There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper describes the ATLAS experiment as installed in i ts experimental cavern at point 1 at CERN. It also presents a brief overview of the expec ted performance of the detector.

http://absimage.aps.org/image/SES08/MWS_SES08-2008-020010.pdf

The ATLAS Experiment at the CERN Large Hadron Collider

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

/pdf/using-deep-learning-for-image-based-plant-disease-detection-3ep7b222h5.pdf

Using Deep Learning for Image-Based Plant Disease Detection

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Course Description This is an advanced course in which we explore the field of Statistical Optics. Topics covered include such subjects as the statistical properties of natural (thermal) and laser light, spatial and temporal coherence, effects of partial coherence on optical imaging instruments, effects on imaging due to randomly inhomogeneous media, and a statistical treatment of the detection of light. Development of this more comprehensive model of the behavior of light draws upon the use of tools traditionally available to the electrical engineer, such as linear system theory and the theory of stochastic processes.

http://ieeexplore.ieee.org/iel5/3/23094/01073023.pdf

Statistical optics

We propose a 3D color point cloud processing pipeline to count apples on individual apple trees in trellis structured orchards. Fruit counting at the tree level requires separating trees, which is challenging in dense orchards. We employ point clouds acquired from the leaf-off orchard in winter period, where the branch structure is visible, to delineate tree crowns. We localize apples in point clouds acquired in harvest period. Alignment of the two point clouds enables mapping apple locations to the delineated winter cloud and assigning each apple to its bearing tree. Our apple assignment method achieves an accuracy rate higher than 95%. In addition to presenting a first proof of feasibility, we also provide suggestions for further improvement on our apple assignment pipeline.

Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds.

The current huge development of new 3D microscopic techniques (synchrotron microtomography, optical coherence tomography, light sheet microscopy, …) opens a large variety of new perspectives for life sciences. The contrasts of these new microscopies are mostly well understood on samples of known material content such as those used in physics or instrumentation studies. The situation is different when it comes to the interpretation of the contrasts observed with complex heterogeneous media found in biology. Therefore determining which 3D microscopy technique is suited for which biological question is a topic of current interest (see [1,2] for instance in our group).In this communication, we propose a comparison of the contrast observed with full-field optical coherence tomography (OCT) and propagation-based phase contrast tomography (PCT) on bone tissue at similar spatial resolution. A first comparison of OCT with standard absorption microtomography was given in [3] for bones and we extend this comparison to PCT which is known to provide enhanced contrast on bones at multiple scales [4]. The contrast of both these techniques are a priori interesting to be compared since they both rely on discontinuities of refraction index. This produces phase shift in PCT which operates in the X-ray domain with a monochromatic beam (generated by a synchrotron) while this generates direct intensity reflexion with OCT which only resorts to white light in the visible domain.

Comparison of propagation-based phase contrast tomography and full-field optical coherence tomography on bone tissue

We present a set of sensors dedicated to various architectural measurements on plants. These sensors have been developed in the framework of a parternship with INRA Angers to answer given practical constraints of cost, size and acquisition time. We demonstrate the interest of these automated tools in comparison with the current manual approach used by biologists. Mots cles : Capteur sans fil, plateforme Android, tomographie X, plateau tournant, caméra de profondeur.

David Rousseau

Papers

Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds.

Comparison of propagation-based phase contrast tomography and full-field optical coherence tomography on bone tissue

Instrumentation et capteurs innovants appliqués au phénotypage automatisé des végétaux