16 research outputs found

    Inferring Facial and Body Language

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    Machine analysis of human facial and body language is a challenging topic in computer vision, impacting on important applications such as human-computer interaction and visual surveillance. In this thesis, we present research building towards computational frameworks capable of automatically understanding facial expression and behavioural body language. The thesis work commences with a thorough examination in issues surrounding facial representation based on Local Binary Patterns (LBP). Extensive experiments with different machine learning techniques demonstrate that LBP features are efficient and effective for person-independent facial expression recognition, even in low-resolution settings. We then present and evaluate a conditional mutual information based algorithm to efficiently learn the most discriminative LBP features, and show the best recognition performance is obtained by using SVM classifiers with the selected LBP features. However, the recognition is performed on static images without exploiting temporal behaviors of facial expression. Subsequently we present a method to capture and represent temporal dynamics of facial expression by discovering the underlying low-dimensional manifold. Locality Preserving Projections (LPP) is exploited to learn the expression manifold in the LBP based appearance feature space. By deriving a universal discriminant expression subspace using a supervised LPP, we can effectively align manifolds of different subjects on a generalised expression manifold. Different linear subspace methods are comprehensively evaluated in expression subspace learning. We formulate and evaluate a Bayesian framework for dynamic facial expression recognition employing the derived manifold representation. However, the manifold representation only addresses temporal correlations of the whole face image, does not consider spatial-temporal correlations among different facial regions. We then employ Canonical Correlation Analysis (CCA) to capture correlations among face parts. To overcome the inherent limitations of classical CCA for image data, we introduce and formalise a novel Matrix-based CCA (MCCA), which can better measure correlations in 2D image data. We show this technique can provide superior performance in regression and recognition tasks, whilst requiring significantly fewer canonical factors. All the above work focuses on facial expressions. However, the face is usually perceived not as an isolated object but as an integrated part of the whole body, and the visual channel combining facial and bodily expressions is most informative. Finally we investigate two understudied problems in body language analysis, gait-based gender discrimination and affective body gesture recognition. To effectively combine face and body cues, CCA is adopted to establish the relationship between the two modalities, and derive a semantic joint feature space for the feature-level fusion. Experiments on large data sets demonstrate that our multimodal systems achieve the superior performance in gender discrimination and affective state analysis.Research studentship of Queen Mary, the International Travel Grant of the Royal Academy of Engineering, and the Royal Society International Joint Project

    TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

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    Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality

    Exploring 3D Shapes through Real Functions

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    This thesis lays in the context of research on representation, modelling and coding knowledge related to digital shapes, where by shape it is meant any individual object having a visual appareance which exists in some two-, three- or higher dimensional space. Digital shapes are digital representations of either physically existing or virtual objects that can be processed by computer applications. While the technological advances in terms of hardware and software have made available plenty of tools for using and interacting with the geometry of shapes, to manipulate and retrieve huge amount of data it is necessary to define methods able to effectively code them. In this thesis a conceptual model is proposed which represents a given 3D object through the coding of its salient features and defines an abstraction of the object, discarding irrelevant details. The approach is based on the shape descriptors defined with respect to real functions, which provide a very useful shape abstraction method for the analysis and structuring of the information contained in the discrete shape model. A distinctive feature of these shape descriptors is their capability of combining topological and geometrical information properties of the shape, giving an abstraction of the main shape features. To fully develop this conceptual model, both theoretical and computational aspects have been considered, related to the definition and the extension of the different shape descriptors to the computational domain. Main emphasis is devoted to the application of these shape descriptors in computational settings; to this aim we display a number of application domains that span from shape retrieval, to shape classification and to best view selection.Questa tesi si colloca nell\u27ambito di ricerca riguardante la rappresentazione, la modellazione e la codifica della conoscenza connessa a forme digitali, dove per forma si intende l\u27aspetto visuale di ogni oggetto che esiste in due, tre o pi? dimensioni. Le forme digitali sono rappresentazioni di oggetti sia reali che virtuali, che possono essere manipolate da un calcolatore. Lo sviluppo tecnologico degli ultimi anni in materia di hardware e software ha messo a disposizione una grande quantit? di strumenti per acquisire, rappresentare e processare la geometria degli oggetti; tuttavia per gestire questa grande mole di dati ? necessario sviluppare metodi in grado di fornirne una codifica efficiente. In questa tesi si propone un modello concettuale che descrive un oggetto 3D attraverso la codifica delle caratteristiche salienti e ne definisce una bozza ad alto livello, tralasciando dettagli irrilevanti. Alla base di questo approccio ? l\u27utilizzo di descrittori basati su funzioni reali in quanto forniscono un\u27astrazione della forma molto utile per analizzare e strutturare l\u27informazione contenuta nel modello discreto della forma. Una peculiarit? di tali descrittori di forma ? la capacit? di combinare propriet? topologiche e geometriche consentendo di astrarne le principali caratteristiche. Per sviluppare questo modello concettuale, ? stato necessario considerare gli aspetti sia teorici che computazionali relativi alla definizione e all\u27estensione in ambito discreto di vari descrittori di forma. Particolare attenzione ? stata rivolta all\u27applicazione dei descrittori studiati in ambito computazionale; a questo scopo sono stati considerati numerosi contesti applicativi, che variano dal riconoscimento alla classificazione di forme, all\u27individuazione della posizione pi? significativa di un oggetto

    Evolutionary Computation

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    This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

    WEB recommendations for E-commerce websites

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    In this part of the thesis we have investigated how the navigation utilizing web recommendations can be implemented on the e-commerce websites based on integrated data sources. The integrated e-commerce websites are an interesting use case for web recommendations. One of the reasons for this interest is that many modern, large and economically successful e-commerce websites follow the integrated approach. Another reason is that especially in the integrated environment, due to the lack of the pre-defined semantic connections between the data, the web recommendations step forward as means of enabling user navigation. In this chapter we have presented the architecture for the websites based on integrated data sources named EC-Fuice. We have also presented the prototypical implementation of our architecture which serves as a proof-of-concept and investigated the challenges of creating navigation on an integrated website. The following issues were addressed in this part of the thesis: Combination of several state-of-the-art tools and techniques in the fields of databases, data integration, ontology matching and web engineering into one generic architecture for creating integrated websites. Comparative experiments with several techniques for instance matching (also known as record linkage or duplicate detection). Investigation on using the ontology matching to facilitate the instance matching. Comparative experiments with several techniques for ontology matching. Investigations on the instance-based ontology matching and the possibilities for combining instance-based ontology matching with other techniques for ontology matching. Investigation of the possibilities to improve user navigation in the integrated data environment with different types of web recommendations. Review of the related work in the fields of data integration and ontology matching and discussion of the contact points between the research described here and other related projects. The main contributions of the research described in this part of the thesis are the EC-Fuice architecture, the novel method for matching e-commerce ontologies based on combination of instance information and metadata information, the experimental results of ontology and instance matching performed by different matching algorithms and the classification of the types of recommendations which can be used on an integrated e-commerce website
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