Tracking granules on the Sun's surface and reconstructing horizontal velocity fields: I. the CST algorithm

Determination of horizontal velocity fields on the solar surface is crucial for understanding the dynamics of structures like mesogranulation or supergranulation or simply the distribution of magnetic fields. We pursue here the development of a method called CST for coherent structure tracking, which determines the horizontal motion of granules in the field of view. We first devise a generalization of Strous method for the segmentation of images and show that when segmentation follows the shape of granules more closely, granule tracking is less effective for large granules because of increased sensitivity to granule fragmentation. We then introduce the multi-resolution analysis on the velocity field, based on Daubechies wavelets, which provides a view of this field on different scales. An algorithm for computing the field derivatives, like the horizontal divergence and the vertical vorticity, is also devised. The effects from the lack of data or from terrestrial atmospheric distortion of the images are also briefly discussed.Comment: in press in Astronomy and Astrophysics, 9 page

Holographie numérique de particules : amélioration de l'algorithme de dépouillement par optimisation

8 pagesNational audienceL'holographie numérique en ligne est une technique prometteuse dans le domaine de la visualisation quantitative des écoulements. Elle permet notamment de mesurer et de positionner en 3D des petits objets à partir de l'acquisition d'une seule image et avec un montage expérimental très simple. Le traitement numérique des images-hologramme correspondantes est un domaine beaucoup étudié actuellement. L'objet de cette communication est de proposer une amélioration d'un algorithme de dépouillement proposé par notre équipe. En effet, cet algorithme permet notamment d'augmenter la taille du champ et la précision sur l'estimation des paramètres des particules, mais il présente l'inconvénient d'un temps de calcul élevé. Après avoir rappelé le principe de l'algorithme initial, nous présentons des modifications et nous évaluons leur impact sur les performances à partir d'hologrammes synthétiques. Cette étude permet de conclure que la nouvelle version de l'algorithme permet de gagner un facteur allant de 2,5 à 4,9 sur le temps de calcul

String representations and distances in deep Convolutional Neural Networks for image classification

International audienceRecent advances in image classification mostly rely on the use of powerful local features combined with an adapted image representation. Although Convolutional Neural Network (CNN) features learned from ImageNet were shown to be generic and very efficient, they still lack of flexibility to take into account variations in the spatial layout of visual elements. In this paper, we investigate the use of structural representations on top of pre-trained CNN features to improve image classification. Images are represented as strings of CNN features. Similarities between such representations are computed using two new edit distance variants adapted to the image classification domain. Our algorithms have been implemented and tested on several challenging datasets, 15Scenes, Caltech101, Pas-cal VOC 2007 and MIT indoor. The results show that our idea of using structural string representations and distances clearly improves the classification performance over standard approaches based on CNN and SVM with linear kernel, as well as other recognized methods of the literature

D2.2: Evaluation of the usefulness of tools and end-user materials to farmers

Organic farming is knowledge intensive and in supporting farmers in enhancing their production systems, there is a need to improve how knowledge is shared. This is the overall aim of the OK Net Arable project. Work Package 2 of the project is concerned with facilitating the testing of practical and educational materials with farmer innovation groups to improve knowledge provision in this sector. The work package adopts an interactive multi-actor approach, bringing together practitioners from regional innovation groups with each other, and with advisers and scientists. This report presents feedback from farmers and advisors on knowledge exchange tools on the range of topics addressed in this project (such as soil and nutrient management, weed control, pest and diseases) and in a range of formats (technical guides, websites, decision support tools and videos). The feedback was gathered in 22 workshops held across 10 countries during 2016, working with established farmer innovation groups. It brings together some of the key findings that can help to inform the creation of knowledge exchange tools, which fit the needs of farmers, to support their learning and enhance organic farming

Fusion of tf.idf Weighted Bag of Visual Features for Image Classification

International audienceImage representation using bag of visual words approach is commonly used in image classification. Features are extracted from images and clustered into a visual vocabulary. Images can then be represented as a normalized histogram of visual words similarly to textual documents represented as a weighted vector of terms. As a result, text categorization techniques are applicable to image classification. In this paper, our contribution is twofold. First, we propose a suitable Term-Frequency and Inverse Document Frequency weighting scheme to characterize the importance of visual words. Second, we present a method to fuse different bag-of-words obtained with different vocabularies. We show that using our tf.idf normalization and the fusion leads to better classification rates than other normalization methods, other fusion schemes or other approaches evaluated on the SIMPLIcity collection

Approximate Image Matching using Strings of Bag-of-Visual Words Representation

International audienceThe Spatial Pyramid Matching approach has become very popular to model images as sets of local bag-of words. The image comparison is then done region-by-region with an intersection kernel. Despite its success, this model presents some limitations: the grid partitioning is predefined and identical for all images and the matching is sensitive to intra- and inter-class variations. In this paper, we propose a novel approach based on approximate string matching to overcome these limitations and improve the results. First, we introduce a new image representation as strings of ordered bag-of-words. Second, we present a new edit distance specifically adapted to strings of histograms in the context of image comparison. This distance identifies local alignments between subregions and allows to remove sequences of similar subregions to better match two images. Experiments on 15 Scenes and Caltech 101 show that the proposed approach outperforms the classical spatial pyramid representation and most existing concurrent methods for classification presented in recent years

Twin-image noise reduction by phase retrieval in in-line digital holography

14 pagesInternational audienceIn-line digital holography conciles the applicative interest of a simple optical set-up with the speed, low cost and potential of digital reconstruction. We address the twin-image problem that arises in holography due to the lack of phase information in intensity measurements. This problem is of great importance in in-line holography where spatial elimination of the twin-image cannot be carried out as in off-axis holography. Applications in digital holography of particle fields greatly depend on its suppression to reach greater particle concentrations, keeping a sufficient signal to noise ratio in reconstructed images. We describe in this paper methods to improve numerically the reconstructed images by twin-image reduction. ©2005 COPYRIGHT SPI

Spatial orientations of visual word pairs to improve Bag-of-Visual-Words model

International audienceThis paper presents a novel approach to incorporate spatial information in the bag-of-visual-words model for category level and scene classification. In the traditional bag-of-visual-words model, feature vectors are histograms of visual words. This representation is appearance based and does not contain any information regarding the arrangement of the visual words in the 2D image space. In this framework, we present a simple and effi- cient way to infuse spatial information. Particularly, we are interested in explicit global relationships among the spatial positions of visual words. Therefore, we take advantage of the orientation of the segments formed by Pairs of Identical visual Words (PIW). An evenly distributed normalized histogram of angles of PIW is computed. Histograms pro- duced by each word type constitute a powerful description of intra type visual words relationships. Experiments on challenging datasets demonstrate that our method is com- petitive with the concurrent ones. We also show that, our method provides important complementary information to the spatial pyramid matching and can improve the overall performance

Numerical suppression of the twin-image in in-line holography of a volume of micro-objects

This paper was published in Measurement Science and Technology and is made available as an electronic reprint with the permission of IOP. The paper can be found at the following URL on the IOP website: http://www.iop.org/EJ/journal/MSTInternational audienceWe address the twin-image problem that arises in holography due to the lack of phase information in intensity measurements. This problem is of great importance in in-line holography where spatial elimination of the twin image cannot be carried out as in off-axis holography. A unifying description of existing digital suppression methods is given in the light of deconvolution techniques. Holograms of objects spread in 3D cannot be processed through available approaches. We suggest an iterative algorithm and demonstrate its efficacy on both simulated and real data. This method is suitable to enhance the reconstructed images from a digital hologram of small objects

Spatial histograms of soft pairwise similar patches to improve the bag-of-visual-words model

International audienceIn the context of category level scene classification, the bag-of-visual-words model (BoVW) is widely used for image representation. This model is appearance based and does not contain any information regarding the arrangement of the visual words in the 2D image space. To overcome this problem, recent approaches try to capture information about either the absolute or the relative spatial location of visual words. In the first category, the so-called Spatial Pyramid Representation (SPR) is very popular thanks to its simplicity and good results. Alternatively, adding information about occurrences of relative spatial configurations of visual words was proven to be effective but at the cost of higher computational complexity, specifically when relative distance and angles are taken into account. In this paper, we introduce a novel way to incorporate both distance and angle information in the BoVW representation. The novelty is first to provide a computationally efficient representation adding relative spatial information between visual words and second to use a soft pairwise voting scheme based on the distance in the descriptor space. Experiments on challenging data sets MSRC-2, 15Scene, Caltech101, Caltech256 and Pascal VOC 2007 demonstrate that our method outperforms or is competitive with concurrent ones. We also show that it provides important complementary information to the spatial pyramid matching and can improve the overall performance