivation by selecting, for each sample, the more relevant elements from the training set elements. The adaptivity criterion is achieved by a likelihood measurement. Finally, the spectral reflectance estimation results are evaluated with the commonly used goodness-of-fit coefficient (GFC) and ¿E color difference, and prove the reliability of the proposed methods.

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.

Subpixel determination of imperfect circles characteristics

This article deals with the problem of the determination of characteristics of imperfect circular objects in discrete images, namely the radius and center coordinates. To limit distortion, a multi-level method based on active contours was developed. Its originality is to furnish a set of geometric envelopes in one pass, with a correspondence between grayscale and a regularity scale. The adequacy of this approach was tested with several methods, among them is the Radon-based method. More particularly, this study indicates the relevance of the use of active contours combined with a Radon transform-based method which was improved using a fitting considering the discrete implementation of the Radon transform.

Integration of knowledge to support automatic object reconstruction from images and 3D data

Object reconstruction is an important task in many fields of application as it allows to generate digital representations of our physical world used as base for analysis, planning, construction, visualization or other aims. A reconstruction itself normally is based on reliable data (images, 3D point clouds for example) expressing the object in his complete extent. This data then has to be compiled and analyzed in order to extract all necessary geometrical elements, which represent the object and form a digital copy of it. Traditional strategies are largely based on manual interaction and interpretation, because with increasing complexity of objects human understanding is inevitable to achieve acceptable and reliable results. But human interaction is time consuming and expensive, why many researches has already been invested to use algorithmic support, what allows to speed up the process and to reduce manual work load. Presently most of such supporting algorithms are data-driven and concentate on specific features of the objects, being accessible to numerical models. By means of these models, which normally will represent geometrical (flatness, roughness, for example) or physical features (color, texture), the data is classified and analyzed. This is successful for objects with low complexity, but gets to its limits with increasing complexness of objects. Then purely numerical strategies are not able to sufficiently model the reality. Therefore, the intention of our approach is to take human cognitive strategy as an example, and to simulate extraction processes based on available human defined knowledge for the objects of interest. Such processes will introduce a semantic structure for the objects and guide the algorithms used to detect and rexognize objects, which will yield a higher effectiveness. Hence, our research proposes an approach using knowledge to guide the algorithms in 3D point cloud and image processing.