A field experiment on a steel Gerber-truss bridge for damage detection utilizing vehicle-induced vibrations

A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This article summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including the identified modal parameters and their statistical patterns, Nair’s damage indicator and its statistical pattern and different sets of measurement points. The modal parameters are identified by autoregressive time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis–Taguchi system, a multivariate pattern recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing Mahalanobis–Taguchi system on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of the modal parameters to damage. For Nair’s damage indicator, bridge damage detection could be achieved by performing Mahalanobis–Taguchi system on Nair’s damage indicators of most sets of measurement points. As a damage indicator, Nair’s damage indicator was superior to the modal parameters. Three main advantages were observed: it does not require any subjective decision in calculating Nair’s damage indicator, thus potential human errors can be prevented and an automatic detection task can be achieved; its statistical pattern has high sensitivity to damage and, finally, it is flexible regarding the choice of sets of measurement points.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.

An analysis of facial description in static images and video streams

[ES]This paper describes an analysis performed for facial description in static images and video streams. The still image context is first analyzed in order to decide the optimal classifier configuration for each problem: gender recognition, race classification, and glasses and moustache presence. These results are later applied to significant samples which are automatically extracted in real-time from video streams achieving promising results in the facial description of 70 individuals by means of gender, race and the presence of glasses and moustache.

Multiple face detection at different resolutions for perceptual user interfaces

[EN]This paper describes in detail a real-time multiple face detection system for video streams. The system adds to the good performance provided by a window shift approach, the combination of different cues available in video streams due to temporal coherence. The results achieved by this combined solution outperform the basic face detector obtaining a 98% success rate for around 27000 images, providing additionally eye detection and a relation between the successive detections in time by means of detection threads.

Who are you?

[EN]Most automatic recognition systems are focused on recognizing, given a single mug-shot of an individual, any new image of that individual. Most verification systems are designed to authenticate an identity provided by the user. However, the previous work rarely focus on the problem of detecting when a new individual, i.e., an unknown one, is present. The goal of the work presented in this paper deals with the possibility of providing the system with basic tools to detect when a new individual starts an interactive session, in order to allow the system to add or improve an identity model in the database. Experiments carried out with a set of 36 different individuals show promising results.

Multimodal attention system for an interactive robot

[EN]Social robots are receiving much interest in the robotics community. The most important goal for such robots lies in their interaction capabilities. An attention system is crucial, both as a filter to center the robot’s perceptual resources and as a mean of letting the observer know that the robot has intentionality. In this paper a simple but flexible and functional attentional model is described. The model, which has been implemented in an interactive robot currently under development, fuses both visual and auditive information extracted from the robot’s environment, and can incorporate knowledge-based influences on attention.

The ENCARA system for face detection and normalization

[EN]Facial image processing is becoming widespread in human-computer applications, despite its complexity. High-level processes such as face recognition or gender determination rely on low-level routines that must e ectively detect and normalize the faces that appear in the input image. In this paper, a face detection and normalization system is described. The approach taken is based on a cascade of fast, weak classi ers that together try to determine whether a frontal face is present in the image.

Before characterizing faces

[EN]This paper describes the approach for face detection and selection of frontal views, for further processing. This approach based on color detection and symmetry operator application, is integrated in an Active Vision System o ering promising results just making use of some opportunistic skills.

Focal length calibration for depth recovery using a focusing technique

[EN]The work presented in this paper is related to Depth Recovery from Focus The approach starts calibrating focal length of the camera using the Gaussian lens law for the thin lens camera model Two approaches are presented based on the availability of the internal distance of the lens

Fast and accurate hand pose detection for human-robot interaction

[EN]Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.

Fast classification in incrementally growing spaces

[EN]The classification speed of state-of-the-art classifiers such as SVM is an important aspect to be considered for emerging applications and domains such as data mining and human-computer interaction. Usually, a test-time speed increase in SVMs is achieved by somehow reducing the number of support vectors, which allows a faster evaluation of the decision function. In this paper a novel approach is described for fast classification in a PCA+SVM scenario. In the proposed approach, classification of an unseen sample is performed incrementally in increasingly larger feature spaces. As soon as the classification confidence is above a threshold the process stops and the class label is retrieved...