Gaël Chareyron

Phenoscope: an automated large‐scale phenotyping platform offering high spatial homogeneity

Increased phenotyping accuracy and throughput are necessary to improve our understanding of quantitative variation and to be able to deconstruct complex traits such as those involved in growth responses to the environment. Still, only a few facilities are known to handle individual plants of small stature for non-destructive, real-time phenotype acquisition from plants grown in precisely adjusted and variable experimental conditions. Here, we describe Phenoscope, a high-throughput phenotyping platform that has the unique feature of continuously rotating 735 individual pots over a table. It automatically adjusts watering and is equipped with a zenithal imaging system to monitor rosette size and expansion rate during the vegetative stage, with automatic image analysis allowing manual correction. When applied to Arabidopsis thaliana, we show that rotating the pots strongly reduced micro-environmental disparity: heterogeneity in evaporation was cut by a factor of 2.5 and the number of replicates needed to detect a specific mild genotypic effect was reduced by a factor of 3. In addition, by controlling a large proportion of the micro-environmental variance, other tangible sources of variance become noticeable. Overall, Phenoscope makes it possible to perform large-scale experiments that would not be possible or reproducible by hand. When applied to a typical quantitative trait loci (QTL) mapping experiment, we show that mapping power is more limited by genetic complexity than phenotyping accuracy. This will help to draw a more general picture as to how genetic diversity shapes phenotypic variation.

Tourist behavior analysis through geotagged photographies: A method to identify the country of origin

Much information can be extracted from geotagged photographies posted on online image databases like Flickr or Panoramio. Recent works have demonstrated that some treatment of this data can provide a good estimation of tourism behavior. Tourism represents today and for several years an important factor in the regional economy. Understanding and analyzing the tourist behavior corresponds to a significant demand from institutions. For this purpose, many studies have been launched. Many specialists of tourism need to separate tourists according to their place of residence. In the context of two projects supported by territorial collectivities, this paper introduces a new paradigm to estimate photographers country of residence. Each user will be described by his photographic timeline. This timeline allows to compute intermediate properties: travel time at a destination, number of trips, number of visited countries... This generation of symbolic data is essential and allows to synthesize the richness of the timeline in front of the recognition task to achieve. Classification algorithms will then be introduced, some sets with experts of science of tourism, others using data clustering and supervised learning techniques. We compared these methods for two distinct questions: firstly we classify photographers into two categories (French/non-French for example); secondly we find the country of residence of each user. It demonstrates that, using learning algorithms or expert-defined rules permits to identify users residence efficiently. We are thus able to meet the request of experts in tourism and refine even more the analysis of tourist behavior.

Big data: A new challenge for tourism

This paper proposes the foundations for a definition of digital tourist survey field based on the study of social networks. Several social network provide essential information on the perceptions of tourist destination. Nevertheless to operate correctly, several points should be discussed. This article provides multiple ideas for reflections on the challenges and opportunities offered by these new media.

Image watermarking based on a color quantization process

The purpose of this paper is to propose a color image watermarking scheme based on an image dependent color gamut sampling of the L*a*b* color space. The main motivation of this work is to control the reproduction of color images on different output devices in order to have the same color feeling, coupling intrinsic informations on the image gamut and output device calibration. This paper is focused firstly on the research of an optimal LUT (Look Up Table) which both circumscribes the color gamut of the studied image and samples the color distribution of this image. This LUT is next embedded in the image as a secret message. The principle of the watermarking scheme is to modify the pixel value of the host image without causing any change neither in image appearance nor on the shape of the image gamut.

Mining tourist routes using Flickr traces

This paper is about a new methodology to automatically rebuild main paths from Flickrs traces. Our geotagged image metadatas corpus allows the construction of each photographers timeline. The tourists itinerary in the destination can then be reconstructed considering two modes: the fastest path and the most likely path. Major paths are directly extracted from the fusion of all itineraries. To illustrate the multi-scale efficiency of this work, several fields of study are presented : Berlin, the Loire Valley and the Palace of Versailles.

Smart travel guide: from internet image database to intelligent system

To help the tourist to discover a city, a region or a park, many options are provided by public tourism travel centers, by free online guides or by dedicated book guides. Nonetheless, these guides provide only mainstream information which are not conform to a particular tourist behavior. On the other hand, we may find several online image databases allowing users to upload their images and to localize each image on a map. These websites are representative of tourism practices and constitute a proxy to analyze tourism flows. Then, this work intends to answer this question: knowing what I have visited and what other people have visited, where should I go now? This process needs to profile users, sites and photos. our paper presents the acquired data and relationship between photographers, sites and photos and introduces the model designed to correctly estimate the site interest of each tourism point. The third part shows an application of our schema: a smart travel guide on geolocated mobile devices. This android application is a travel guide truly matching the user wishes.

A new area tourist ranking method

This paper proposes a new method to classify tourist areas following specific characteristics. Indeed, we introduce some original computed indicators using social networks and administrative data. These 10 indicators are used to construct tourist areas profiles. We demonstrate in this paper that different tourist spots can then be grouped according to their profiles. Thus we present an original area tourist ranking method based on big data analysis.

Cognitive map of tourist behavior based on Tripadvisor

The objective of this paper is to identify, based on data from Tripadvisor, tourist behavior in how users rate a tourist place. Firstly, we propose different correspondence analyses (CA) on data from Tripadvisor to discover pairwise dependences between data properties (e.g. rating vs. place type, age vs. country). Secondly, we merge and map all our CA results as a cognitive map, both to bring out and understand influences between the studied concepts and to make easier human visualization.

A framework for analysis of large database of old art paintings

For many years, a lot of museums and countries organize the high definition digitalization of their own collections. In consequence, they generate massive data for each object. In this paper, we only focus on art painting collections. Nevertheless, we faced a very large database with heterogeneous data. Indeed, image collection includes very old and recent scans of negative photos, digital photos, multi and hyper spectral acquisitions, X-ray acquisition, and also front, back and lateral photos. Moreover, we have noted that art paintings suffer from much degradation: crack, softening, artifact, human damages and, overtime corruption. Considering that, it appears necessary to develop specific approaches and methods dedicated to digital art painting analysis. Consequently, this paper presents a complete framework to evaluate, compare and benchmark devoted to image processing algorithms.

Fully automatic leaf characterisation in heterogeneous environment of plant growing automation

In the last decade, we have seen a tremendous emergence of genome sequencing analysis systems. These systems are limited by the ability to phenotype numerous plants under controlled environmental conditions. To avoid this limitation, it is desirable to use an automated system designed with plants control growth feature in mind. For each experimental sequence, many parameters are subject to variations: illuminant, plant size and color, humidity, temperature, to name a few. These parameters variations require the adjustment of classical plant detection algorithms. This paper present an innovative and automatic imaging scheme for characterising the plants leafs growth. By considering a plant growth sequence it is possible, using the color histogram sequence, to detect day color variations and, then, to compute to set the algorithm parameters. The main difficulty is to take into account the automaton properties since the plant is not photographed exactly at the same position and angle. There is also an important evolution of the plant background, like moss, which needs to be taken into account. Ground truth experiments on several complete sequences will demonstrate the ability to identify the rosettes and to extract the plant characteristics whatever the culture conditions are.