Comparison of optical sensors discrimination ability using spectral libraries

In remote sensing, the ability to discriminate different land covers or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Previous studies showed that the spectral resolution is a critical issue, especially in complex environment. In spite of the increasing availability of hyperspectral data, multispectral optical sensors onboard various satellites are acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this paper, we studied seven of these optical sensors: Pleiades, QuickBird, SPOT5, Ikonos, Landsat TM, Formosat and Meris. This study focuses on the ability of each sensor to discriminate different materials according to its spectral resolution. We used four different spectral libraries which contains around 2500 spectra of materials and land covers with a fine spectral resolution. These spectra were convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensors’ resolutions. Then, these reduced spectra were compared using separability indexes (Divergence, Transformed divergence, Bhattacharyya, Jeffreys-Matusita) and machine learning tools. In the experiments, we highlighted that the spectral bands configuration could lead to important differences in classification accuracy according to the context of application (e.g. urban area)

Crowdsourcing of Histological Image Labeling and Object Delineation by Medical Students

Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this work investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. 76 medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: (1) Labeling of images showing tissue regions, and (2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants' performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology

Approche formelle de fusion d'ontologies à l'aide des grammaires de graphes typés

L'article propose une approche formelle de fusion d'ontologies se reposant sur les grammaires de graphes typés. Elle se décompose en trois étapes : 1) la recherche de similarités entre concepts ; 2) la fusion des ontologies par l'approche algébrique SPO (Simple Push Out) ; 3) l'adaptation d'une ontologie globale par le biais de règles de réécriture de graphes. Contrairement aux solutions existantes, cette méthode offre une représentation formelle de la fusion d'ontologies ainsi qu'une implémentation fonctionnelle basée sur l'outil AGG

Algebraic graph transformations for merging ontologies

The conception of an ontology is a complex task influenced by numerous factors like the point of view of the authors or the level of details. Consequently, several ontologies have been developed to model identical or related domains leading to partially overlapping representations. This divergence of conceptualization requires the study of ontologies merging in order to create a common repository of knowledge and integrate various sources of information. In this paper, we propose a formal approach for merging ontologies using typed graph grammars. This method relies on the algebraic approach to graph transformations, SPO (Simple PushOut) which allows a formal representation and ensures the consistence of the results. Furthermore, a new ontologies merging algorithm called GROM (Graph Rewriting for Ontology Merging) is presented

Algebraic graph transformations for formalizing ontology changes and evolving ontologies

An ontology represents a consensus on the representation of the concepts and axioms of a given domain. This consensus is often reached through an iterative process, each iteration consisting in modifying the current version of the consensus. Furthermore, frequent and continuous changes are also occurring when the represented domain evolves or when new requirements have to be considered. Consequently, ontologies have to be adaptable to handle evolution, revision and refinement. However, this process is highly challenging as it is often difficult to understand all affected ontology parts when changes are performed. Thus, inconsistencies can occur in the ontology as the changes can introduce contradictory axioms. To address this issue, this paper presents a formal approach for evolving ontologies using Typed Graph Grammars. This method relies on the algebraic approach Simple PushOut (SPO) of graph transformations. It formalizes the ontology changes and proposes an a priori approach of inconsistencies resolution. The modified ontology does not need an explicit checking as an incorrect ontology version cannot actually be generated. To validate our proposal, an implementation is presented using the Attributed Graph Grammar (AGG) toolbox

Fast segmentation for texture-based cartography of whole slide images

In recent years, new optical microscopes have been developed, providing very high spatial resolution images called Whole Slide Images (WSI). The fast and accurate display of such images for visual analysis by pathologists and the conventional automated analysis remain challenging, mainly due to the image size (sometimes billions of pixels) and the need to analyze certain image features at high resolution. To propose a decision support tool to help the pathologist interpret the information contained by the WSI, we present a new approach to establish an automatic cartography of WSI in reasonable time. The method is based on an original segmentation algorithm and on a supervised multiclass classification using a textural characterization of the regions computed by the segmentation. Application to breast cancer WSI shows promising results in terms of speed and quality

Synthesizing Whole Slide Images

The increasing availability of digital whole slide images opens new perspectives for computer-assisted image analysis complementing modern histopathology, assuming we can implement reliable and efficient image analysis algorithms to extract the biologically relevant information. Both validation and supervised learning techniques typically rely on ground truths manually made by human experts. However, this task is difficult, subjective and usually not exhaustive. This is a well-known issue in the field of biomedical imaging, and a common solution is the use of artificial “phantoms”. Following this trend, we study the feasibility of synthesizing artificial histological images to create perfect ground truths. In this paper, we show that it is possible to generate a synthetic whole slide image with reasonable computing resources, and we propose a way to evaluate its quality

Web site audience segmentation using hybrid alignment techniques

We are working on behavioral marketing in the Internet. On one hand we observe the behavior of visitors, and on the other hand we trigger (in real-time) stimulations intended to alter this behavior. Real-time and mass-customization are the two challenges that we have to address. In this paper, we present a hybrid approach for clustering visitor sessions, based on a combination of global and local sequence alignments, such as Needleman-Wunsch and Smith-Waterman. Our goal is to define very simple approaches able to address about 80 % of visitor sessions to be segmented, and which can be easily turned into small pieces of program, to be run in parallel in thousands of web browsers

Consistent Ontologies Evolution Using Graph Grammars

Ontologies are often used for the meta-modelling of dynamic domains, therefore it is essential to represent and manage their changes and to adapt them to new requirements. Due to changes, an ontology may become invalid and non-interpretable. This paper proposes the use of the graph grammars to formalize and manage ontologies evolution. The objective is to present an a priori approach of inconsistencies resolutions to adapt the ontologies and preserve their consistency. A framework composed of different graph rewriting rules is proposed and presented using the AGG (Algebraic Graph Grammar) tool. As an application, the article considers the EventCCAlps ontology developed within the CCAlps European project

Learning fuzzy rules to characterize objects of interest from remote sensing images

In this article a new method for learning concepts from examples of objects provided by experts for remote sensing images is presented. The goal of this method is to give the geographer expert a description of complex objects of interest extracted from very high resolution remote sensing images. The description of such objects needs to handle imprecision inherent to segmentation and very high resolution images. The first step of this approach is to classify objects composing all the examples. This classification allows the learning of a rule describing how the examples are composed regarding the segmentation. Finally, this rule is used to extract objects corresponding to the examples. Experiments on a remote sensing image of a urban landscape in Toulouse, France are presented to show the relevance of the method