The bilateral filter and its variants, such as the joint/cross bilateral filter, are well-known edge-preserving image smoothing tools used in many applications. The reason of this success is its simple definition and the possibility of many adaptations. The bilateral filter is known to be related to robust estimation. This link is lost by the ad hoc introduction of the guide image in the joint/cross bilateral filter. We here propose a new way to derive the joint/cross bilateral filter as a particular case of a more generic filter, which we name the guided bilateral filter. This new filter is iterative, generic, inherits the robustness properties of the robust bilateral filter, and uses a guide image. The link with robust estimation allows us to relate the filter parameters with the statistics of input images. A scheme based on graduated nonconvexity is proposed, which allows converging to an interesting local minimum even when the cost function is nonconvex. With this scheme, the guided bilateral filter can handle non-Gaussian noise on the image to be filtered. A complementary scheme is also proposed to handle non-Gaussian noise on the guide image even if both are strongly correlated. This allows the guided bilateral filter to handle situations with more noise than the joint/cross bilateral filter can work with and leads to high peak signal-to-noise ratio values as shown experimentally.

Sight distance along the pathway plays a significant role in road safety and in particular, has a clear impact on the choice of speed limits. Mapping visibility distance is thus of importance for road engineers and authorities. While visibility distance criteria are routinely taken into account in road design, few systems exist for evaluating them on existing road networks. Most available systems comprise a target vehicle followed at a constant distance by an observer vehicle. This only allows to check if a given, fixed visibility distance is available: estimating the maximum visibility distance requires several passages, with increasing inter-vehicle intervals. We propose two alternative approaches for estimating the maximum available visibility distance, that exploit 3D models of the road and its close environment. These methods involve only one acquisition vehicle and use either active vision, more specifically 3D range sensing (LIDAR), or passive vision, namely, stereovision. The first approach is based on a Terrestrial LIDAR Mobile Mapping System. The triangulated 3D model of the road and its surroundings provided by the system is used to simulate targets at different distances, which allows for estimation of the maximum geometric visibility distance along the pathway in a quite flexible way. The second approach involves the processing of two views taken by digital cameras on-board an inspection vehicle. After road segmentation, the 3D road model is reconstructed which allows maximum roadway visibility distance estimation. Both approaches are described, evaluated and compared. Their pros and cons with respect to vehicle-following systems are also discussed.

Road sign identification in images is an important issue, in particular for vehicle safety applications. It is usually tackled in three stages: detection, recognition and tracking, and evaluated as a whole. To progress towards better algorithms, we focus in this paper on the first stage of the process, namely road sign detection. More specifically, we compare, on the same ground-truth image database, results obtained by three algorithms that sample different state-of-the-art approaches. The three tested algorithms: Contour Fitting, Radial Symmetry Transform, and pair-wise voting scheme, all use color and edge information and are based on geometrical models of road signs. The test dataset is made of 847 images 960×1080 of complex urban scenes (available at www.itowns.fr/benchmarking.html). They feature 251 road signs of different shapes (circular, rectangular, triangular), sizes and types. The pros and cons of the three algorithms are discussed, allowing to draw new research perspectives.

Most road marking detection systems use image processing to extract potential marking elements in their first stage. Hence, the performances of extraction algorithms clearly impact the result of the whole process. In this paper, we address the problem of extracting road markings in high resolution environment images taken by inspection vehicles in a urban context. This situation is challenging since large special markings, such as crosswalks, zebras or pictographs must be detected as well as lane markings. Moreover, urban images feature many white elements that might lure the extraction process. In prior work an efficient extraction process, called Median Local Threshold algorithm, was proposed that can handle all kinds of road markings. This extraction algorithm is here improved and compared to other extraction algorithms. An experimental study performed on a database of images with ground-truth shows that the stereovision strategy reduces the number of false alarms without significant loss of true detection.

Regularized pixel-based tomographic reconstruction techniques suffer from streaking artifacts when only few projection angles are available. Shape-based methods, that reconstruct objects by optimizing their boundaries typically enforce a length penalty on the evolving curve, which is not suited to all possible shapes or topologies. To overcome this limitation, we propose in this paper to introduce high-level shape priors in tomographic reconstruction using active contours. Our shape descriptor is moment-based-hence rather compact and hierarchical - and may be made invariant to geometric transformations up to affine ones. It can handle multiple references simultaneously to accommodate shape variations. Experimental results on synthetic data show the effectiveness of the prior, especially for small numbers of noisy projections.

In this paper we propose a novel approach of the two-image alignment problem based on a functional representation of images. This allows us to derive a one-to-several correspondence, multi-scale algorithm. At the same time, it also formalizes the problem as a robust estimation problem between possible matches. We then derive an accurate, robust and faster version for the alignment of edge images. The proposed algorithm is developed and tested in the context of off-line longitudinal road profile reconstruction from stereo images.

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm.

We present a new way of constraining the evolution of a region-based active contour with respect to a reference shape. Minimizing a shape prior, defined as a distance between shape descriptors based on the Legendre moments of the characteristic function, leads to a geometric flow that can be used with benefits in a two-class segmentation application. The shape model includes intrinsic invariance with regard to pose and affine deformations.

In this paper, we present a novel approach for incorporating geometric shape priors in region-based active contours in order to provide more robustness to clutter, noise and occlusions. We define shape descriptors based on Legendre moments and embed them in a recent Eulerian formulation of region-based active contours, which enables a rigorous mathematical treatment. An evolution equation that minimizes a function of the distance between the active contour and a reference shape is derived. Experimental results show the ability of the geometric shape prior to constrain an evolving curve to resemble a target shape. We finally introduce the new shape prior into a two-class segmentation functional and show its benefits on segmentation results, in presence of occlusions and clutter.

We present a new robust algorithm for reconstructing images into a linear subspace using MAP estimation. The algorithm takes into account the a priori distribution of the subspace variables and the noise is robustly modeled to allow for occlusions. The subspace distribution is estimated using nonparametric density estimation techniques. An efficient optimization scheme based on the mean shift procedure D Comaniciu et al. (2002) and on half-quadratic theory [ D Geman et al. (1992), P Charbonnier et al. (1997)] is developed, making optimization of the MAP function feasible for high-dimensional images. Preliminary results on real images demonstrate the contribution of a priori distribution modeling of sub-space variables, with respect to standard reconstruction methods over linear subspaces.

Change detection in image sequences has mainly focused on the recovery of moving objects when the viewing system is static, or on the detection of simple production effects such as video shot boundaries or scene transitions. Camera motion is usually handled by the compensation of dominant motion, using motion estimation and segmentation schemes. We propose a novel statistical change detection method able to handle more complex events such as entering or exiting objects, or changes in object appearance, when the camera is moving. Temporal changes of objects are captured by analyzing the statistics of successive images. Considering an appropriate choice of image features, we show how it is possible to extract the statistics of changing objects from a pair of successive image histograms. Changing objects are then located by statistical backprojection techniques. The method is completely unsupervised and does not require any motion estimation or motion compensation. It is illustrated here on real world road scenes exhibiting large camera motion.

In this paper a robust pattern recognition system, using an appearance-based representation of colour images is described. Standard appearance-based approaches are not robust to outliers, occlusions or segmentation errors. The approach proposed here relies on robust M-estimators, involving non-quadratic and possibly non-convex energy functions. To deal with the minimisation of non-convex functions in a deterministic framework, we introduce an estimation scheme relying on M-estimators used in continuation, from convex functions to hard redescending nonconvex estimators. At each step of the robust estimation scheme, the non-quadratic criterion is minimized using the half-quadratic theory. This leads to a weighted least squares algorithm, which is easy to implement. The proposed robust estimation scheme does not require any user interaction because all necessary parameters are previously estimated. The method is illustrated on a road sign recognition application. Experiments show significant improvements with respect to standard estimation schemes.

Many image processing problems are ill-posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. In this paper, we first give conditions for the design of such an edge-preserving regularization. Under these conditions, we show that it is possible to introduce an auxiliary variable whose role is twofold. First, it marks the discontinuities and ensures their preservation from smoothing. Second, it makes the criterion half-quadratic. The optimization is then easier. We propose a deterministic strategy, based on alternate minimizations on the image and the auxiliary variable. This leads to the definition of an original reconstruction algorithm, called ARTUR. Some theoretical properties of ARTUR are discussed. Experimental results illustrate the behavior of the algorithm. These results are shown in the field of 2D single photon emission tomography, but this method can be applied in a large number of applications in image processing.

In emission computerized tomography, the use of realistic constraints such as edge-preserving smoothing lead to nonlinear regularisation. Charbonnier et al. (see IEEE Trans. on Image Processing, 1994) used the half-quadratic regularization in order to solve this problem. Applied together with a Gaussian likelihood, it formed the ARTUR algorithm. We propose a new regularized algorithm called MOISE which takes into account the Poisson nature of the statistical noise and uses this half-quadratic regularization. For that reason, MOISE differ from the MAP EM (maximum a posteriori expectation maximization) algorithm developed by P.J. Green (1990) which uses the one step late technique. We tested MOISE and compared it with ARTUR, on numerical simulation and real data. The results show that, despite the slowness of convergence, the half-quadratic regularization can be applied in the case of a Poisson statistic.

This paper deals with edge-preserving regularization. The definition of the regularizing functions and properties such as edge modeling or stability are studied in the variational approach (by minimizing of a criterion) and in the anisotropic diffusion approach (by solving a PDE). We propose sufficient conditions to define an edge-preserving regularizing function, and analyze comparatively several usual functions. We use the algorithm ARTUR based on the half-quadratic transform to solve the nonlinear equation. The image and the edge map are simultaneously estimated.

A new method for tomographic reconstruction using 2-D wavelet transform (WT) is proposed. Computed tomography involves huge matrices which make the reconstruction process costly. The WT can focus the useful information on a small number of entries. Then, the computation time can be significantly reduced by using special algorithms to exploit the sparsity of the matrices. Moreover, computing the solution in the WT domain scatters errors on several resolutions and thus, control the quality of the reconstruction. We also show that the 2-D WT is more efficient than the 1-D WT.>

Many image processing problems are ill-posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. The authors first give sufficient conditions for the design of such an edge-preserving regularization. Under these conditions, it is possible to introduce an auxiliary variable whose role is twofold. Firstly, it marks the discontinuities and ensures their preservation from smoothing. Secondly, it makes the criterion half-quadratic. The optimization is then easier. The authors propose a deterministic strategy, based on alternate minimizations on the image and the auxiliary variable. This yields two algorithms, ARTUR and LEGEND. The authors apply these algorithms to the problem of SPECT reconstruction.>

Many image processing problems are ill-posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. We first propose sufficient conditions for the design of such an edge-preserving regularization. Using the Legendre transform, it is then possible to introduce an auxiliary variable which role is twofold. Firstly, it marks the discontinuities and ensures their preservation from smoothing. Secondly, it makes the criterion half-quadratic. The optimization is then easier. We propose a deterministic algorithm, based on alternate minimizations over the image and the auxiliary variable. We apply this algorithm to the problem of SPECT reconstruction.