Selecting salient objects in real scenes: An oscillatory correlation model

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system. (C) 2010 Elsevier Ltd. All rights reserved.

Chaotic synchronization in general network topology for scene segmentation

Chaotic synchronization has been discovered to be an important property of neural activities, which in turn has encouraged many researchers to develop chaotic neural networks for scene and data analysis. In this paper, we study the synchronization role of coupled chaotic oscillators in networks of general topology. Specifically, a rigorous proof is presented to show that a large number of oscillators with arbitrary geometrical connections can be synchronized by providing a sufficiently strong coupling strength. Moreover, the results presented in this paper not only are valid to a wide class of chaotic oscillators, but also cover the parameter mismatch case. Finally, we show how the obtained result can be applied to construct an oscillatory network for scene segmentation.

3D reconstruction from a vascular tree model

[EN] In this paper, we present a vascular tree model made with synthetic materials and which allows us to obtain images to make a 3D reconstruction.We have used PVC tubes of several diameters and lengths that will let us evaluate the accuracy of our 3D reconstruction. In order to calibrate the camera we have used a corner detector. Also we have used Optical Flow techniques to follow the points through the images going and going back. We describe two general techniques to extract a sequence of corresponding points from multiple views of an object. The resulting sequence of points will be used later to reconstruct a set of 3D points representing the object surfaces on the scene. We have made the 3D reconstruction choosing by chance a couple of images and we have calculated the projection error. After several repetitions, we have found the best 3D location for the point.

A PDE model for computing the optical flow

[EN] In this paper we present a new model for optical flow calculation using a variational formulation which preserves discontinuities of the flow much better than classical methods. We study the Euler-Lagrange equations asociated to the variational problem. In the case of quadratic energy, we show the existence and uniqueness of the corresponding evolution problem. Since our method avoid linearization in the optical flow constraint, it can recover large displacement in the scene. We avoid convergence to irrelevant local minima by embedding our method into a linear scale-space framework and using a focusing strategy from coarse to fine scales.

Lag Camera: A Moving Multi-Camera Array for Scene-Acquisition

Many applications, such as telepresence, virtual reality, and interactive walkthroughs, require a three-dimensional(3D)model of real-world environments. Methods, such as lightfields, geometric reconstruction and computer vision use cameras to acquire visual samples of the environment and construct a model. Unfortunately, obtaining models of real-world locations is a challenging task. In particular, important environments are often actively in use, containing moving objects, such as people entering and leaving the scene. The methods previously listed have difficulty in capturing the color and structure of the environment while in the presence of moving and temporary occluders. We describe a class of cameras called lag cameras. The main concept is to generalize a camera to take samples over space and time. Such a camera, can easily and interactively detect moving objects while continuously moving through the environment. Moreover, since both the lag camera and occluder are moving, the scene behind the occluder is captured by the lag camera even from viewpoints where the occluder lies in between the lag camera and the hidden scene. We demonstrate an implementation of a lag camera, complete with analysis and captured environments.

Accident or homicide - virtual crime scene reconstruction using 3D methods

The analysis and reconstruction of forensically relevant events, such as traffic accidents, criminal assaults and homicides are based on external and internal morphological findings of the injured or deceased person. For this approach high-tech methods are gaining increasing importance in forensic investigations. The non-contact optical 3D digitising system GOM ATOS is applied as a suitable tool for whole body surface and wound documentation and analysis in order to identify injury-causing instruments and to reconstruct the course of event. In addition to the surface documentation, cross-sectional imaging methods deliver medical internal findings of the body. These 3D data are fused into a whole body model of the deceased. Additional to the findings of the bodies, the injury inflicting instruments and incident scene is documented in 3D. The 3D data of the incident scene, generated by 3D laser scanning and photogrammetry, is also included into the reconstruction. Two cases illustrate the methods. In the fist case a man was shot in his bedroom and the main question was, if the offender shot the man intentionally or accidentally, as he declared. In the second case a woman was hit by a car, driving backwards into a garage. It was unclear if the driver drove backwards once or twice, which would indicate that he willingly injured and killed the woman. With this work, we demonstrate how 3D documentation, data merging and animation enable to answer reconstructive questions regarding the dynamic development of patterned injuries, and how this leads to a real data based reconstruction of the course of event.

Model of ICT skills and new professional profile of the Guidance Counsellors in some European countries

El artículo analiza la utilización de las Tecnologías de la Información y Comunicación en el escenario de algunos países europeos en el ámbito de la Orientación Educativa y Profesional.

Model of ICT skills and new professional profile of the Guidance Counsellors in some European countries

El artículo analiza la utilización de las Tecnologías de la Información y Comunicación en el escenario de algunos países europeos en el ámbito de la Orientación Educativa y Profesional.

Model of ICT skills and new professional profile of the Guidance Counsellors in some European countries

El artículo analiza la utilización de las Tecnologías de la Información y Comunicación en el escenario de algunos países europeos en el ámbito de la Orientación Educativa y Profesional.

Proposal of a three level complexity model for office automation

Office automation is one of the fields where the complexity related with technologies and working environments can be best shown. This is the starting point we have chosen to build up a theoretical model that shows us a scene quite different from the one traditionally considered. Through the development of the model, the levels of complexity associated with office automation and office environments have been identified, establishing a relationship between them. Thus, the model allows to state a general principle for sociotechnical design of office automation systems, comprising the ontological distinctions needed to properly evaluate each particular technology and its virtual contribution to office automation. From this fact comes the model's taxonomic ability to draw a global perspective of the state-of-art in office automation technologies.

Accurate depth-color scene modeling for 3D contents generation with low cost depth cameras

In this paper, we present a depth-color scene modeling strategy for indoors 3D contents generation. It combines depth and visual information provided by a low-cost active depth camera to improve the accuracy of the acquired depth maps considering the different dynamic nature of the scene elements. Accurate depth and color models of the scene background are iteratively built, and used to detect moving elements in the scene. The acquired depth data is continuously processed with an innovative joint-bilateral filter that efficiently combines depth and visual information thanks to the analysis of an edge-uncertainty map and the detected foreground regions. The main advantages of the proposed approach are: removing depth maps spatial noise and temporal random fluctuations; refining depth data at object boundaries, generating iteratively a robust depth and color background model and an accurate moving object silhouette.

3D model based map building

Comunicación presentada en el IX Workshop de Agentes Físicos (WAF'2008), Vigo, 11-12 septiembre 2008.

GPGPU implementation of growing neural gas: application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

A generative probabilistic oriented wavelet model for texture segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.