The CaliPhoto Method

International audienceWe propose an innovative method based on photography and image processing of interdisciplinary relevance, permitting the uncomplicated and inexpensive evaluation of material properties. This method-CaliPhoto-consists of using a dedicated colour plate with a specific design, placed in the field of view of a photograph of the material to be characterized. A specific image processing workflow is then applied to obtain colour vectors independent of illumination conditions. The method works using commercial colour cameras (e.g., smartphone cameras), and the colour plate can be printed on any colour printer. Herein, we describe the principle of the method and demonstrate that it can be used to describe and compare samples, identify materials or make relatively precise concentration measurements. The CaliPhoto method is highly complementary to any scientific research and may find applications across a range of domains, from planetary science to oceanography. The method may also be widely used in industry

Images processing dedicated to the morphological and nano structural characterization of carbon blacks in polymer matrices

Pour la confection des matériaux polymères à base de caoutchouc, le noir de carbone (NC) reste la charge renforçante la plus utilisée. Sa caractérisation morphologique et nano structurale est essentielle dans la maitrise des propriétés physico-chimiques qu’il confère aux matériaux auxquels il est mélangé. Les analyses classiques ne permettent d’accéder que de façon indirecte et incomplète à ces propriétés. Cette thèse propose une méthode de caractérisation innovante par le couplage d’un détecteur STEM (Scanning Transmission Electron Microscopy) et d’une chaine d’analyse d’images complètement automatique pour identifier les types de NC. Une étude statistique approfondie d’une centaine de caractéristiques morphologiques et structurales des NC a été réalisée sur les 6000 images STEM acquises. Cette étude a permis d’introduire 7 nouveaux descripteurs et de sélectionner les 37 descripteurs les plus discriminants pour la création du modèle d’identification. Pour rendre le processus le plus automatique possible, un algorithme de segmentation non supervisé a été développé et évalué. Cinq classifieurs ont ensuite été entraînés et comparés sur une base de près de 65000 agrégats. Le modèle le plus adapté s’avère les réseaux de neurones avec une identification des NC avoisinant les 100%. L’identification étant réalisée à partir de projections 2D des agrégats via les images STEM, une confrontation statistique valide la capacité des descripteurs 2D à caractériser la structure tridimensionnelle des NC observée par tomographies électroniques. L'approche complète proposée, depuis le protocole de préparation des échantillons et l'acquisition d'images STEM jusqu'à leur classification en passant par les étapes d'analyse d'images, offre une nouvelle méthode de caractérisation fiable des NC (à l’état natif ou au sein de mélanges élastomères) exploitable en routine.In the field of rubber material development, CB is the most commonly used reinforcing filler. The characterization of CB morphology and nanostructure is therefore crucial to understand the physicochemical properties induced by the introduction of CB in rubber materials. Classical analytical methods only allow indirect and incomplete access to these properties. This PhD offers an innovative method that allows the automatic identification of CB grades by coupling Scanning Transmission Electron Microscopy (STEM) detector and image processing chain. A thorough statistical investigation over a hundred of morphological and structural characteristics of CB was performed on a set of 6000 STEM images. This study has introduced 7 new features and selected the 37 most discriminating descriptors to create the final model. An unsupervised segmentation algorithm has been developed and evaluated in order to build an automatic process as efficient as possible. Then, five classifiers were trained and compared on a base of nearly 65,000 aggregates. It appears that the most suitable descriptor is the Neuron networks as it gives a perfect recognition. As the recognition model is based on 2D projections of CB aggregates, it is necessary to verify that the chosen descriptors are indeed able to correctly characterize the three dimensional structure of CB. The statistical comparison of the 2D descriptors with 3D descriptors extracted from electronic tomography images has been successful, and therefore demonstrates the relevance of the model. The proposed approach, starting from the sample preparation and STEM acquisitions to their classification and through the image analysis steps, offers a new and innovative method for the reliable characterization of CB. This method can be used routinely on raw CB or CB extracted from vulcanizes rubbers

CaliPhoto: a powerful method to identify rock powders on Mars

International audienceIntroduction In order to study unaltered rocks, Mars rovers are equipped with abrasive and/or drilling devices. NASA's Spirit and Opportunity rovers were equipped with a Rock Abrasion Tool to remove the first mm of altered material [1]. NASA's Curiosity and Perseverance and ESA's Rosalind Franklin ExoMars rovers are equipped with drilling device to collect samples for in situ analysis and, for Perseverance, in preparation for a future Mars Sample Return mission [2-4]. During these drilling phases, a pile of rock powder, of varying size depending on the drilling depth, forms at the surface.The objective of the ExoMars mission will be to search for past or extant biosignatures for which drill-cores will be collected from up to 2 meters deep; the depth at which organic matter is preserved from degrading UV and particle irradiation. The drill has a diameter of 3 cm. The cone of powder at the surface could thus represent more than 1.5 dm3, a relatively large quantity of material which will not be analysed by the instruments inside the rover but which could be observed by the CLUPI and PanCam cameras [4-6].Powder can be considered as a textureless material when the grain size is lower than the spatial resolution of the photograph, which is the case for rocks drilled on Mars as observed by MSL [2]. Colour is then the only measurable data; however, this apparent colour is totally dependent on ambient light and on the camera itself. In order to solve this problem, we have developed a new method called CaliPhoto, for which a reference plate is added to the camera's field of view and then image processing is used to compensate for camera characteristics and lighting conditions [7,8]. The images thus obtained can then be compared with each other or with a reference database. Here, we used a series of analogue rocks to demonstrate the ability of the method to identify volcanic rock powders on Mars. Materials and methods The majority of rocks on the surface of Mars are volcanic [9,10] thus, for this study, 23 relevant samples were selected from the Massif Central, in France, in order to cover a large range of volcanic rock types, as designated in the compositional TAS diagram (Total Alkali Silica). The samples were then crushed and each powder was placed in the centre of the CaliPhoto reference plate and photographed. The CaliPhoto image processing was then used to "calibrate" the photographs and a database was created (see Fig. 1).Figure 1: Images of the volcanic rock samples after CaliPhoto image processing. Results and discussion Different tests were carried out [8]. First, each sample was photographed twice in different lighting conditions, the first image was imported into the database and the second was used to test the identification procedure. For 50% of powders, the identification is exact, i.e., the studied powder corresponds to the highest matching identification from the database, in 77% of cases, the studied powder is in the top two matches, and in 95% of cases, it is in the top three. Moreover, when the studied powder is not in the first position, the best match occurs for a rock of similar or close composition.The analogue rock ESA-01-E (picrobasalt), chosen by ESA for its physical and chemical similarities to known Martian rocks, was then used to test the ability of the method to evaluate the composition of a powder that is not in the database. The method successfully identified the sample as a picrobasalt.The rocks were crushed at 4 different grain sizes in order to evaluate the effect of grain size distribution on the method. Indeed, the apparent luminosity of powder is known to increase with decreasing grain size. For 32% of the powders the identification is exact, i.e., the studied powder corresponds to the highest matching value. Moreover, for 91% of cases, a rock with a similar or adjacent composition as defined by the TAS diagram is in the top three matches, even when the powder is not in the database.Finally, by coupling hand sample and powder colour vectors, the identification is exact for 68% of rocks and the studied sample is in the top three matches in 100% of cases. Moreover, when the studied sample is only in the second or third position, the difference with the best match is always lower than 1%. Conclusion and perspectives The CaliPhoto method could be very useful on Mars to help identify rocks during drilling without adding any new instrumentation except a specific colour plate that could be positioned near the powders. Unfortunately, Mars rovers are not equipped with such a plate. Thus we proposed to use the calibration targets present on the rovers to calibrate the colour of the martian floor before drilling then to use it as reference for the CaliPhoto method. The first tests were relatively conclusive.Finally, with the postponement of the mission, the CaliPhoto colour plate could constitute a good complement to the ExoMars rover. AcknowledgementsWe acknowledge the Maison du parc national des volcans d'Auvergne for permission to sample. We thank CNRS, CNES and SATT Grand Centre for funding. References[1] Gorevan S. P. et al. (2003) J.-Geophys.-Res. 108.[2] Abbey W. et al. (2019) Icarus 319, 1-13.[3] Farley K. A. et al. (2020) Space Sci. Rev. 216, 142.[4] Vago J. L. et al. (2017) Astrobiology 17:6-7, 471-510.[5] Josset J.-L. et al. (2017) Astrobiology 17:6-7, 595-611.[6] Coates A. J. et al. (2017) Astrobiology 17:6-7, 511-541.[7] Foucher F. et al. (2019) Inventions 4, 67.[8] Foucher F. et al. (2022) Icarus 375, 114848.[9] McSween H.Y. et al. (2009) Science 324, 736, 2009.[10] Bost N. et al. (2013) Planet. Sp. Sci. 82-83, 113-127