RESIDUAL APPROACH ON A HIERARCHICAL SEGMENTATION

International audienceResidual operators analyze the evolution of an image subject to the application of a series of transformations, for example a series of openings of increasing size. When a significant object is filtered out by a transformation corresponding to its size, an important residue is observed. Maximal residues are kept for each pixel, indicating the most significant objects present in the image. Different families of operators have been used in the literature: morphological openings or clos-ings, attribute openings or openings by reconstruction. In this paper we propose to compute residues on a hierarchy of parti-tions, computing the differences between regions at different hierarchical levels based on the classical earth mover's dis-tance. The advantage of our approach is that it is autodual and generic as it can be applied with any hierarchical approach

Morphological Segmentation on Learned Boundaries

International audienceColour information is usually not enough to segment natural complex scenes. Texture contains relevant information that segmentation approaches should consider. Martin et al. [Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5) (2004) 530-549] proposed a particularly interesting colour-texture gradient. This gradient is not suitable for Watershed-based approaches because it contains gaps. In this paper, we propose a method based on the distance function to fill these gaps. Then, two hierarchical Watershed-based approaches, the Watershed using volume extinction values and the Waterfall, are used to segment natural complex scenes. Resulting segmentations are thoroughly evaluated and compared to segmentations produced by the Normalised Cuts algorithm using the Berkeley segmentation dataset and benchmark. Evaluations based on both the area overlap and boundary agreement with manual segmentations are performed

Filtering of Artifacts and Pavement Segmentation from Mobile LiDAR Data

International audienceThis paper presents an automatic method for filtering and segmenting 3D point clouds acquired from mobile LIDAR systems. Our approach exploits 3D information by using range images and several morphological operators. Firstly, a detection of artifacts is carried out in order to filter point clouds. The artifact detection is based on a Top-Hat of hole filling algorithm. Secondly, ground segmentation extracts the contour between pavements and roads. The method uses a quasi-flat zone algorithm and a region adjacency graph representation. Edges are evaluated with the local height difference along the corresponding boundary. Finally, edges with a value compatible with the pavement/road difference (about 14[ cm ] ) are selected. Preliminary results demonstrate the ability of this approach to automatically filter artifacts and segment pavements from 3D data

Local blur estimation based on toggle mapping

International audienceA local blur estimation method is proposed, based on the difference between the gradient and the residue of the toggle mapping. This method is able to compare the quality of images with different content and does not require a contour detection step. Qualitative results are shown in the context of the LINX project. Then, quantitative results are given on DIQA database, outperforming the combination of classical blur detection methods reported in the literature

Segmentation of Facade Images using Ultimate Opening

International audienceIn recent years, automatic reconstruction, modeling and interpretation of urban environment and building structures is an area which gained interest. Urban environment's modeling allows developing different applications. This analysis extracts and reconstructs windows, doors and ornaments to provide rich information of the buildings adding real- ism for visualization. Our goal is the automation of the fac ̧ades interpretation from images; especially to detection/extraction structural objects mainly windows. We propose connected-component (CC) segmentation to detect of facade structures. The segmentation is based on a morpho- logical operator named ultimate opening

P algorithm, a dramatic enhancement of the waterfall transformation

This document has been extended by "Towards a unification of waterfalls, standard and P algorithms", see http://hal-ensmp.archives-ouvertes.fr/hal-00835016.This document describes an efficient enhancement of the waterfall algorithm, a hierarchical segmentation algorithm defined from the watershed transformation. The first part of the document recalls the definition of the waterfall algorithm, its various avatars as well as its links with the geodesic reconstruction. The second part starts by analyzing the different shortcomings of the algorithm and introduces several strategies to palliate them. Two enhancements are presented, the first one named standard algorithm and the second one, P algorithm. The different properties of P algorithm are analyzed. This analysis is detailed in the last part of the document. The performances of the two algorithms, in particular, are addressed and their analogies with perception mechanisms linked to the brightness constancy phenomenon are discussed

Morphological Segmentation of Building Façade Images

ISBN : 978-1-4244-5653-6International audienceIn this paper, we describe an automatic method for segmentation of building façade images. First, individual façades are isolated from general city block images. This step is based on accumulation of directional color gradients, assuming that façade structures are aligned. Then sky region is detected based on segmentation approach and color marker extraction. Finally, the images are split in floors using directional color gradient accumulation, as well. Our approach introduces several morphological filters to augment the robustness to problems such as: textured balconies, some specular reflections of the bright windows and small obstacles in images. The experimental results show the performance of our approach

Segmentation et Interprétation de Nuages de Points pour la Modélisation d'Environnements Urbains

National audienceIn this article, we present a method for detection and classification of artifacts at the street level, in order to filter cloud point, facilitating the urban modeling process. Our approach exploits 3D information by using range image, a projection of 3D points onto an image plane where the pixel intensity is a function of the measured distance between 3D points and the plane. By assuming that the artifacts are on the ground, they are detected using a Top-Hat of the hole filling algorithm of range images. Then, several features are extracted from the detected connected components and a stepwise forward variable/model selection by using the Wilk's Lambda criterion is performed. Afterward, CCs are classified in four categories (lampposts, pedestrians, cars and others) by using a supervised machine learning method. The proposed method was tested on cloud points of Paris, and have shown satisfactory results on the whole dataset.Dans cet article, nous présentons une méthode pour la détection et la classification d'artefacts au niveau du sol, comme phase de filtrage préalable à la modélisation d'environnements urbains. La méthode de détection est réalisée sur l'image profondeur, une projection de nuage de points sur un plan image où la valeur du pixel correspond à la distance du point au plan. En faisant l'hypothèse que les artefacts sont situés au sol, ils sont détectés par une transformation de chapeau haut de forme par remplissage de trous sur l'image de profondeur. Les composantes connexes ainsi obtenues, sont ensuite caractérisées et une analyse des variables est utilisée pour la sélection des caractéristiques les plus discriminantes. Les composantes connexes sont donc classifiées en quatre catégories (lampadaires, piétons, voitures et "Reste") à l'aide d'un algorithme d'apprentissage supervisé. La méthode a été testée sur des nuages de points de la ville de Paris, en montrant de bons résultats de détection et de classification dans l'ensemble de données

Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning

International audienceWe propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature

Urban accessibility diagnosis from mobile laser scanning data

International audienceIn this paper we present an approach for automatic analysis of urban acessibility using 3D point clouds. Our approach is based on range images and it consists in two main steps: urban objects segmentation and curbs detection. Both of them are required for accessibility diagnosis and itinerary planning. Our method automatically segments facades and urban objects using two hypotheses: facades are the highest vertical structures in the scene and objects are bumps on the ground on the range image. The segmentation result is used to build an urban obstacle map. After that, the gradient is computed on the ground range image. Curb candidates are selected using height and geodesic features. Then, nearby curbs are reconnected using Bézier curves. Finally, accessibility is defined based on geometrical features and accessibility standards. Our methodology is tested on two MLS databases from Paris (France) and Enschede (The Netherlands). Our experiments show that our method has good detection rates, is fast and presents few false alarms. Our method outperforms other works reported in the literature on the same databases