This paper presents an optimal parametric active contour method based on Fourier descriptors to detect and track a contour from a sequence of images in real-time. The advantage of this approach is that not only convex but also concave contours can be considered. We also propose a method to initialize the active contour using only three user's clicks in the first image of the sequence and to automatically re-initialize it when possible topological changes are detected during the tracking. Moreover, the algorithm is implemented on GPU to ensure the real-time tracking performance.

This paper proposes a new way to achieve robotic tasks by two-dimensional (2-D) visual servoing. Indeed, instead of using classical geometric features such as points, straight lines, pose, or a homography, as is usually done, the luminance of all pixels in the image is considered here. The main advantage of this new approach is that it requires no tracking or matching process. The key point of our approach relies on the analytic computation of the interaction matrix. This computation is based either on a temporal luminance-constancy hypothesis or on a reflection model so that complex illumination changes can be considered. Experimental results on positioning and tracking tasks validate the proposed approach and show its robustness to approximated depths, low-textured objects, partial occlusions, and specular scenes. They also showed that luminance leads to lower positioning errors than a classical visual servoing based on 2-D geometric visual features.

Direct photometric visual servoing has proved to be an efficient approach for robot positioning. Instead of using classical geometric features such as points, straight lines, pose or an homography, as it is usually done, information provided by all pixels in the image are considered. In the past mainly luminance information has been considered. In this paper, considering that most of the useful information in an image is located in its high frequency areas (that are contours), we have consider various possible combinations of global visual feature based on luminance and gradient. Experimental results are presented to show the behavior of such features.

We present in this paper a way to perform a fast and robust image segmentation and to track a contour along a sequence of images. Our approach is based on a dynamic deformable model. More precisely, we revisit the physics based-model to show the benefit of using a polar description to model the contour, in particular to cope with the well-known initialization problem. Indeed, we show that this way to proceed leads to diagonal and constant matrices in the equations of the snake evolution yielding therefore to a faster algorithm. Experimental results on image segmentation and contour tracking validate the efficiency of this new formulation.

We present in this paper a way to achieve positioning tasks by visual servoing under complex luminance variations. To do that, we use as visual features the luminance of all pixels in the image as we did in our previous work [4]. An important issue of this approach is that it does not rely at all on a any matching nor tracking process, contrary to all the approaches based on geometric visual features (points, straight lines, pose, homography, etc.). However, we consider in this paper a complete illumination model contrary to [4] where the temporal luminance constancy hypothesis was assumed. The main issue of this paper is thus the analytical computation of the interaction matrix related to the luminance from the Phong illumination model. Experimental results on specular objects validate our approach.

The goal of this paper is to present a way to perform visual servoing tasks from color attributes. This approach can be seen as an extension of our previous papers based on the luminance. Indeed, as we did for the luminance, color attributes are directly used in the control law avoiding therefore any complex images processing as features extraction or matching. We propose in this paper several potential color features and then a way to select a priori the best choice among them with respect to the scene being observed. Experimental results validate as well the interest of using color attributes as visual features as our selection process.

Lambertpsilas model is widely used in low level computer vision algorithms such as matching, tracking or optical flow computation for example. However, it is well known that these algorithms often fail when they face complex luminance variations. Therefore, we revise in this paper the underlying hypothesis of its temporal constancy and propose a new optical flow constraint. To do that, we use the Blinn-Phong reflection model to take into account that the scene may move with respect to the lighting and/or to the observer, and that specular highlights may occur. To validate in practice these analytical results, we consider the case where a camera is mounted on a robot end-effector with a lighting mounted on this camera and show experimental results of target tracking by visual servoing. Such an approach requires to analytically compute the luminance variations due to the observer motion which can be easily derived from our revised optical flow constraint. In addition, while the visual servoing classical approaches rely on geometric features, we present here a new method that directly relies on the luminance of all pixels in the image which does not require any tracking or matching process.

This paper proposes a new way to achieve robotic tasks by visual servoing. Instead of using geometric features (points, straight lines, pose, homography, etc.) as it is usually done, we use directly the luminance of all pixels in the image. Since most of the classical control laws fail in this case, we turn the visual servoing problem into an optimization problem leading to a new control law. Experimental results validate the proposed approach and show its robustness regarding to approximated depths, non Lambertian objects and partial occlusions.

This paper presents a method to determine the rough shape of an object. This is a step in the development of a ldquoone click grasping toolrdquo, a grasping tool of everyday-life objects for an assistant robot dedicated to elderly or disabled. The goal is to determine the quadric that approximates at best the shape of an unknown object using multi-view measurements. Non-linear optimization techniques are considered to achieve this goal. Since multiple views are necessary, an active vision process is considered in order to minimize the uncertainty on the estimated parameters and determine the next best view. Finally, results that show the validity of the approach are presented.

This paper focuses on the way to achieve accurate visual servoing tasks when the shape of the object being observed as well as the desired image are unknown. More precisely, we want to control the camera orientation with respect to the tangent plane at a certain object point corresponding to the center of a region of interest. We also want to observe this point at the principal point to fulfil a fixation task. A 3-D reconstruction phase must, therefore, be performed during the camera motion. Our approach is then close to the structure-from-motion problem. The reconstruction phase is based on the measurement of the 2-D motion in a region of interest and on the measurement of the camera velocity. Since the 2-D motion depends on the shape of the objects being observed, we introduce a unified motion model to cope both with planar and nonplanar objects. However, since this model is only an approximation, we propose two approaches to enlarge its domain of validity. The first is based on active vision, coupled with a 3-D reconstruction based on a continuous approach, and the second is based on statistical techniques of robust estimation, coupled with a 3-D reconstruction based on a discrete approach. Theoretical and experimental results compare both approaches.

This paper presents a method to achieve visual servoing tasks when the shape of the object being observed as well as the final image are unknown. Therefore, a reconstruction phase has to be performed during the camera motion. More precisely, the reconstruction phase is based on the measurement of the 2D motion in a region of interest and on the measurement of the camera velocity. Since the 2D motion depends on the shape of objects being observed, we introduce an unified motion model to cope as well with planar as with non-planar objects. This is an improvement of our previous works described by Lhaj et al. (2003), Collewet et al. (2004) where only planar objects had been considered. However, since this model is only an approximation, we propose to use active vision to enlarge its domain of validity. Experimental results validate the proposed approach.

The aim of this article is to achieve accurate visual servoing tasks when the shape of the object being observed as well as the final image are unknown. More precisely, we want to control the orientation of the tangent plane at a certain point on the object corresponding to the center of a region of interest and to move this point to the principal point to fulfill a fixation task. To do that, we perform a 3D reconstruction phase during the servoing. It is based on the measurement of the 2D displacement in the region of interest and on the measurement of the camera velocity. Since the 2D displacement depends on the scene, we introduce an unified motion model to deal with planar as well with non-planar objects. Unfortunately, this model is only an approximation. We proposed to use active vision to enlarge its domain of validity and a 3D reconstruction based on a continuous approach (2007). In this paper, we propose to use robust estimation techniques and a 3D reconstruction based on discrete approach. Experimental results compare both approaches.

This article proposes a local photometric model that compensates for specular highlights and lighting variations due to position and intensity changes. We define clearly on which assumptions it is based, according to widely used reflection models. Moreover, its theoretical validity is studied according to few configurations of the scene geometry (lighting, camera and object relative locations). Next, this model is used to improve the robustness of points tracking in luminance images with respect to specular highlights and lighting changes.

Positioning a robot with respect to objects by using data provided by a camera is a well known technique called visual servoing. In order to perform a task, the object must exhibit visual features which can be extracted from different points of view. Then, visual servoing is object-dependent as it depends on the object appearance. Therefore, performing the positioning task is not possible in presence of non-textured objects or objects for which extracting visual features is too complex or too costly. This paper proposes a solution to tackle this limitation inherent to the current visual servoing techniques. Our proposal is based on the coded structured light approach as a reliable and fast way to solve the correspondence problem. In this case, a coded light pattern is projected providing robust visual features independently of the object appearance

This paper considers the problem of positioning an eye-in-hand system so that it becomes parallel to a planar object. Our approach to this problem is based on linking to the camera a structured light emitter designed to produce a suitable set of visual features. The aim of using structured light is not only for simplifying the image processing and allowing low-textured objects to be considered, but also for producing a control scheme with nice properties like decoupling, convergence, and adequate camera trajectory. This paper focuses on an image-based approach that achieves decoupling in all the workspace, and for which the global convergence is ensured in perfect conditions. The behavior of the image-based approach is shown to be partially equivalent to a 3-D visual servoing scheme, but with a better robustness with respect to image noise. Concerning the robustness of the approach against calibration errors, it is demonstrated both analytically and experimentally

This paper focuses on the problem of realizing a plane-to-plane virtual link between a camera attached to the end-effector of a robot and a planar object. In order to do the system independent to the object surface appearance, a structured light emitter is linked to the camera so that 4 laser pointers are projected onto the object. In a previous paper we showed that such a system has good performance and nice characteristics like partial decoupling near the desired state and robustness against misalignment of the emitter and the camera (J. Pages et al., 2004). However, no analytical results concerning the global asymptotic stability of the system were obtained due to the high complexity of the visual features utilized. In this work we present a better set of visual features which improves the properties of the features in (J. Pages et al., 2004) and for which it is possible to prove the global asymptotic stability.

In this article, we study the color appearance of the ink printed on a background, according to both its concentration and the background color. We find some attributes, the concentration quotients ratios, that are more invariant to the ink concentration than simple color attributes. Our work deals with traceability of porcine products. We have to detect the animal identifier, printed with ink on the pork rind. Using the concentration quotients ratios, our segmentation technique succeeds for any quantity of ink and any hue of pork rind. This technique could be applied to segment any set of pixels, that are colorimetrically and spatially close, but not necessarily all connected.

We present a way to achieve positioning tasks by model-free visual servoing in the case of planar and motionless objects whose shape is unknown. Emphasize is made on the algorithm of 3D reconstruction which allows to synthesize easily the control law. More precisely, the reconstruction phase is based on the measurement of the 2D displacements in a region of interest and on the measurement of the camera velocity. However, we will show that the proposed algorithm is robust with respect to inaccurate values of this velocity. 2D displacements rather than 2D motions are used to remove the assumption that the acquisition rate has to be high. In addition, a particular attention is paid to the complex case of large displacements to access high camera velocities. Once the parameters of the plane are sufficiently stable, a visual servoing scheme is used to control the orientation of the camera with respect to the object and to ensure that it remains in the camera field of view for any desired orientation. The 3D reconstruction phase is maintained active during the servoing to improve the accuracy of the parameters and, consequently, to obtain a small positioning error. Experimental results validate the proposed approach.

In this paper we face the problem of positioning a camera attached to the end-effector of a robotic manipulator so that it gets parallel to a planar object. Such problem has been treated for a long time in visual servoing. Our approach is based on linking to the camera several laser pointers so that its configuration is aimed to produce a suitable set of visual features. The aim of using structured light is not only for easing the image processing and to allow low-textured objects to be treated, but also for producing a control scheme with nice properties like decoupling, stability, well conditioning and good camera trajectory.

This paper deals with the way to achieve positioning tasks by visual servoing in the case of planar and motionless objects whose shape is unknown. In fact, we consider here complex objects like those one can encounter in the agrifood industry. A 3D reconstruction phase is first considered yielding to the parameters of the plane. This computation is based on the measurement of the 2D velocity in a region of interest and on the measurement of the camera velocity. Once the parameters of the plane are sufficiently stable, a visual servoing scheme is used to control the orientation of the camera with respect to the object and to ensure that it remains in the camera field of view for any desired orientation. This 3D reconstruction phase is maintained active during the servoing to improve the accuracy of the parameters and, consequently, to obtain a small positioning error. Finally, experimental results validate the proposed approach as well as for its effectiveness than for the obtained accuracy.

This paper proposes a way to achieve positioning tasks by 2-D visual servoing when the desired image of the observed object cannot be precisely described. The object is assumed to be planar and motionless, but no knowledge about its shape or pose is required. First, we treat the case of a threadlike object, and then we show how our approach can be generalized to an object with three particular points. The control law is based on the use of 2-D visual servoing, and on an estimation of two 3-D parameters. We show that this control scheme is not sensitive to the calibration of the camera. We conclude this paper by experimental results relative to objects of unknown shape. In addition, an algorithm to estimate the depth between the object and the camera is provided, which finally leads to a 3-D estimation of the object shape.

This paper proposes a way to achieve positioning tasks by visual servoing, for any orientation of the camera, when the desired image of the observed object cannot be precisely described. The object is assumed to be planar and motionless but no knowledge about its shape or pose is required. To simplify the problem, first, we treat the case of a thread-like object and then we show how our approach can be generalized to an object with three particular points. The control law is based on the use of 2D visual servoing and on an estimation of a 3D parameter. Experimental results relative to objects of unknown shape are given to validate the approach. In addition, an algorithm to estimate the depth between the object and the camera is provided which leads to the dimensions of the object.

We describe a method to achieve robotic positioning tasks by image-based visual servoing when the object being observed has a complex and unknown shape. We first focus on the computation of an analytical expression of the interaction matrix according to a polar description of the image contour of the object. Experimental results are presented to validate the proposed algorithm. In particular, the robustness of the control law is tested with respect to a coarse calibrated system, to an approximation of the depth of the object, and to partial occlusion.