Jérôme Da Rugna

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.

Automatic blur detection for meta-data extraction in content-based retrieval context

During the last few years, image by content retrieval is the aim of many studies. A lot of systems were introduced in order to achieve image indexation. One of the most common method is to compute a segmentation and to extract different parameters from regions. However, this segmentation step is based on low level knowledge, without taking into account simple perceptual aspects of images, like the blur. When a photographer decides to focus only on some objects in a scene, he certainly considers very differently these objects from the rest of the scene. It does not represent the same amount of information. The blurry regions may generally be considered as the context and not as the information container by image retrieval tools. Our idea is then to focus the comparison between images by restricting our study only on the non blurry regions, using then these meta data. Our aim is to introduce different features and a machine learning approach in order to reach blur identification in scene images.

Hybrid color spaces applied to image database

Many difficulties of color image processing may be resolved using specific color spaces. The problematic when discussing about image database is the same: in which color space a method will be the most effective. We present classical color spaces, and a tool able to represent images in these spaces in order to analyze which color space is the most relevant on the studied images. Secondly we will introduce hybrid color spaces. The basic idea of hybrid color spaces is to combine several color components from different color spaces in order to increase the effectiveness of color components to discriminate color data, and to reduce correlation rate between color components. Generally computed from an unique image we propose an extension of hybrid computation to generate Hybrid color space from image database. The main idea is to use a set of images as a unique image, and to realize statistical computation on this “virtual” image. Finally, we will present a system able to manage hybrid color space generation on images set, using Icobra and ColorSpace tools.

Color interest points detector for visual information retrieval

The purpose of our visual information retrieval tool is to extract from a database images that are similar to an image query. Color features are generally used to define a measure of similarity between images, as they are usually very robust to noise, image degradation, changes in size, resolution or orientation. Nevertheless, the most often features suffer objectively from the lack of color spatial knowledge. Then, our purpose is to merge two classical methods : the color pyramid and the interest points detection, well-known for grey level image analysis. The pertinence of this new method is demonstrated by an evaluation and a comparison with others keypoints detectors. We show the interest for image indexation with concrete tests on our large images database, using the icobra system.

A robust approach to segment desired object based on salient colors

This paper presents a clustering-based color segmentation method where the desired object is focused on. As classical methods suffer from a lack of robustness, salient colors appearing in the object are used to intuitively tune the algorithm. These salient colors are extracted according to a psychovisual scheme and a peak-finding step. Results on various test sequences, covering a representative set of outdoor real videos, show the improvement when compared to a simple implementation of the same K-means oriented segmentation algorithm with ad hoc parameter setting strategy and with the well-known mean-shift algorithm.

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.

Blur Identification for Content Aware Processing in Images

In many types of photographies, it is desirable to have the entire image sharp. And in the opposite occurrence, some techniques try to deblur them. Classically, the blur is modeled in image processing as 訣岫捲岻 噺 岫月 茅 血岻岫捲岻. With x a pixel, g the degraded image, f the true image, * the operator of convolution and h the point spread function, noted PSF (Tao et al., 2005). The process of the image restoration is estimating the true image from the observed image (Savakis & Trussel, 1993). Many estimators like inverse filters or Wiener filters require a priori knowledge of the PSF. Nevertheless, PSF estimator gives more or less only information of smooth around a point but none information inside a specific zone. Another technique to extract blur information proposes to evaluate depth in image using in particular depth from focus or depth from defocus approaches (Ma & Staunton, 2005). We cannot confront these approaches to our problem as they require a bunch of images of the same scene obtained by combining different optic properties of the camera...where we consider only one image in this study.

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.