Evolutionary ecology of reproduction in two mediterranean pine species: Pinus Pinaster Ait.and Pinus Halepensis Mill.

El estudio de los caracteres de historia vital es central en biología evolutiva y ecología, pues están íntimamente relacionados con el entorno vital de los organismos. Entre los caracteres de historia vital destacan los reproductivos, tales como el tamaño umbral de reproducción, la fecundidad y el reparto del esfuerzo reproductor a lo largo de la vida. Su estudio ayuda a entender procesos adaptativos pasados y a inferir los futuros. En el caso de los árboles forestales, este conocimiento es particularmente valioso y urgente dado el papel básico de los árboles forestales en ecosistemas de todo el mundo. Los caracteres de historia vital pueden ser estudiados a nivel de especie, pero comúnmente también existe variación intraespecífica entre y dentro de poblaciones. En el caso de numerosas especies vegetales es común observar cómo el momento de floración es variable y está relacionado con presiones selectivas naturales. Esta relación puede deberse a causas plásticas o genéticas. Un requisito para que se den respuestas a nivel genético es la existencia de variación genética aditiva. Ésta puede ser revelada mediante estudios de genética cuantitativa o estudios de selección artificial. Además, según la teoría de historia vital, la expresión de caracteres relacionados con la aptitud biológica (fitness) está limitada por la existencia de costes o compensaciones entre funciones. Pinus pinaster Ait. y P. halepensis Mill. son dos especies de pinos mediterráneos que presentan numerosas ventajas para el estudio de caracteres reproductivos en árboles desde un enfoque ecológico-evolutivo. Usando estas especies como modelo es posible integrar aspectos como la diferenciación entre poblaciones, adaptación local pasada y futura, plasticidad, arquitectura genética y relación con otros caracteres adaptativos.The study of life-history traits is central to evolutionary biology and ecology, as life-history traits are closely linked to the environment where organisms thrive. Among them, traits related to reproduction such as the threshold size for reproduction, fecundity and the schedule of reproductive investment along life are particularly relevant. Their study helps to understand past adaptive processes as well as to infer future ones. For forest trees, this knowledge is particularly valuable and urgent, given their founding role in ecosystems all over the world. Life-history traits can be addressed at the species level but usually, intraspecific within- and among-population variation also exists. This is the case for numerous plant species, as flowering time is commonly very variable and found to be correlated with natural selective pressures. This correlation may be due to plastic or genetic causes. A requirement for genetic responses to take place is the existence of additive genetic variation, which can be revealed by quantitative genetic studies or artificial selection experiments. Moreover, according to life-history theory, the expression of particular fitness-related traits is limited by costs or trade-offs with other traits also related to fitness. Pinus pinaster Ait. and P. halepensis Mill. are two Mediterranean pine species showing numerous advantages for the study of tree reproductive traits from an evolutionary-ecological standpoint. Using these species as a model, it is possible to integrate aspects such as population differentiation, past and future local adaptation, plasticity, genetic architecture and multi-trait adaptive relationships

Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion. This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the tracking performance. Several works address this problem using ego-motion compensation strategies. They use a deterministic approach to compensate the camera motion assuming a specific model of geometric transformation. However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations: Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter using the appearance information. This approach is able to adapt to different camera ego-motion conditions, and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR dataset, showing a high efficiency in the tracking of different types of targets in real working conditions

Automatic Feature-Based Stabilization of Video with Intentional Motion through a Particle Filter

Video sequences acquired by a camera mounted on a hand held device or a mobile platform are affected by unwanted shakes and jitters. In this situation, the performance of video applications, such us motion segmentation and tracking, might dramatically be decreased. Several digital video stabilization approaches have been proposed to overcome this problem. However, they are mainly based on motion estimation techniques that are prone to errors, and thus affecting the stabilization performance. On the other hand, these techniques can only obtain a successfully stabilization if the intentional camera motion is smooth, since they incorrectly filter abrupt changes in the intentional motion. In this paper a novel video stabilization technique that overcomes the aforementioned problems is presented. The motion is estimated by means of a sophisticated feature-based technique that is robust to errors, which could bias the estimation. The unwanted camera motion is filtered, while the intentional motion is successfully preserved thanks to a Particle Filter framework that is able to deal with abrupt changes in the intentional motion. The obtained results confirm the effectiveness of the proposed algorith

Aerial moving target detection based on motion vector field analysis

An efficient automatic detection strategy for aerial moving targets in airborne forward-looking infrared (FLIR) imagery is presented in this paper. Airborne cameras induce a global motion over all objects in the image, that invalidates motion-based segmentation techniques for static cameras. To overcome this drawback, previous works compensate the camera ego-motion. However, this approach is too much dependent on the quality of the ego-motion compensation, tending towards an over-detection. In this work, the proposed strategy estimates a robust motion vector field, free of erroneous vectors. Motion vectors are classified into different independent moving objects, corresponding to background objects and aerial targets. The aerial targets are directly segmented using their associated motion vectors. This detection strategy has a low computational cost, since no compensation process or motion-based technique needs to be applied. Excellent results have been obtained over real FLIR sequences

Learning 3D structure from 2D images using LBP features

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms

Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results

Enhanced automatic 2D-3D conversion using retinex in machine learning framework

In this paper, we present an approach for automatically convert images from 2D to 3D. The algorithm uses a color + depth dataset to estimate a depth map of a query color image by searching structurally similar images in the dataset and fusing them. Our experimental results indicate that the inclusion of a retinex based stage for the query image and the dataset images improves the performance of the system on commonly-used databases and for different image descriptors

Improved 2D-to-3D video conversion by fusing optical flow analysis and scene depth learning

Abstract: Automatic 2D-to-3D conversion aims to reduce the existing gap between the scarce 3D content and the incremental amount of displays that can reproduce this 3D content. Here, we present an automatic 2D-to-3D conversion algorithm that extends the functionality of the most of the existing machine learning based conversion approaches to deal with moving objects in the scene, and not only with static backgrounds. Under the assumption that images with a high similarity in color have likely a similar 3D structure, the depth of a query video sequence is inferred from a color + depth training database. First, a depth estimation for the background of each image of the query video is computed adaptively by combining the depths of the most similar images to the query ones. Then, the use of optical flow enhances the depth estimation of the different moving objects in the foreground. Promising results have been obtained in a public and widely used database

Genetic, environmental and ontogenetic effects on cone serotiny in Aleppo pine (Pinus halepensis Mill.): abstract

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