On covariate factor detection and removal for robust gait recognition

We propose a novel bolt-on module capable of boosting the robustness of various single compact 2D gait representations. Gait recognition is negatively influenced by covariate factors including clothing and time which alter the natural gait appearance and motion. Contrary to traditional gait recognition, our bolt-on module remedies this by a dedicated covariate factor detection and removal procedure which we quantitatively and qualitatively evaluate. The fundamental concept of the bolt-on module is founded on exploiting the pixel-wise composition of covariate factors. Results demonstrate how our bolt-on module is a powerful component leading to significant improvements across gait representations and datasets yielding state-of-the-art results.

Automatic detection and classification of football players

Color segmentation of images usually requires a manual selection and classification of samples to train the system. This paper presents an automatic system that performs these tasks without the need of a long training, providing a useful tool to detect and identify figures. In real situations, it is necessary to repeat the training process if light conditions change, or if, in the same scenario, the colors of the figures and the background may have changed, being useful a fast training method. A direct application of this method is the detection and identification of football players.

2D silhouette and 3D skeletal models for human detection and tracking

In this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a Principal Component Analysis (PCA). The problem of non-lineal PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model increase reliability and robustness.

Bacteria microarrays as sensitive tools for exploring pathogen surface epitopes and recognition by host receptors

We describe a protocol for the generation and validation of bacteria microarrays and their application to the study of specific features of the pathogen's surface and interactions with host receptors. Bacteria were directly printed on nitrocellulose-coated glass slides, using either manual or robotic arrayers, and printing quality, immobilization efficiency and stability of the arrays were rigorously controlled by incorporating a fluorescent dye into the bacteria. A panel of wild type and mutant strains of the human pathogen Klebsiella pneumoniae, responsible for nosocomial and community-acquired infections, was selected as model bacteria, and SYTO-13 was used as dye. Fluorescence signals of the printed bacteria were found to exhibit a linear concentration-dependence in the range of 1 x 10(8) to 1 x 10(9) bacteria per ml. Similar results were obtained with Pseudomonas aeruginosa and Acinetobacter baumannii, two other human pathogens. Successful validation of the quality and applicability of the established microarrays was accomplished by testing the capacity of the bacteria array to detect recognition by anti-Klebsiella antibodies and by the complement subcomponent C1q, which binds K. pneumoniae in an antibody-independent manner. The biotin/AlexaFluor-647-streptavidin system was used for monitoring binding, yielding strain-and dose-dependent signals, distinctive for each protein. Furthermore, the potential of the bacteria microarray for investigating specific features, e.g. glycosylation patterns, of the cell surface was confirmed by examining the binding behaviour of a panel of plant lectins with diverse carbohydrate-binding specificities. This and other possible applications of the newly developed arrays, as e.g. screening/evaluation of compounds to identify inhibitors of host-pathogen interactions, make bacteria microarrays a useful and sensitive tool for both basic and applied research in microbiology, biomedicine and biotechnology.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

Proposed wavelet-neurofuzzy combined system for power qualityviolations detection and diagnosis

A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet multiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed information. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is extensively reduced. A modified organisation map of the neurofuzzy classifier has significantly improved the diagnosis efficiency. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis