Sylvie Chambon

Automatic Road Pavement Assessment with Image Processing: Review and Comparison

In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

Dense matching using correlation: new measures that are robust near occlusions

In the context of computer vision, matching can be done using correlation measures. This paper presents the classification of fifty measures into five families. In addition, eighteen new measures based on robust statistics are presented to deal with the problem of occlusions. An evaluation protocol is proposed and the results show that robust measures (one of the five families), including the new measures, give the best results near occlusions.

Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection

This paper proposes a new algorithm for automatic crack detection from 2D pavement images. It strongly relies on the localization of minimal paths within each image, a path being a series of neighboring pixels and its score being the sum of their intensities. The originality of the approach stems from the proposed way to select a set of minimal paths and the two postprocessing steps introduced to improve the quality of the detection. Such an approach is a natural way to take account of both the photometric and geometric characteristics of pavement images. An intensive validation is performed on both synthetic and real images (from five different acquisition systems), with comparisons to five existing methods. The proposed algorithm provides very robust and precise results in a wide range of situations, in a fully unsupervised manner, which is beyond the current state of the art.

Towards correlation-based matching algorithms that are robust near occlusions

In the context of computer vision, matching can be done using correlation measures. This paper presents new algorithms that use two correlation measures: the zero mean normalised cross-correlation, ZNCC, and the smooth median absolute deviation, SMAD. While ZNCC is efficient in non-occluded areas and non-robust near occlusions, SMAD is non-efficient in non-occluded areas and robust near occlusions. The aim is to use the advantages of ZNCC and SMAD to deal with the problem of occlusions and to obtain dense disparity maps. The experimental results show that these algorithms are better than the ZNCC-based algorithm and SMAD-based algorithm.

Colour Correlation-based Matching

In the context of computer vision, stereo matching can be done using correlation measures. Few papers deal with color correlationbased matching so the underlying problem of this paper is about how it can be adapted to color images. The goals of this work are to help choosing a color space and to generalize the correlation measures to color. Nine color spaces and three different methods have been investigated to evaluate their suitability for stereo matching. The results show us to what extent stereo matching can be improved with color.

A region-based randomized voting scheme for stereo matching

This paper presents a region-based stereo matching algorithm which uses a new method to select the final disparity: a random process computes for each pixel different approximations of its disparity relying on a surface model with different image segmentations and each found disparity gets a vote. At last, the final disparity is selected by estimating the mode of a density function built from these votes. We also advise how to choose the different parameters. Finally, an evaluation shows that the proposed method is efficient even at sub-pixel accuracy and is competitive with the state of the art.

Reliability measure for propagation-based stereo matching

Seed propagation-based stereo matching can help to reduce ambiguity occuring when a pixel from one image has different putative correspondents in the other one due to difficult areas (repetitive patterns, homogeneous areas, occlusions and depth discontinuities). They rely on previously computed matches (seeds) to reduce the size of the search area, and thus the number of candidates. One approach of these iterative methods selects the “best” seed at each iteration to prevent the propagation of errors. However, little attention has been brought to this best-first selection criterion for which a correlation score is usually employed. This value itself does not consider any ambiguity and is not well-suited to select the most reliable seed. Therefore, in this paper we introduce a reliability measure. It has the advantage of taking into account information from the other candidates, and leads, according to the provided experimental evaluation, to better results than the correlation score alone.

Towards Skeleton Based Reconstruction: From Projective Skeletonization to Canal Surface Estimation

We present a novel approach to reconstruct a 3D object from images corresponding to two different viewpoints: we estimate the skeleton of the object instead of its surface. The originality of the method is to be able to reconstruct a complete tubular 3D object from only two input images. Unlike classical reconstruction methods like multiview stereo, this approach does not rely on interest points but estimates the topology of the object and derives its surface. Our contributions are twofold. First, given two perspective images of the 3D shape, the projection of the skeleton is computed in 2D. Second the 3D skeleton is reconstructed from the two projections using triangulation and matching. A mesh is finally derived for each skeleton branch.

Towards multi-scale feature detection repeatable over intensity and depth images

Object recognition based on local features computed at multiple locations is robust to occlusions, strong viewpoint changes and object deformations. These features should be repeatable, precise and distinctive. We present an operator for repeatable feature detection on depth images (relative to 3D models) as well as 2D intensity images. The proposed detector is based on estimating the curviness saliency at multiple scales in each kind of image. We also propose quality measures that evaluate the repeatability of the features between depth and intensity images. The experiments show that the proposed detector outperforms both the most powerful, classical point detectors (e.g., SIFT) and edge detection techniques.

Geometry-based Superpixel Segmentation - Introduction of Planar Hypothesis for Superpixel Construction

Superpixel segmentation is widely used in the preprocessing step of many applications. Most of existing methods are based on a photometric criterion combined to the position of the pixels. In the same way as the Simple Linear Iterative Clustering (SLIC) method, based on k-means segmentation, a new algorithm is introduced. The main contribution lies on the definition of a new distance for the construction of the superpixels. This distance takes into account both the surface normals and a similarity measure between pixels that are located on the same planar surface. We show that our approach improves over-segmentation, like SLIC, i.e. the proposed method is able to segment properly planar surfaces.