Error induced by the estimation of the corneal power and the effective lens position with a rotationally asymmetric refractive multifocal intraocular lens

AIM: To evaluate the prediction error in intraocular lens (IOL) power calculation for a rotationally asymmetric refractive multifocal IOL and the impact on this error of the optimization of the keratometric estimation of the corneal power and the prediction of the effective lens position (ELP). METHODS: Retrospective study including a total of 25 eyes of 13 patients (age, 50 to 83y) with previous cataract surgery with implantation of the Lentis Mplus LS-312 IOL (Oculentis GmbH, Germany). In all cases, an adjusted IOL power (PIOLadj) was calculated based on Gaussian optics using a variable keratometric index value (nkadj) for the estimation of the corneal power (Pkadj) and on a new value for ELP (ELPadj) obtained by multiple regression analysis. This PIOLadj was compared with the IOL power implanted (PIOLReal) and the value proposed by three conventional formulas (Haigis, Hoffer Q and Holladay). RESULTS: PIOLReal was not significantly different than PIOLadj and Holladay IOL power (P>0.05). In the Bland and Altman analysis, PIOLadj showed lower mean difference (-0.07 D) and limits of agreement (of 1.47 and -1.61 D) when compared to PIOLReal than the IOL power value obtained with the Holladay formula. Furthermore, ELPadj was significantly lower than ELP calculated with other conventional formulas (P<0.01) and was found to be dependent on axial length, anterior chamber depth and Pkadj. CONCLUSION: Refractive outcomes after cataract surgery with implantation of the multifocal IOL Lentis Mplus LS-312 can be optimized by minimizing the keratometric error and by estimating ELP using a mathematical expression dependent on anatomical factors.

An improvement of a SLAM RGB-D method with movement prediction derived from a study of visual features

This paper presents a method for the fast calculation of a robot’s egomotion using visual features. The method is part of a complete system for automatic map building and Simultaneous Location and Mapping (SLAM). The method uses optical flow to determine whether the robot has undergone a movement. If so, some visual features that do not satisfy several criteria are deleted, and then egomotion is calculated. Thus, the proposed method improves the efficiency of the whole process because not all the data is processed. We use a state-of-the-art algorithm (TORO) to rectify the map and solve the SLAM problem. Additionally, a study of different visual detectors and descriptors has been conducted to identify which of them are more suitable for the SLAM problem. Finally, a navigation method is described using the map obtained from the SLAM solution.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Evaluating approximations generated by the GNG3D method for mesh simplification

In this paper we present different error measurements with the aim to evaluate the quality of the approximations generated by the GNG3D method for mesh simplification. The first phase of this method consists on the execution of the GNG3D algorithm, described in the paper. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The implementation of three error functions, named Eavg, Emax, Esur, permitts us to control the error of the simplified model, as it is shown in the examples studied.

Event-based visual servoing with features’ prediction

Event-based visual servoing is a recently presented approach that performs the positioning of a robot using visual information only when it is required. From the basis of the classical image-based visual servoing control law, the scheme proposed in this paper can reduce the processing time at each loop iteration in some specific conditions. The proposed control method enters in action when an event deactivates the classical image-based controller (i.e. when there is no image available to perform the tracking of the visual features). A virtual camera is then moved through a straight line path towards the desired position. The virtual path used to guide the robot improves the behavior of the previous event-based visual servoing proposal.

Variación espacial de la exposición a contaminación atmosférica en la ciudad de Valencia y su relación con un índice de privación

Objetivo: Evaluar la variación espacial de la exposición a dióxido de nitrógeno (NO2) en la ciudad de Valencia y su relación con la privación socioeconómica y la edad. Métodos: La población por sección censal (SC) procede del Instituto Nacional de Estadística. Los niveles de NO2 se midieron en 100 puntos del área de estudio, mediante captadores pasivos, en tres campañas entre 2002 y 2004. Se utilizó regresión por usos del suelo (LUR) para obtener el mapa de los niveles de NO2. Las predicciones del LUR se compararon con las proporcionadas por: a) el captador más cercano de la red de vigilancia, b) el captador pasivo más cercano, c) el conjunto de captadores en un entorno y d) kriging. Se asignaron niveles de contaminación para cada SC. Se analizó la relación entre los niveles de NO2, un índice de privación con cinco categorías y la edad (≥65 años). Resultados: El modelo LUR resultó el método más preciso. Más del 99% de la población superó los niveles de seguridad propuestos por la Organización Mundial de la Salud. Se encontró una relación inversa entre los niveles de NO2 y el índice de privación (β = –2,01 μg/m3 en el quintil de mayor privación respecto al de menor, IC95%: –3,07 a –0,95), y una relación directa con la edad (β = 0,12 μg/m3 por incremento en unidad porcentual de población ≥65 años, IC95%: 0,08 a 0,16). Conclusiones: El método permitió obtener mapas de contaminación y describir la relación entre niveles de NO2 y características sociodemográficas.

Chloride Penetration Prediction in Concrete through an Empirical Model Based on Constant Flux Diffusion

An empirical model based on constant flux is presented for chloride transport through concrete in atmospherical exposure conditions. A continuous supply of chlorides is assumed as a constant mass flux at the exposed concrete surface. The model is applied to experimental chloride profiles obtained from a real marine structure, and results are compared with the classical error-function model. The proposed model shows some advantages. It yields a better predictive capacity than the classical error-function model. The previously observed chloride surface concentration increases are compatible with the proposed model. Nevertheless, the predictive capacity of the model can fail if the concrete microstructure changes with time. The model seems to be appropriate for well-maturated concretes exposed to a marine environment in atmospherical conditions.