Nicolas Passat

Université de Reims Champagne-Ardenne, Rheims, Champagne-Ardenne, France

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Publications (88)76.22 Total impact

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    ABSTRACT: Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relations. The induced image representation models are no longer partition hierarchies (i.e., trees), but directed acyclic graphs that generalize standard morphological tree structures such as component trees, binary partition trees or hierarchical watersheds. We describe how to efficiently build and handle these richer data structures, and we illustrate the versatility of the proposed framework in image filtering and image segmentation.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 06/2015; 37(6):1162-1176. DOI:10.1109/TPAMI.2014.2366145 · 5.69 Impact Factor
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    ABSTRACT: Object-based image analysis (OBIA) has been widely adopted as a common paradigm to deal with very high-resolution remote sensing images. Nevertheless, OBIA methods strongly depend on the results of image segmentation. Many segmentation quality metrics have been proposed. Supervised metrics give accurate quality estimation but require a ground-truth segmentation as reference. Unsupervised metrics only make use of intrinsic image and segment properties; yet most of them strongly depend on the application and do not deal well with the variability of objects in remote sensing images. Furthermore, the few metrics developed in a remote sensing context mainly focus on global evaluation. In this paper, we propose a novel unsupervised metric, which evaluates local quality (per segment) by analyzing segment neighborhood, thus quantifying under- and over-segmentation given a certain homogeneity criterion. Additionally, we propose two variants of this metric, for estimating global quality of remote sensing image segmentation by the aggregation of local quality scores. Finally, we analyze the behavior of the proposed metrics and validate their applicability for finding segmentation results having good tradeoff between both kinds of errors.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 05/2015; 8(5):1-10. DOI:10.1109/JSTARS.2015.2424457 · 3.03 Impact Factor
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    Camille Kurtz · Benoit Naegel · Nicolas Passat
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    ABSTRACT: In recent works, a new notion of component-graph was introduced. It extends the classical notion of componenttree initially proposed in mathematical morphology to model the structure of grey-level images. Component-graphs can indeed model the structure of any - grey-level or multivalued - images. We now extend the antiextensive filtering scheme based on component-trees, in order to make it tractable in the framework of component-graphs. More precisely, we provide solutions for building a component-graph; reducing it based on selection criteria; and reconstructing a filtered image from a reduced component-graph. In this article, we first consider the cases where component-graphs still have a tree structure; they are then called multivalued component-trees. The relevance and usefulness of such multivalued component-trees are illustrated by applicative examples on hierarchically classified remote sensing images.
    IEEE Transactions on Image Processing 10/2014; 23(12). DOI:10.1109/TIP.2014.2362053 · 3.11 Impact Factor
  • Yukiko Kenmochi · Phuc Ngo · Hugues Talbot · Nicolas Passat
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    ABSTRACT: Rigid transformations are involved in a wide variety of image processing applications, including image registration. In this context, we recently proposed to deal with the associated optimization problem from a purely discrete point of view, using the notion of discrete rigid transformation (DRT) graph. In particular, a local search scheme within the DRT graph to compute a locally optimal solution without any numerical approximation was formerly proposed. In this article, we extend this study, with the purpose to reduce the algorithmic complexity of the proposed optimization scheme. To this end, we propose a novel algorithmic framework for just-in-time computation of sub-graphs of interest within the DRT graph. Experimental results illustrate the potential usefulness of our approach for image registration.
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    Phuc Ngo · Yukiko Kenmochi · Nicolas Passat · Hugues Talbot
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    ABSTRACT: In the continuous domain, rigid transformations are topology-preserving operations. Due to digitization, this is not the case when considering digital images, i.e., images defined on Z^n. In this article, we begin to investigate this problem by studying conditions for digital images to preserve their topological properties under all rigid transformations on Z^2. Based on (i) the recently introduced notion of DRT graph, and (ii) the notion of simple point, we propose an algorithm for evaluating digital images topological invariance.
    Journal of Mathematical Imaging and Vision 06/2014; 49(2):418-433. DOI:10.1007/s10851-013-0474-z · 2.33 Impact Factor
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    Benoît Naegel · Nicolas Passat
    05/2014; 4:89-97. DOI:10.5201/ipol.2014.71
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    Nicolas Passat · Benoît Naegel
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    ABSTRACT: Component-trees model the structure of grey-level images by considering their binary level-sets obtained from successive thresholdings. They also enable to define anti-extensive filtering procedures for such images. In order to extend this image processing approach to any (grey-level or multivalued) images, both the notion of component-tree, and its associated filtering framework, have to be generalised. In this article we deal with the generalisation of the component-tree structure. We define a new data structure, the component-graph, which extends the notion of component-tree to images taking their values in any (partially or totally) ordered set. The component-graphs are declined in three variants, of increasing richness and size, whose structural properties are studied.
    Journal of Mathematical Imaging and Vision 05/2014; 49(1). DOI:10.1007/s10851-013-0438-3 · 2.33 Impact Factor
  • Phuc Ngo · Nicolas Passat · Yukiko Kenmochi · Hugues Talbot
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    ABSTRACT: We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping.
    IEEE Transactions on Image Processing 02/2014; 23(2). DOI:10.1109/TIP.2013.2295751 · 3.11 Impact Factor
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    ABSTRACT: The automated detection and mapping of landslides from Very High Resolution (VHR) images present several challenges related to the heterogeneity of landslide sizes, shapes and soil surface characteristics. However, a common geomorphological characteristic of landslides is to be organized with a series of embedded and scaled features. These properties motivated the use of a multiresolution image analysis approach for their detection. In this work, we propose a hybrid segmentation/classification region-based method, devoted to this specific issue. The method, which uses images of the same area at various spatial resolutions (Medium to Very High Resolution), relies on a recently introduced top-down hierarchical framework. In the specific context of landslide analysis, two main novelties are introduced to enrich this framework. The first novelty consists of using non-spectral information, obtained from Digital Terrain Model (DTM), as a priori knowledge for the guidance of the segmentation/classification process. The second novelty consists of using a new domain adaptation strategy, that allows to reduce the expert’s interaction when handling large image datasets. Experiments performed on satellite images acquired over terrains affected by landslides demonstrate the efficiency of the proposed method with different hierarchical levels of detail addressing various operational needs.
    ISPRS Journal of Photogrammetry and Remote Sensing 01/2014; 87:122-136. DOI:10.1016/j.isprsjprs.2013.11.003 · 3.13 Impact Factor
  • Yukiko Kenmochi · Phuc Ngo · Nicolas Passat · Hugues Talbot
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    ABSTRACT: Curvature is a continuous and infinitesimal notion. These properties induce geometrical difficulties in digital frameworks, and the following question is naturally asked: "How to define and compute curvatures of digital shapes?" In fact, not only geometrical but also topological difficulties are also induced in digital frameworks. The deeper question thus arises: "Can we still define and compute curvatures?" This latter question, that is relevant in the context of digitization, i.e., when passing from R^n to Z^n, can also be stated in Z^n itself, when applying geometric transformations on digital shapes. This paper proposes a preliminary discussion on this topic.
  • Phuc Ngo · Yukiko Kenmochi · Nicolas Passat · Hugues Talbot
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    ABSTRACT: Rigid transformations are involved in a wide range of digital image processing applications. In such a context, they are generally considered as continuous processes, followed by a digitization of the results. Recently, rigid transformations on ℤ 2 have been alternatively formulated as a fully discrete process. Following this paradigm, we investigate – from a combinatorial point of view – the effects of pixel-invariance constraints on such transformations. In particular we describe the impact of these constraints on both the combinatorial structure of the transformation space and the algorithm leading to its generation.
    Annals of Mathematics and Artificial Intelligence 01/2014; DOI:10.1007/s10472-014-9406-x · 0.49 Impact Factor
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    ABSTRACT: We propose a new distance called Hierarchical Semantic-Based Distance (HSBD), devoted to the comparison of nominal histograms equipped with a dissimilarity matrix providing the semantic correlations between the bins. The computation of this distance is based on a hierarchical strategy, progressively merging the considered instances (and their bins) according to their semantic proximity. For each level of this hierarchy, a standard bin-to-bin distance is computed between the corresponding pair of histograms. In order to obtain the proposed distance, these bin-to-bin distances are then fused by taking into account the semantic coherency of their associated level. From this modus operandi, the proposed distance can handle histograms which are generally compared thanks to cross-bin distances. It preserves the advantages of such cross-bin distances (namely robustness to histogram translation and histogram bin size issues), while inheriting the low computational cost of bin-to-bin distances. Validations in the context of geographical data classification emphasize the relevance and usefulness of the proposed distance.
    Data & Knowledge Engineering 09/2013; 87:206–225. DOI:10.1016/j.datak.2013.06.002 · 1.49 Impact Factor
  • Phuc Ngo · Nicolas Passat · Yukiko Kenmochi · Hugues Talbot
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    ABSTRACT: We study the conditions under which the topological properties of a 2D well-composed binary image are preserved under arbitrary rigid transformations. This work initiates a more global study of digital image topological properties under such transformations, which is a crucial but under-considered problem in the context of image processing, e.g., for image registration and warping.
    2013 20th IEEE International Conference on Image Processing (ICIP); 09/2013
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    ABSTRACT: Thin structure filtering is an important preprocessing task for the analysis of 2D and 3D bio-medical images in various contexts. We propose a filtering framework that relies on three approaches that are distinct and infrequently used together: linear, non-linear and non-local. This strategy, based on recent progress both in algorithmic/computational and methodological points of view, provides results that benefit from the advantages of each approach, while reducing their respective weaknesses. Its relevance is demonstrated by validations on 2D and 3D images.
    2013 20th IEEE International Conference on Image Processing (ICIP); 09/2013
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    ABSTRACT: Cerebrovascular atlases can be used to improve medical tasks requiring the analysis of 3D angiographic data. The generation of such atlases remains however a complex and infrequently considered issue. The existing approaches rely on information exclusively related to the vessels. We alternatively investigate a new way, consisting of using both vascular and morphological information (i.e., cerebral structures) to improve the accuracy and relevance of the obtained vascular atlases. Experiments emphasize improvements in the main steps of the atlas generation process impacted by the use of morphological information. An example of cerebrovascular atlas obtained from a dataset of 56 MRAs acquired from different acquisition devices is finally provided.
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on; 04/2013
  • Phuc Ngo · Yukiko Kenmochi · Nicolas Passat · Hugues Talbot
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    ABSTRACT: Rigid transformations are involved in a wide range of digital image processing applications. When applied on discrete images, rigid transformations are usually performed in their associated continuous space, requiring a subsequent digitization of the result. In this article, we propose to study rigid transformations of digital images as fully discrete processes. In particular, we investigate a combinatorial structure modelling the whole space of digital rigid transformations on arbitrary subset of Z2Z2 of size N × N. We describe this combinatorial structure, which presents a space complexity O(N9)O(N9) and we propose an algorithm enabling to construct it in linear time with respect to its space complexity. This algorithm, which handles real (i.e., non-rational) values related to the continuous transformations associated to the discrete ones, is however defined in a fully discrete form, leading to exact computation.
    Computer Vision and Image Understanding 04/2013; 117(4):393–408. DOI:10.1016/j.cviu.2012.08.014 · 1.36 Impact Factor

Publication Stats

499 Citations
76.22 Total Impact Points

Institutions

  • 2013–2014
    • Université de Reims Champagne-Ardenne
      Rheims, Champagne-Ardenne, France
  • 2004–2013
    • University of Strasbourg
      • Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection
      Strasburg, Alsace, France
  • 2008–2012
    • French National Centre for Scientific Research
      Lutetia Parisorum, Île-de-France, France