Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Statistical moving object detection for mobile devices with camera

A novel and high-quality system for moving object detection in sequences recorded with moving cameras is proposed. This system is based on the collaboration between an automatic homography estimation module for image alignment, and a robust moving object detection using an efficient spatiotemporal nonparametric background modeling.

CDMA multiuser detection, neural networks, and statistical mechanics

A novel approach, based on statistical mechanics, to analyze typical performance of optimum code-division multiple-access (CDMA) multiuser detectors is reviewed. A `black-box' view ot the basic CDMA channel is introduced, based on which the CDMA multiuser detection problem is regarded as a `learning-from-examples' problem of the `binary linear perceptron' in the neural network literature. Adopting Bayes framework, analysis of the performance of the optimum CDMA multiuser detectors is reduced to evaluation of the average of the cumulant generating function of a relevant posterior distribution. The evaluation of the average cumulant generating function is done, based on formal analogy with a similar calculation appearing in the spin glass theory in statistical mechanics, by making use of the replica method, a method developed in the spin glass theory.

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.

The Lea Grating Test In Assessing Detection Grating Acuity In Normal Infants Less Than 4 Months Of Age.

To assess binocular detection grating acuity using the LEA GRATINGS test to establish age-related norms in healthy infants during their first 3 months of life. In this prospective, longitudinal study of healthy infants with clear red reflex at birth, responses to gratings were measured at 1, 2, and 3 months of age using LEA gratings at a distance of 28 cm. The results were recorded as detection grating acuity values, which were arranged in frequency tables and converted to a one-octave scale for statistical analysis. For the repeated measurements, analysis of variance (ANOVA) was used to compare the detection grating acuity results between ages. A total of 133 infants were included. The binocular responses to gratings showed development toward higher mean values and spatial frequencies, ranging from 0.55 ± 0.70 cycles per degree (cpd), or 1.74 ± 0.21 logMAR, in month 1 to 3.11 ± 0.54 cpd, or 0.98 ± 0.16 logMAR, in month 3. Repeated ANOVA indicated differences among grating acuity values in the three age groups. The LEA GRATINGS test allowed assessment of detection grating acuity and its development in a cohort of healthy infants during their first 3 months of life.

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Onion (Allium cepa) is one of the most cultivated and consumed vegetables in Brazil and its importance is due to the large laborforce involved. One of the main pests that affect this crop is the Onion Thrips (Thrips tabaci), but the spatial distribution of this insect, although important, has not been considered in crop management recommendations, experimental planning or sampling procedures. Our purpose here is to consider statistical tools to detect and model spatial patterns of the occurrence of the onion thrips. In order to characterize the spatial distribution pattern of the Onion Thrips a survey was carried out to record the number of insects in each development phase on onion plant leaves, on different dates and sample locations, in four rural properties with neighboring farms under different infestation levels and planting methods. The Mantel randomization test proved to be a useful tool to test for spatial correlation which, when detected, was described by a mixed spatial Poisson model with a geostatistical random component and parameters allowing for a characterization of the spatial pattern, as well as the production of prediction maps of susceptibility to levels of infestation throughout the area.

NDT flaw mapping of steel surfaces by continuous magnetic Barkhausen noise: Volumetric flaw detection case

This paper reports the use of a non-destructive, continuous magnetic Barkhausen noise (CMBN) technique to investigate the size and thickness of volumetric defects, in a 1070 steel. The magnetic behavior of the used probe was analyzed by numerical simulation, using the finite element method (FEM). Results indicated that the presence of a ferrite coil core in the probe favors MBN emissions. The samples were scanned with different speeds and probe configurations to determine the effect of the flaw on the CMBN signal amplitude. A moving smooth window, based on a second-order statistical moment, was used for analyzing the time signal. The results show the technique`s good repeatability, and high capacity for detection of this type of defect. (C) 2009 Elsevier Ltd. All rights reserved.

Genetic Diversity and a PCR-Based Method for Xanthomonas axonopodis Detection in Passion Fruit

Xanthomonas axonopodis pv. passiflorae causes bacterial spot in passion fruit. It attacks the purple and yellow passion fruit as well as the sweet passion fruit. The diversity of 87 isolates of pv. passiflorae collected from across 22 fruit orchards in Brazil was evaluated using molecular profiles and statistical procedures, including an unweighted pair-group method with arithmetical averages-based dendrogram, analysis of molecular variance (AMOVA), and an assigning test that provides information on genetic structure at the population level. Isolates from another eight pathovars were included in the molecular analyses and all were shown to have a distinct repetitive sequence-based polymerase chain reaction profile. Amplified fragment length polymorphism technique revealed considerable diversity among isolates of pv. passiflorae, and AMOVA showed that most of the variance (49.4%) was due to differences between localities. Cluster analysis revealed that most genotypic clusters were homogeneous and that variance was associated primarily with geographic origin. The disease adversely affects fruit production and may kill infected plants. A method for rapid diagnosis of the pathogen, even before the disease symptoms become evident, has value for producers. Here, a set of primers (Xapas) was designed by exploiting a single-nucleotide polymorphism between the sequences of the intergenic 16S-23S rRNA spacer region of the pathovars. Xapas was shown to effectively detect all pv. passiflorae isolates and is recommended for disease diagnosis in passion fruit orchards.

Cardiac MRI for Detection of Unrecognized Myocardial Infarction in Patients With End-Stage Renal Disease: Comparison With ECG and Scintigraphy

OBJECTIVE. The purposes of this study were to use the myocardial delayed enhancement technique of cardiac MRI to investigate the frequency of unrecognized myocardial infarction (MI) in patients with end-stage renal disease, to compare the findings with those of ECG and SPECT, and to examine factors that may influence the utility of these methods in the detection of MI. SUBJECTS AND METHODS. We prospectively performed cardiac MRI, ECG, and SPECT to detect unrecognized MI in 72 patients with end-stage renal disease at high risk of coronary artery disease but without a clinical history of MI. RESULTS. Fifty-six patients (78%) were men ( mean age, 56.2 +/- 9.4 years) and 16 (22%) were women ( mean age, 55.8 +/- 11.4). The mean left ventricular mass index was 103.4 +/- 27.3 g/m(2), and the mean ejection fraction was 60.6% +/- 15.5%. Myocardial delayed enhancement imaging depicted unrecognized MI in 18 patients (25%). ECG findings were abnormal in five patients (7%), and SPECT findings were abnormal in 19 patients (26%). ECG findings were false-negative in 14 cases and false-positive in one case. The accuracy, sensitivity, and specificity of ECG were 79.2%, 22.2%, and 98.1% (p = 0.002). SPECT findings were false-negative in six cases and false-positive in seven cases. The accuracy, sensitivity, and specificity of SPECT were 81.9%, 66.7%, and 87.0% ( not significant). During a period of 4.9-77.9 months, 19 cardiac deaths were documented, but no statistical significance was found in survival analysis. CONCLUSION. Cardiac MRI with myocardial delayed enhancement can depict unrecognized MI in patients with end-stage renal disease. ECG and SPECT had low sensitivity in detection of MI. Infarct size and left ventricular mass can influence the utility of these methods in the detection of MI.

Detection of nosocomial malnutrition is improved in Amazon region by a standard clinical nutrition education program

Background: In Brazil hospital malnutrition is highly prevalent. physician awareness of malnutrition is low, and nutrition therapy is underprescribed. One alternative to approach this problem is to educate health care providers in clinical nutrition. The present study aims to evaluate the effect of an intensive education course given to health care professionals and students on the diagnosis ability concerning to hospital malnutrition. Materials and methods: An intervention study based on a clinical nutrition educational program, offered to medical and nursing students and professionals, was held in a hospital of the Amazon region. Participants were evaluated through improvement of diagnostic ability, according to agreement of malnutrition diagnosis using Subjective Global Assessment before and after the workshop, as compared to independent evaluations (Kappa Index, k). To evaluate the impact of the educational intervention on the hospital malnutrition diagnosis, medical records were reviewed for documentation of parameters associated with nutritional status of in-patients. The SPSS statistical software package was used for data analysis. Results: A total of 165 participants concluded the program. The majority (76.4%) were medical and nursing students. Malnutrition diagnosis improved after the course (before k = 0.5; after k = 0.64; p < 0.05). A reduction of false negatives from 50% to 33.3% was observed. During the course, concern of nutritional diagnosis was increased W = 17.57; p < 0.001) and even after the course, improvement on the height measurement was detected chi(2) 12.87;p < 0.001). Conclusions: Clinical nutrition education improved the ability of diagnosing malnutrition; however the primary impact was on medical and nursing students. To sustain diagnostic capacity a clinical nutrition program should be part of health professional curricula and be coupled with continuing education for health care providers.

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and Technology

Detection of EBV-DNA in serum samples of an immunosuppressed child during a three years follow-up: association of clinical and PCR data with active infection

Twenty-four whole blood and serum samples were drawn from an eight year-old heart transplant child during a 36 months follow-up. EBV serology was positive for VCA-IgM and IgG, and negative for EBNA-IgG at the age of five years old when the child presented with signs and symptoms suggestive of acute infectious mononucleosis. After 14 months, serological parameters were: positive VCA-IgG, EBNA-IgG and negative VCA-IgM. This serological pattern has been maintained since then even during episodes suggestive of EBV reactivation. PCR amplified a specific DNA fragment from the EBV gp220 (detection limit of 100 viral copies). All twenty-four whole blood samples yielded positive results by PCR, while 12 out of 24 serum samples were positive. We aimed at analyzing whether detection of EBV-DNA in serum samples by PCR was associated with overt disease as stated by the need of antiviral treatment and hospitalization. Statistical analysis showed agreement between the two parameters evidenced by the Kappa test (value 0.750; p < 0.001). We concluded that detection of EBV-DNA in serum samples of immunosuppressed patients might be used as a laboratory marker of active EBV disease when a Real-Time PCR or another quantitative method is not available.