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

Registration of complex and self similar images such as plant canopy images is a challenge in plant sciences. Yet, this is often a required step for multimodal imaging, where unaligned sensors yield unregistered image pairs. We propose a pipeline adapted to such constraints, applied t o a pple tree canopies.
Specifically, we apply an intensity based registration on downscaled and/or Gaussian blurred versions of the targeted images. This helps to eliminate spurious details, which smooths the optimization landscape and also helps to reduce differences between the modalities. Resul ts show better registration than with standard feature based or intensity based methods.

https://hal.archives-ouvertes.fr/hal-02183837/document

A strategy for multimodal canopy images registration

An optimal estimator can have its performance improved by an increase in the level of noise, a form of stochastic resonance. This is established by means of an optimal Bayesian estimator operating on a nonlinear signal-noise mixture with non-Gaussian noise. The addressed problem is the estimation of the frequency of a periodic signal corrupted by a phase noise. The optimal Bayesian estimator minimizing the mean square estimation error is explictly derived. Conditions where this minimal error decreases when the noise level increases are exhibited, both theoretically and with a Monte Carlo simulation of the optimal estimator. 19` eme Colloque GRETSI sur le Traitement du Signal et des Images, Paris, France, 8-11 sept. 2003 ; vol. III, pp. 50-53.

http://documents.irevues.inist.fr/bitstream/handle/2042/13769/A47.pdf;sequence=1

R´ esonance stochastique et performance am´ elior´ ee par le bruit pour un estimateur optimal

The subventricular zone (SVZ) is a region of ongoing postnatal germinal activity that shows complex spatial heterogeneity. For instance, different SVZ microdomains contain neural stem cells that express distinct transcription factors and generate different glial and neuronal progenies. These unique characteristics call for the development of new methods to integrate a spatial dimension to histological analyses performed in this germinal region. We developed “FlashMap”, a semi-automatic software that allows the segmentation and rapid measurement of optical densities throughout the full SVZ coordinates. “FlashMap” generates easily readable two-dimensional heatmaps that can be superimposed onto three-dimensional reconstructions of the ventricular system for optimal spatial exploration. Accurate heatmaps can be obtained, even following serial section subsampling thereby reducing the amount of tissue and time required for histological analysis. We first illustrate the potential of “FlashMap” by spatially exploring the correlation of SVZ thickness and cellular density with germinal activity throughout its rostro-caudal coordinates. We then used “FlashMap” to analyse the spatial expression of the transcription factors Dlx2, Tbr2 and Hopx as well as of the immature neuronal marker Dcx, to demonstrate the suitability of this approach to explore the regional production of cells of distinct lineages by defined SVZ microdomains.

/pdf/flashmap-a-semi-automatic-tool-for-rapid-and-accurate-1m2l6czoei.pdf

"FlashMap" - A Semi-Automatic Tool for Rapid and Accurate Spatial Analysis of Marker Expression in the Subventricular Zone.

Laser Doppler flowmetry (LDF) technique is an optical tool used in clinical investigations to monitor microvascular blood flow. Recent preliminary works have shown that LDF signals recorded in young healthy human subjects are weakly multifractal. Such an information is important as it could lead to a better knowledge of the underlying optical processes giving rise to the signals. In the present work, our goal is to analyze the behavior of LDF signals in anesthesia conditions. For this purpose, we herein study the possible modifications brought by isoflurane, an anesthetic commonly used in clinical practice, on the complexity of LDF signals. In order to conduct our work, twenty LDF signals from anesthetized healthy rats are processed. Anesthesia is performed by using doses of isoflurane varying between 1.5% and 3%, which leads to very light and very deep anesthesia, respectively. The signal processing approach is carried out with two different methods, a parametric generalized quadratic variation based estimation method and a Hurst rescaled range analysis. The results show that extreme doses of isoflurane lead to no distinguishable modification on the characterization of LDF signals based on the two above approaches. These findings infer that, if isoflurane changes the microvascular tissue optical properties, these modifications have no influence on LDF signals complexity measured by the two signal processing approaches used herein.

Laser Doppler flowmetry in healthy rats: impact of isoflurane anesthetic on signal complexity

We consider the problem of sorting cells from fluorescent markers spatial organization via single cell microscopy. This problem is important in bioimaging since this organization can reflect the healthy or pathological state of cells, the chromatin chain configurations or the spatial organization of DNA during main cell cycle. In this work, we address the question of the detection of spatial organization differences according to fluorescent markers density or distribution using textural features. We compare the performances by simulating 3D images and 2D images of the focal plane with various size of the point spread function of the confocal microscope, that is determined by varying the numerical aperture N A of the used objective lens.

David Rousseau

Papers

A strategy for multimodal canopy images registration

R´ esonance stochastique et performance am´ elior´ ee par le bruit pour un estimateur optimal

"FlashMap" - A Semi-Automatic Tool for Rapid and Accurate Spatial Analysis of Marker Expression in the Subventricular Zone.

Laser Doppler flowmetry in healthy rats: impact of isoflurane anesthetic on signal complexity

Sorting cells from fluorescent markers organization in confocal microscopy: 3D versus 2D images