Automatic identification of cell files in light microscopic images of conifer wood

International audienceIn this paper, we present an automatic method to recognize cell files in light microscopic images of conifer wood. This original method is decomposed into three steps: the segmentation step which extracts some anatomical structures in the image, the classification step which identifies in these structures the interesting cells, and the cell files recognition step. Some preliminary results obtained on several species of conifers are presented and analyzed

JPEG2000-Based Data Hiding to Synchronously Unify Disparate Facial Data for Scalable 3D Visualization

International audienceWe present a scalable encoding strategy for the 3D facial data in various bandwidth scenarios. The scalability, needed to cater diverse clients, is achieved through the multiresolution characteristic of JPEG2000. The disparate 3D facial data is synchronously unified by the application of data hiding wherein the 2.5D facial model is embedded in the corresponding 2D texture in the discrete wavelet transform (DWT) domain. The unified file conforms to the JPEG2000 standard and thus no novel format is introduced. The method is effective and has the potential to be applied in videosurveillance and videoconference applications

3D Facial Visualization Through Adaptive Spread Spectrum Synchronous Scalable (A4S) Data Hiding

International audienceAn adaptive spread spectrum synchronous scalable(A4S) data hiding strategy is being put forward to integrate the disparate 3D facial visualization data, into a single JPEG2000 format file with the aim to cater diverse clients in various bandwidth scenarios. The method is both robust and imperceptible in the sense that the robustness of the spread spectrum (SS) is coupled with the removable embedding that ensures highest possible visualization quality. The SS embedding of the DWT-domain 2.5D facial model is carried out in the transform domain YCrCb components, of the 2D texture, from the coding stream of JPEG2000 codec just after the DWT stage. High depth map quality is ensured through the adaptation of synchronization during embedding that would exclude some highest frequency subbands. The results show that the method can be exploited for video-surveillance and video-conference applications

How to build an average model when samples are variably incomplete? Application to fossil data

International audienceIn paleontology, incomplete samples with small or large missing parts are frequently encountered. For example,dental crowns, which are widely studied in paleontology because of their potential interest in taxonomic and phylogenetic analyses, are nearly systematically affected by a variable degree of wear that alters considerably their shape. It is then difficult to compute a significant reference surface model based on classical methods which are used to build atlases from set of samples. In this paper, we present a general approach to deal with the problem of estimating an average model from a set of incomplete samples. Our method is based on a state-of-the-art non-rigid surface registration algorithm. In a first step, we detect missing parts which allows one to focus only on the common parts to get an accurate registration result. In a second step, we try to build average model of the missing parts by using information which is available in a subset of the samples. We specifically apply our method on teeth, and more precisely on the surface in between dentine and enamel issues (EDJ). We investigate the robustness and accuracy properties of the methods on a set of artificial samples representing a high degree of incompleteness. We compare the reconstructed complete shape to a ground-truth dataset. We then show some results on real data

Automatic coral reef fish identification and 3D measurement in the wild

In this paper we present a pipeline using stereo images in order to automatically identify, track in 3D fish, and measure fish population.Comment: This paper is in its draft version and should be improved in order to be published. This paper is issued from one Year of Engineering wor

Pooled Steganalysis in JPEG: how to deal with the spreading strategy?

International audienceIn image pooled steganalysis, a steganalyst, Eve, aims to detect if a set of images sent by a steganographer, Alice, to a receiver, Bob, contains a hidden message. We can reasonably assess that the steganalyst does not know the strategy used to spread the payload across images. To the best of our knowledge, in this case, the most appropriate solution for pooled steganalysis is to use a Single-Image Detector (SID) to estimate/quantify if an image is cover or stego, and to average the scores obtained on the set of images. In such a scenario, where Eve does not know the spreading strategies, we experimentally show that if Eve can discriminate among few well-known spreading strategies, she can improve her steganalysis performances compared to a simple averaging or maximum pooled approach. Our discriminative approach allows obtaining steganalysis efficiencies comparable to those obtained by a clairvoyant, Eve, who knows the Alice spreading strategy. Another interesting observation is that DeLS spreading strategy behaves really better than all the other spreading strategies. Those observations results in the experimentation with six different spreading strategies made on Jpeg images with J-UNIWARD, a state-of-the-art Single-Image-Detector, and a dis-criminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various spreading strategies

Augmented reality for agroforestry system design

In agriculture, thanks to the availability of mobile devices and the development of dedicated software, digital tools assist many tasks. Among digital technologies, augmented reality, the superimposition of virtual objects on views of the real world, is a powerful tool to visualize the evolution of a plot or to plan agricultural processes by interacting with digital representations of plants. This technology already helps for many works (Zheng and Campbell 2019; Janina et al. 2018; Katsaros et al. 2017). In this contribution, we propose a novel application case for agroforestry system design workshops. Agroforestry system design workshops gather different actors (farmers, extensionists, local policymakers etc.) and aim at collectively decide the tree species to plant, the spatial organisation of the system and the tree and crop management options. Currently, such workshops use whiteboards, maps or physical mock-ups representing the system with tokens, pins or toothpicks. The result of these workshops is a model that represents part of the plot. Such workshops promote discussion between the different actors, but the participants cannot observe the impact of their choices and it is impossible to see the evolution of the plot over the years. Moreover, the model isn't a usable plantation map, an expert must adapt it to the whole plot and translate it into a usable plantation map. In order to facilitate agroforestry system design and adoption, we propose to use augmented reality both for indoor design workshops and for outdoor field visits. For the indoor design application, our objective is to allow users to easily interact with a physical mock-up in order to facilitate user involvement and visualize quickly modification of the system by providing the users with information relevant to them: tree size, level of tree-crop competition, crop yield heterogeneity, etc. For example, a user could propose a specific tree species and visualize the consequences for the crops, as well as the agronomical or environmental performance of this system. For field visits, our aim is to visualise the impact of trees on the landscape, which would be useful for farmers to help them imagine their system, and for educational purposes, to foster discussions on the impacts of trees on crops, biodiversity, farming operations etc. Finally, the link between the indoor workshop and the in situ visualization, i.e. the implementation of the theoretical pattern in a particular plot of the farm to get the coordinates of the trees, could also benefit from augmented reality, thanks to automatic detection of the agroforestry pattern, replication of this pattern within field borders (with constraints) and generation of geographic coordinates of each tree. Thus, we identified three steps, in which augmented reality could facilitate discussions, projections and help decision-making in the process of agroforestry system design: - - - In the mock-up design stage, augmented reality allows visualizing the evolution of the plants and their constraints (tree growth, competition on light, spreading of diseases) In the mock-up implementation stage, the mock-up instantiation leads to the definition of a 3D scene, representing the future field. This implementation stage involves several processes such as pattern recognition, graph modelling, replication, and georeferencing At the in situ visualization stage, augmented reality is mobilized for realistic plants (trees, crops) visualisation in the plot including the possibility to simulate plant growth to see the impact on the landscape after 5, 10 or 50 years. We get some results for the two first steps. Figure 1 shows, on the left, an image acquired by a smartphone or a webcam, of a (simplified) agroforestry system mock-up, with 6 "P" paper markers representing poplar trees, 2 "C" markers representing crops and one "TRI" marker defining the reference coordinate system(trihedron). Augmented reality allows to overlay, in real-time, on this image virtual representations of the trees (green points represent the canopy as the trunk cannot be seen from this viewing angle), crops (currently restricted to the marker surface but the objective is to fill the whole alley between trees) and the coordinate system vectors. On the right part of Figure1, the mock-up has been automatically translated into a 3D scene in the Unity visualisation environment for further analysis. We propose here a methodology to move from the design stage to the mock-up implementation. It must integrate a complete description of the plot scene extracted from the image (which include to deal with the approximate marker positioning), and extract automatically relationships such as "the elements P2, P6 and P8 form a line" or "C1 is between the left line of trees and the middle line". We will then use them to infer the Ecosystem Service Functional Motif (ESFM) (Rafflegeau et al. 2019), which is the smallest pattern supporting all the ecosystem services targeted by the designers of the agroforestry system. During the instantiation step, the system repeats the EFSM automatically over the whole plot. An expert could correct, if necessary, to finalize the plantation map. Further work will focus on: (i) predicting useful information (such as light or water competition) from the ESFM using simulation models and visualising them as augmented reality on the mock-up; (ii) formalising constraints in the implementation step (e.g. no trees in small corners of the plot) to replicate the ESFM throughout the map of the actual plot; (iii) visualising realistic-looking trees in situ, directly in the farmer's fields

Graphs theory applied to agroforestry system design. Poster H10

Designing agroforestry systems is a complex task due to the numerous elements and their multiple interactions. Co-design workshops are useful to overcome these difficulties by gathering people from different disciplines. But these workshops use tools that do not provide a standardised representation of agroforestry systems nor the provision of ecosystem services. Our objective is thus to develop a conceptual information framework based on graphs that allows modeling the agroforestry system and its ecosystem functions. In the first step, we infer a topological graph from the position of the physical objects representing system components: trees, crops, tree lines, and their spatial relationships (e.g. adjacency, inclusion...). We represent then the relationship between ecosystem structure and functioning as a graph. Applying graph technics such as subgraph research, we can estimate whether a specific system (graph) can support a specific ecosystem function (subgraph). This results in a new graph-based model to describe both the spatial and functional relationships between elements of agroforestry systems. Figure 1 illustrates the different steps to apply this model to a simple agroforestry system: 1. Create a system mock-up with physical objects 2. Acquire the scene by a camera and identify physical objects 3. Automatically extract the topological graph 4. Perform subgraphs research to identify functions 5. Visualize the scene with both realistic trees and schematic representations of ecosystem functions In the future, this work will be combined with augmented reality to visualize the agroforestry system and the production of ecosystem services directly on the physical mock-up. Thus, our work will improve the efficiency of co-design workshop by (i) formalizing the knowledge on the relationship between structure and function in agroforestry systems and, by (ii) sharing this information with non-specialists in a visual way, while allowing intuitive interaction through the physical mock-up. Thus, the designed system answers farmer's needs better