A Lagrangian descriptor applied to the Kuroshio current

The understanding of the circulation of ocean currents, the exchange of CO2 between atmosphere and oceans, and the in uence of the oceans on the distribution of heat on a global scale is key to our ability to predict and assess the future evolution of climate.

A global Lagrangian descriptor applied to the Kuroshio current

A global Lagrangian descriptor applied to the Kuroshio current

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

HOG-Like gradient-based descriptor for visual vehicle detection

One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

Analysing written production competence descriptors for academic and professional purposes and their calibration to the CEFR

The Common European Framework of Reference for Languages (CEFR) "describes in a comprehensive way what language learners have to learn to do in order to use a language for communication and what knowledge and skills they have to develop so as to be able to act effectively" (Council of Europe, 2001: 1). This paper reports on the findings of two studies whose purpose was to assess written production competence descriptors meant for their inclusion into the Academic and Professional English Language Portfolio KELP) for students of engineering and architecture. The main objective of these studies was to establish whether the language competence descriptors were a satisfactory valid tool in their language programmes from the point of view of clarity, relevance and reliability, as perceived by the students and fellow English for Academic Purposes (RAP) / English for Science and Technology (EST) instructors. The studies shed light on how to improve unsatisfactory descriptors. Results show that the final descriptor lists were on the whole well calibrated and fairly well written: the great majority was considered valid for both teachers and students involved.

Comparación de algoritmos detectores de puntos singulares para reconocimiento de objetos en vídeo quirúrgico.

El análisis de vídeo laparoscópico ofrece nuevas posibilidades a la navegación quirúrgica al garantizar una incorporación mínima de tecnología en quirófano, evitando así alterar la ergonomía y los flujos de trabajo de las intervenciones. Una de sus principales ventajas es que puede servir como fuente de datos para reconstruir tridimensionalmente la escena laparoscópica, lo que permite dotar al cirujano de la sensación de profundidad perdida en este tipo de cirugía. En el presente trabajo de investigación se comparan dos detectores de puntos singulares, SIFT y SURF, para estimar cuál de los dos podría integrarse en un algoritmo de cálculo de coordenadas 3D, MonoSLAM, basado en la detección y el seguimiento de estos puntos singulares en los fotogramas del vídeo. Los resultados obtenidos posicionan a SURF como la mejor opción gracias a su rapidez y a su mayor capacidad de discriminación entre estructuras anatómicas e instrumental quirúrgico.

Identification procedure of mealy apples by instrumental means. Extraction of storage features.

Mealiness is a negative attribute of sensory texture that combines the sensation of a disaggregated tissue with the sensation of lack of juiciness. Since January 1996, a wide EC Project entitled : "Mealiness in fruits. Consumers perception and means for detection'" is being carried out. Within it, three sensory panels have been trained at : the Institute of Food Research (IFR, United Kingdom), the Instituto de Agroquímica y Tecnología de los Alimentos (IATA, Spain) and the Institut voor Agrotechnologisch Onderzoek (ATO-DLO, Netherlands) to assess mealiness in apples. In all three cases, mealiness has been described as a multidimensional sensory descriptor capable of gathering the loss of consistency (of crispness and of hardness) and of juiciness. Also within the EC Project several instrumental procedures have been tested for mealiness assessment. In this sense the Physical Properties Laboratory (ETS1A-UPM) has focused its aims in a first stage on performing instrumental tests for assessing some textural descriptors as crispiness, hardness and juiciness. The results obtained within these tests have shown to correlate well with the sensory measurements (Barreiro et Ruiz-Altisent, 1997) in apples, but also have succeed when trying to generate several texture degradation levels on peaches from which mealiness appears to be the last stage (Ortiz et al. 1997).

Direct estimation wave setup as a medium level in swash

The extreme runup is a key parameter for a shore risk analysis in which the accurate and quantitative estimation of the upper limit reached by waves is essential. Runup can be better approximated by splitting the setup and swash semi-amplitude contributions. In an experimental study recording setup becomes difficult due to infragravity motions within the surf zone, hence, it would be desirable to measure the setup with available methodologies and devices. In this research, an analysis is made of evaluated the convenience of direct estimation setup as the medium level in the swash zone for experimental runup analysis through a physical model. A physical mobile bed model was setup in a wave flume at the Laboratory for Maritime Experimentation of CEDEX. The wave flume is 36 metres long, 6.5 metres wide and 1.3 metres high. The physical model was designed to cover a reasonable range of parameters, three different slopes (1/50, 1/30 and 1/20), two sand grain sizes (D50 = 0.12 mm and 0.70 mm) and a range for the Iribarren number in deep water (ξ0) from 0.1 to 0.6. Best formulations were chosen for estimating a theoretical setup in the physical model application. Once theoretical setup had been obtained, a comparison was made with an estimation of the setup directly as a medium level of the oscillation in swash usually considered in extreme runup analyses. A good correlation was noted between both theoretical and time-averaging setup and a relation is proposed. Extreme runup is analysed through the sum of setup and semi-amplitude of swash. An equation is proposed that could be applied in strong foreshore slope-dependent reflective beaches.

Neuroanatomic-based detection algorithm for automatic labeling of brain structures in brain injury

The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient. However, the body of knowledge and clinical intervention guides are basically focused on functional disorder and they still do not take into account the location of injuries. The prognostic value of location information is not known in detail either. This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical and sub-cortical areas. Themain goal is to register injured neuroanatomic structures to generate a database containing patient?s structural impairment profile. This kind of information permits to establish a relation with functional disorders and the prognostic evolution during neurorehabilitation procedures.

Image analysis by moment invariants using a set of step-like basis functions

Moment invariants have been thoroughly studied and repeatedly proposed as one of the most powerful tools for 2D shape identification. In this paper a set of such descriptors is proposed, being the basis functions discontinuous in a finite number of points. The goal of using discontinuous functions is to avoid the Gibbs phenomenon, and therefore to yield a better approximation capability for discontinuous signals, as images. Moreover, the proposed set of moments allows the definition of rotation invariants, being this the other main design concern. Translation and scale invariance are achieved by means of standard image normalization. Tests are conducted to evaluate the behavior of these descriptors in noisy environments, where images are corrupted with Gaussian noise up to different SNR values. Results are compared to those obtained using Zernike moments, showing that the proposed descriptor has the same performance in image retrieval tasks in noisy environments, but demanding much less computational power for every stage in the query chain.

New generation of human machine interfaces for controlling UAV through depth based gesture recognition

New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

Traffic sign detection and tracking using robust 3D analysis

In this paper we present an innovative technique to tackle the problem of automatic road sign detection and tracking using an on-board stereo camera. It involves a continuous 3D analysis of the road sign during the whole tracking process. Firstly, a color and appearance based model is applied to generate road sign candidates in both stereo images. A sparse disparity map between the left and right images is then created for each candidate by using contour-based and SURF-based matching in the far and short range, respectively. Once the map has been computed, the correspondences are back-projected to generate a cloud of 3D points, and the best-fit plane is computed through RANSAC, ensuring robustness to outliers. Temporal consistency is enforced by means of a Kalman filter, which exploits the intrinsic smoothness of the 3D camera motion in traffic environments. Additionally, the estimation of the plane allows to correct deformations due to perspective, thus easing further sign classification.

Fast human pose estimation using 3D Zernike descriptors

Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.

Efficient hybrid monocular-stereo approach to on-board, video-based traffic sign detection and tracking

In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion