"Bacchus" Methodological approach for vineyard inventory and management. Chap.4: Textural and structural analysis

Ce chapitre présente les méthodes qui ont été développées dans le projet Bacchus pour la détection et la caractérisation des parcelles de vigne en imagerie aérienne en se basant sur leur structure. Une analyse texturale est d'abord mise en oeuvre, et complétée par l'introduction de contraintes de régularité des contours pour améliorer la segmentation. Finalement, les parcelles issues de ces premières étapes sont vérifiées et caractérisées au moyen d'une analyse de leur spectre de Fourier. Les résultats obtenus sur diverses zones d'étude du projet Bacchus sont présentés et discutés. / This chapter presents the methodologies that have been developed during the Bacchus project concerning the automatic detection and characterisation of vineyard plots in satellite and aerial images, based on their structural properties. First, a textural analysis has been used. Then shape regularity constraints have been introduced to improve the image segmentation. Finally, the vineyard plots issued from these previous steps are checked and characterised using a Fourier spectrum analysis. Results on various study areas of the Bacchus project are presented and discussed

Segmentation d'images ultrasonores

- Dans cette étude nous nous intéressons à la segmentation d'images ultrasonores. Nous développons une méthode de segmentation utilisant, d'une façon originale, le modèle de régions actives géodésiques. Cette approche tient compte à la fois des informations contours et régions. Nous exploitons les propriétés statistiques de ces informations. Pour cela l'information région est approchée par un modèle de distribution de niveaux de gris de la région. La recherche des contours est faite par la méthode des ensembles de niveaux à partir d'une courbe initiale. Nous avons testé notre algorithme sur des images ultrasonores réelles, images échotomographiques veineuses in vivo présentant un thrombus que nous cherchons à isoler. Les résultats expérimentaux obtenus illustrent bien les bonnes performances de notre algorithme pour détecter et localiser le thrombus, et montrent aussi que notre méthode est bien adaptée à la segmentation des images ultrasonores

A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TPAMI.2007.70774Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proved to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, using level-sets. Following the work of Leventon et al., we propose to revisit the use of PCA to introduce prior knowledge about shapes in a more robust manner. We utilize kernel PCA (KPCA) and show that this method outperforms linear PCA by allowing only those shapes that are close enough to the training data. In our segmentation framework, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description permits to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, occlusions, or smearing

Interactive image segmentation based on level sets of probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method. © 2012 IEEE.published_or_final_versio

Robust 3D pose estimation and efficient 2D region-based segmentation from a 3D shape prior

©2008 Springer-Verlag Berlin Heidelberg. The original publication is available at www.springerlink.com.Presented at the 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008.DOI: 10.1007/978-3-540-88688-4_13In this work, we present an approach to jointly segment a rigid object in a 2D image and estimate its 3D pose, using the knowledge of a 3D model. We naturally couple the two processes together into a unique energy functional that is minimized through a variational approach. Our methodology differs from the standard monocular 3D pose estimation algorithms since it does not rely on local image features. Instead, we use global image statistics to drive the pose estimation process. This confers a satisfying level of robustness to noise and initialization for our algorithm, and bypasses the need to establish correspondences between image and object features. Moreover, our methodology possesses the typical qualities of region-based active contour techniques with shape priors, such as robustness to occlusions or missing information, without the need to evolve an infinite dimensional curve. Another novelty of the proposed contribution is to use a unique 3D model surface of the object, instead of learning a large collection of 2D shapes to accommodate for the diverse aspects that a 3D object can take when imaged by a camera. Experimental results on both synthetic and real images are provided, which highlight the robust performance of the technique on challenging tracking and segmentation applications