Antonio Albiol

Face recognition using HOG-EBGM

This paper presents a new face recognition algorithm based on the well-known EBGM which replaces Gabor features by HOG descriptors. The recognition results show a better performance of our approach compared to other face recognition approaches using public available databases. This better performance is explained by the properties of HOG descriptors which are more robust to changes in illumination, rotation and small displacements, and to the higher accuracy of the face graphs obtained compared to classical Gabor-EBGM ones.

Detection of TV commercials

This paper presents a system that labels TV shots either as commercial or program shots. The system uses two observations: logo presence and shot duration. These observations are modeled using HMMs, and a Viterbi decoder is finally used for shot labeling. The system has been tested on several hours of real video, achieving more than 99% correct labeling.

A new adaptive scheme for ECG enhancement

Abstract ECG recordings during effort tests present different kinds of interferences. Several noise cancelling methods have been presented in literature, and, among them, adaptive ones have shown a good performance. In this paper, a new scheme for adaptive filtering of ECG signals is presented. It is based on two previously published algorithms which perform inter-beat average. Hence, their outputs are not always suitable. Our modifications provide a better output ECG signal since it is not an inter-beat average. Moreover, this scheme is simpler from a hardware point of view, as only one recording channel is required.

Flicker reduction in old films

This paper deals with the reduction of flicker in old films. This artifact appears as global, quick and random variations of the luminance and contrast between consecutive frames of a sequence. Initially, we present a method based on the correction of mean and variance parameters of the sequence. However, although this method reduces mean and variance variation between frames, it does not yield good visual results. Finally, an algorithm based on the histogram matching is proposed. This method provides much better visual results.

Detection of Parked Vehicles Using Spatiotemporal Maps

This paper presents a video-based approach to detect the presence of parked vehicles in street lanes. Potential applications include the detection of illegally and double-parked vehicles in urban scenarios and incident detection on roads. The technique extracts information from low-level feature points (Harris corners) to create spatiotemporal maps that describe what is happening in the scene. The method neither relies on background subtraction nor performs any form of object tracking. The system has been evaluated using private and public data sets and has proven to be robust against common difficulties found in closed-circuit television video, such as varying illumination, camera vibration, the presence of momentary occlusion by other vehicles, and high noise levels.

Precise eye localization using HOG descriptors

In this paper, we present a novel algorithm for precise eye detection. First, a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations. Then, a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of preselected eyes. Finally, we compare the eye detection results with three state-of-the-art works and a commercial software. The results show that our algorithm achieves the highest accuracy on the FERET and FRGCv1 databases, which is the most complete comparative presented so far.

Real-time high density people counter using morphological tools

The paper deals with an application of image sequence analysis. In particular, it addresses the problem of determining the number of people who get into and out of a train carriage when it is crowded and background and/or illumination might change. The proposed system analyses image sequences and processes them using an algorithm based on the use of several morphological tools and optical flow motion estimation.

Minimisation of the maximum error signal in active control

This paper deals with multiple input multiple output systems for active control of acoustic signals. These systems are used when the acoustic field is complex and therefore a number of sensors are necessary to estimate the sound field and a number of sources to create the cancelling field. A steepest descent iterative algorithm is applied to minimise the p-norm of a vector composed by the output signals of a microphone array. The existing algorithms deal with the 2-norm of this vector. This paper describes a general framework that covers the existing systems and then it focuses on the /spl infin/-norm minimisation algorithm. The minimax algorithm based on the /spl infin/-norm minimises the output signal which has the greatest power. It is shown by means of simulations using measured data from a real room that the minimax algorithm leads to a more uniform final noise field than the existing algorithms.

Low complexity cut detection in the presence of flicker

This paper deals with techniques to detect abrupt scene transitions when random brightness variations (flicker) are present. This is normally the case when trying to restore or index old films. The application of conventional techniques in this situation tends to produce a large number of false positive detection of cuts. The paper is intently restricted to techniques which require low computation (no motion estimation).

Minimization of the maximum error signal in active control

This paper presents a new multichannel adaptive filtering algorithm for the active control of single frequency noise in acoustic systems. Most active control systems with multiple error sensors minimize the sum of the modulus squared output of these sensors. An adaptive algorithm is presented that minimizes an alternative cost function which, in the limit, is equal to the maximum of the modulus squared values of all the error sensors. The physical consequences of minimizing the maximum modulus squared output to achieve noise reduction are investigated. An analytical framework is developed that covers the steady state performance as well as the convergence properties. By means of simulations, the proposed algorithm has been applied to a linear model of a one-dimensional (1-D) acoustic system and compared with the classical least squares solutions. The proposed algorithm is also used in simulations of the control of the pressure measured at 32 microphone positions in a room using 16 loudspeakers, when the room is excited with an 88 Hz pure tone. The results of these simulations show that the observed convergence and steady-state properties agree well with the theoretical predictions. A comparison with the least squares solutions leads to the conclusion that the proposed algorithm leads to a more uniform acoustic field in the enclosure than the classical least squares algorithm.