Optimal 3D Sensor Placement to Obtain Accurate 3D Point Positions

International audience3D measurements can be achieved from several views using the principle of optical triangulation. This paper deals with the problem of where to place cameras in order to obtain a minimal error in the detection. We pose the problem in terms of an optimization design, dividing this in two main components: 1) an analytical part dedicated to the analysis of error propagation from which a criterion is derivated, 2) an heuristical part which is going to minimize this criterion. In this way, the approach consists of an uncertainty analysis applied to the reconstruction process from which a covariance matrix is computed. This matrix represents the uncertainty of the detection from which the criteria is derived. Thus, a multicellular genetic algorithm is implemented in order to minimize the criterion. Graphical examples are provided to illustrate the effectiveness and efficiency of the solution

Automated Design of Salient Object Detection Algorithms with Brain Programming

Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain's inner workings. Progress on this research area follows the traditional path of hand-made designs using neuroscience knowledge. In recent years two different approaches based on genetic programming appear to enhance their technique. One follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The other approach consists of improving the inner computational structures of basic hand-made models through artificial evolution. This research work proposes expanding the artificial dorsal stream using a recent proposal to solve salient object detection problems. This approach uses the benefits of the two main aspects of this research area: fixation prediction and detection of salient objects. We decided to apply the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in comparison with the state-of-the-art.Comment: 35 pages, 5 figure

Mapping erosion risk at the basin scale in a Mediterranean environment with opencast coal mines to target restoration actions

34 páginas, 9 figurasRiver basin restoration and management is crucial for assuring the continued delivery of ecosystem services and for limiting potential hazards. Human activity, whether directly or indirectly, can induce erosion processes and drastically change the landscape and alter vital ecological functions. Mapping erosion risk before future restoration-management projects will help to reveal the priority areas and develop a hierarchy ordered according to need. For this purpose, we used the Revised Universal Soil Loss Equation (RUSLE) erosion model. We also applied a novel technique called GPVI (Genetic Programming Vegetation Index) in the Martín River basin in NE Spain (2,112 km2), which has a large coalfield located in the southern part of the basin. Approximately two-thirds (69%) of the area of the Martín basin presents low and medium soil loss rates, and one-third (31%) of the area presents high (18%), very high (10%), and irreversible (3%) erosion rates. The southern part of the basin is the most degraded and is strongly influenced by the topography. This work allows us to locate areas prone to erosional degradation processes to help create a buffer around the river and locate “spots” in need of restoration. We also checked the error estimation of the methodology because our soil maps do not include rock and bare rock areas. The usefulness of applying RUSLE for predicting degraded areas and the consequent directing of soil conservation–restoration actions at the basin scale is demonstrated. We highly recommend a field survey of the selected areas to prove the goodness of the model estimations.This work is part of the research and assistance agreement between Endesa S.A. and CSICPyrenean Institute of Ecology (IPE-CSIC). Funding for this study was provided by Endesa S.A. A special acknowledge is given to Endesa Centro Minero Andorra (Teruel). Thanks are given to, J. M. Garcia Ruiz, S. Begueria, E. Nadal, E. Moran-Tejera, and J.J. Jimenez for reviewing and general advises during the development of this work, M. P. Errea, J. Zabalza, L. C. Alatorre for assistance with GIS analysis, M. Angulo for R factor map, M. Pazos with statistical analysis, and F. Reverberi for laboratory work. M. Trabucchi was in receipt of grant from JAE-CSIC (Ref. I3P-BPD-2006).Peer reviewe

Estimación de erosión de suelos utilizando sensores remotos y programación genética

Los índices de vegetación (IVs) son ampliamente utilizados para extraer información de la vegetación a partir de imágenes satelitales. Los modelos de erosión, como la "Ecuación Universal Revisada de la Pérdida de Suelo" (RUSLE) usan IVs como insumo para estimar el factor de cobertura vegetal (C). El factor C es uno de los más importantes porque cuantifica la cobertura que actúa como capa protectora entre el suelo y los elementos atmosféricos. Sin embargo los IVs encontrados en el estado-delarte arrojan pobres resultados, ya que la mayoría de éstos están diseñados para detectar vegetación verde y no vegetación seca; la cual es también un importante factor que contribuye al desempeño del factor C. El propósito de esta investigación es desarrollar un método basado en programación genética para sintetizar IVs que estén mejor correlacionados con el factor C. Los resultados experimentales ilustran la eficiencia de este método y su efecto en el cálculo de erosión en una zona geográfica real. Los índices sintetizados obtienen una mejor aproximación al factor C obtenido en campo que cuando se utilizan los índices reportados en el estado-del-arte.Palabra(s) Clave(s): erosión por agua, índices de vegetación, programación genética, percepción remota, RUSLE

Speciation in Behavioral Space for Evolutionary Robotics

International audienceIn Evolutionary Robotics, population-based evolutionary computation is used to design robot neurocontrollers that produce behaviors which allow the robot to fulfill a user-defined task. However, the standard approach is to use canonical evolutionary algorithms, where the search tends to make the evolving population converge towards a single behavioral solution, even if the high-level task could be accomplished by structurally different behaviors. In this work, we present an approach that preserves behavioral diversity within the population in order to produce a diverse set of structurally different behaviors that the robot can use. In order to achieve this, we employ the concept of speciation, where the population is dynamically subdivided into sub-groups, or species, each one characterized by a particular behavioral structure that all individuals within that species share. Speciation is achieved by describing each neurocontroller using a representations that we call a behavior signature, these are descriptors that characterize the traversed path of the robot within the environment. Behavior signatures are coded using character strings, this allows us to compare them using a string similarity measure, and three measures are tested. The proposed behavior-based speciation is compared with canonical evolution and a method that speciates based on network topology. Experimental tests were carried out using two robot tasks (navigation and homing behavior), several training environments, and two different robots (Khepera and Pioneer), both real and simulated. Results indicate that behavior-based speciation increases the diversity of the behaviors based on their structure, without sacrificing performance. Moreover, the evolved controllers exhibit good robustness when the robot is placed within environments that were not used during training. In conclusion, the speciation method presented in this work allows an evolutionary algorithm to produce several robot behaviors that are structurally different but all are able to solve the same robot task

Local Search is Underused in Genetic Programming

Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied extensively, with many works proposing bloat control methods. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while also reducing program size. Given these results, a question arises: Why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.authorsversionpublishe