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

The pair correlation integral is used to assess the intrinsic dimensionality of the three-dimensional histogram of RGB color images For application in the bounded colorimetric cube, this correlation measure is first calibrated on color histograms of reference constructed with integer dimensionality The measure is then applied to natural color images The results show that their color histogram tends to display a self-similar structure with noninteger fractal dimension Such a fractal organization in the colorimetric space can have relevance for image segmentation or classification, or other areas of color image processing

/pdf/pair-correlation-integral-for-fractal-characterization-of-2riisurq3f.pdf

Pair Correlation Integral for Fractal Characterization of Three-Dimensional Histograms from Color Images

Optical coherence tomography angiography (OCT-A) is an ophthalmic imaging technique which has recently been introduced to clinical use. OCT-A provides visualization of the retinal vascularization in three dimensions, without injection of contrast agents. OCT-A could thus replace the current standard of opthalmic imaging, which is 2D only and requires contrast agents. However, quantitative studies remain to be carried out to assess the full potential of OCT-A. In this context, the present work proposes a methodology to perform OCT-A in a more reproducible and precise way. We introduce a procedure to automatically extract the area of interest in avascular regions, which we demonstrate on various avascular areas with a focus on the optic nerve extracted in 2-dimensional images for a selected depth. We then study the repeatability of OCT-A with our segmentation technique when implemented on various clinical devices. For illustration, we apply this segmentation to healthy control group and to patients presenting different stages of glaucoma, a disease of clinical interest. The variability observed between these two cohorts compares favorably to the variability due to instrumental limitations or the segmentation algorithm. Our results thus constitute a significant step toward a more quantitative use of OCT-A in a clinical context.

/pdf/toward-quantitative-and-reproducible-clinical-use-of-oct-4yqejiqshc.pdf

Toward quantitative and reproducible clinical use of OCT-Angiography.

We demonstrate a new instance of useful-noise effect or stochastic resonance, occurring in magnetic resonance imaging (MRI). Based on the physics of signal–noise coupling specific to MRI, we establish the possibility of regimes where nonlinear post-processing can benefit from an increase in the level of the noise present in the MRI apparatus. The validation is obtained by both theoretical analysis and experimental observations. We especially show that the beneficial tuning of the noise can be practically achieved by controlling the bandwidth of the sampling receiver of the MRI apparatus. These results constitute a nontrivial extension of stochastic resonance in the domain of images, arising here with a signal–noise coupling in MRI which is distinct from the purely additive or multiplicative couplings previously investigated in the framework of useful-noise effect.

/pdf/tuning-the-noise-in-magnetic-resonance-imaging-to-maximize-250tejxuj7.pdf

Tuning the noise in magnetic resonance imaging to maximize nonlinear information transmission

In imaging, the choice of an observation scale is conventionally settled by the operator in charge of the image acquisition, who is left alone with tuning the framing and zooming parameters of the imaging system. In a somewhat decoupled manner, the operator in charge of processing the data has access to the images after their acquisition, and seeks to extract information from the observed scene. This Letter proposes a manifestation of the interest of an alternative joint acquisition-processing approach. We demonstrate with quantitative informational measures how the choice of an observation scale can be directly related to the performance of the final information processing task. Illustrations are given with various tools from statistical information theory with possible applications of practical interest to any noisy imaging domains.

/pdf/joint-acquisition-processing-approach-to-optimize-314nj5mkq4.pdf

Joint acquisition-processing approach to optimize observation scales in noisy imaging.

The impact of multiplicative speckle noise on data acquisition in coherent imaging is studied. This demonstrates the possibility to optimally adjust the level of the speckle noise in order to deliberately exploit, with maximum efficacy, the saturation naturally limiting linear image sensors such as CCD cameras, for instance. This constructive action of speckle noise cooperating with saturation can be interpreted as a novel instance of stochastic resonance or a useful-noise effect.

/pdf/optimizing-the-speckle-noise-for-maximum-efficacy-of-data-nydk9rq86f.pdf

David Rousseau

Papers

Pair Correlation Integral for Fractal Characterization of Three-Dimensional Histograms from Color Images

Toward quantitative and reproducible clinical use of OCT-Angiography.

Tuning the noise in magnetic resonance imaging to maximize nonlinear information transmission

Joint acquisition-processing approach to optimize observation scales in noisy imaging.

Optimizing the speckle noise for maximum efficacy of data acquisition in coherent imaging.