Modeling the environment with egocentric vision systems

Cada vez más sistemas autónomos, ya sean robots o sistemas de asistencia, están presentes en nuestro día a día. Este tipo de sistemas interactúan y se relacionan con su entorno y para ello necesitan un modelo de dicho entorno. En función de las tareas que deben realizar, la información o el detalle necesario del modelo varía. Desde detallados modelos 3D para sistemas de navegación autónomos, a modelos semánticos que incluyen información importante para el usuario como el tipo de área o qué objetos están presentes. La creación de estos modelos se realiza a través de las lecturas de los distintos sensores disponibles en el sistema. Actualmente, gracias a su pequeño tamaño, bajo precio y la gran información que son capaces de capturar, las cámaras son sensores incluidos en todos los sistemas autónomos. El objetivo de esta tesis es el desarrollar y estudiar nuevos métodos para la creación de modelos del entorno a distintos niveles semánticos y con distintos niveles de precisión. Dos puntos importantes caracterizan el trabajo desarrollado en esta tesis: - El uso de cámaras con punto de vista egocéntrico o en primera persona ya sea en un robot o en un sistema portado por el usuario (wearable). En este tipo de sistemas, las cámaras son solidarias al sistema móvil sobre el que van montadas. En los últimos años han aparecido muchos sistemas de visión wearables, utilizados para multitud de aplicaciones, desde ocio hasta asistencia de personas. - El uso de sistemas de visión omnidireccional, que se distinguen por su gran campo de visión, incluyendo mucha más información en cada imagen que las cámara convencionales. Sin embargo plantean nuevas dificultades debido a distorsiones y modelos de proyección más complejos. Esta tesis estudia distintos tipos de modelos del entorno: - Modelos métricos: el objetivo de estos modelos es crear representaciones detalladas del entorno en las que localizar con precisión el sistema autónomo. Ésta tesis se centra en la adaptación de estos modelos al uso de visión omnidireccional, lo que permite capturar más información en cada imagen y mejorar los resultados en la localización. - Modelos topológicos: estos modelos estructuran el entorno en nodos conectados por arcos. Esta representación tiene menos precisión que la métrica, sin embargo, presenta un nivel de abstracción mayor y puede modelar el entorno con más riqueza. %, por ejemplo incluyendo el tipo de área de cada nodo, la localización de objetos importantes o el tipo de conexión entre los distintos nodos. Esta tesis se centra en la creación de modelos topológicos con información adicional sobre el tipo de área de cada nodo y conexión (pasillo, habitación, puertas, escaleras...). - Modelos semánticos: este trabajo también contribuye en la creación de nuevos modelos semánticos, más enfocados a la creación de modelos para aplicaciones en las que el sistema interactúa o asiste a una persona. Este tipo de modelos representan el entorno a través de conceptos cercanos a los usados por las personas. En particular, esta tesis desarrolla técnicas para obtener y propagar información semántica del entorno en secuencias de imágen

Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition

Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embeddings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.Comment: Accepted at IJCB202

Learning Tuple Probabilities

Learning the parameters of complex probabilistic-relational models from labeled training data is a standard technique in machine learning, which has been intensively studied in the subfield of Statistical Relational Learning (SRL), but---so far---this is still an under-investigated topic in the context of Probabilistic Databases (PDBs). In this paper, we focus on learning the probability values of base tuples in a PDB from labeled lineage formulas. The resulting learning problem can be viewed as the inverse problem to confidence computations in PDBs: given a set of labeled query answers, learn the probability values of the base tuples, such that the marginal probabilities of the query answers again yield in the assigned probability labels. We analyze the learning problem from a theoretical perspective, cast it into an optimization problem, and provide an algorithm based on stochastic gradient descent. Finally, we conclude by an experimental evaluation on three real-world and one synthetic dataset, thus comparing our approach to various techniques from SRL, reasoning in information extraction, and optimization

Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation

Computer vision models learn to perform a task by capturing relevant statistics from training data. It has been shown that models learn spurious age, gender, and race correlations when trained for seemingly unrelated tasks like activity recognition or image captioning. Various mitigation techniques have been presented to prevent models from utilizing or learning such biases. However, there has been little systematic comparison between these techniques. We design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation. Using this benchmark, we provide a thorough analysis of a wide range of techniques. We highlight the shortcomings of popular adversarial training approaches for bias mitigation, propose a simple but similarly effective alternative to the inference-time Reducing Bias Amplification method of Zhao et al., and design a domain-independent training technique that outperforms all other methods. Finally, we validate our findings on the attribute classification task in the CelebA dataset, where attribute presence is known to be correlated with the gender of people in the image, and demonstrate that the proposed technique is effective at mitigating real-world gender bias.Comment: To appear in CVPR 202

Learning a Family of Detectors

Object detection and recognition are important problems in computer vision. The challenges of these problems come from the presence of noise, background clutter, large within class variations of the object class and limited training data. In addition, the computational complexity in the recognition process is also a concern in practice. In this thesis, we propose one approach to handle the problem of detecting an object class that exhibits large within-class variations, and a second approach to speed up the classification processes. In the first approach, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved with using a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. For applications where explicit parameterization of the within-class states is unavailable, a nonparametric formulation of the kernel can be constructed with a proper foreground distance/similarity measure. Detector training is accomplished via standard Support Vector Machine learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the image masks for foreground objects are provided in training, the detectors can also produce object segmentation. Methods for generating a representative sample set of detectors are proposed that can enable efficient detection and tracking. In addition, because individual detectors verify hypotheses of foreground state, they can also be incorporated in a tracking-by-detection frame work to recover foreground state in image sequences. To run the detectors efficiently at the online stage, an input-sensitive speedup strategy is proposed to select the most relevant detectors quickly. The proposed approach is tested on data sets of human hands, vehicles and human faces. On all data sets, the proposed approach achieves improved detection accuracy over the best competing approaches. In the second part of the thesis, we formulate a filter-and-refine scheme to speed up recognition processes. The binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the face recognition grand challenge version 2 data set, hand shape detection and parameter estimation on a hand data set, and vehicle detection and estimation of the view angle on a multi-pose vehicle data set. On all data sets, our approach is at least five times faster than simply evaluating all foreground state hypotheses with virtually no loss in classification accuracy

A "non-parametric" version of the naive Bayes classifier

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence and a Normal distribution of the variables) are perhaps violated. In previous work, we applied naive Bayes and other standard algorithms to a breast cancer database from Nottingham City Hospital in which the variables are highly non-Normal and found that the algorithm performed well when predicting a class that had been derived from the same data. However, when we then applied naive Bayes to predict an alternative clinical variable, it performed much worse than other techniques. This motivated us to propose an alternative method, based on naive Bayes, which removes the requirement for the variables to be Normally distributed, but retains the essential structure and other underlying assumptions of the method. We tested our novel algorithm on our breast cancer data and on three UCI datasets which also exhibited strong violations of Normality. We found our algorithm outperformed naive Bayes in all four cases and outperformed multinomial logistic regression (MLR) in two cases. We conclude that our method offers a competitive alternative to MLR and naive Bayes when dealing with data sets in which non-Normal distributions are observed