Multimodal Interactive Transcription of Handwritten Text Images

En esta tesis se presenta un nuevo marco interactivo y multimodal para la transcripción de Documentos manuscritos. Esta aproximación, lejos de proporcionar la transcripción completa pretende asistir al experto en la dura tarea de transcribir. Hasta la fecha, los sistemas de reconocimiento de texto manuscrito disponibles no proporcionan transcripciones aceptables por los usuarios y, generalmente, se requiere la intervención del humano para corregir las transcripciones obtenidas. Estos sistemas han demostrado ser realmente útiles en aplicaciones restringidas y con vocabularios limitados (como es el caso del reconocimiento de direcciones postales o de cantidades numéricas en cheques bancarios), consiguiendo en este tipo de tareas resultados aceptables. Sin embargo, cuando se trabaja con documentos manuscritos sin ningún tipo de restricción (como documentos manuscritos antiguos o texto espontáneo), la tecnología actual solo consigue resultados inaceptables. El escenario interactivo estudiado en esta tesis permite una solución más efectiva. En este escenario, el sistema de reconocimiento y el usuario cooperan para generar la transcripción final de la imagen de texto. El sistema utiliza la imagen de texto y una parte de la transcripción previamente validada (prefijo) para proponer una posible continuación. Despues, el usuario encuentra y corrige el siguente error producido por el sistema, generando así un nuevo prefijo mas largo. Este nuevo prefijo, es utilizado por el sistema para sugerir una nueva hipótesis. La tecnología utilizada se basa en modelos ocultos de Markov y n-gramas. Estos modelos son utilizados aquí de la misma manera que en el reconocimiento automático del habla. Algunas modificaciones en la definición convencional de los n-gramas han sido necesarias para tener en cuenta la retroalimentación del usuario en este sistema.Romero Gómez, V. (2010). Multimodal Interactive Transcription of Handwritten Text Images [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8541Palanci

Multi-Dimensional Medial Geometry: Formulation, Computation, and Applications

Medial axis is a classical shape descriptor. It is a piece of geometry that lies in the middle of the original shape. Compared to the original shape representation, the medial axis is always one dimension lower and it carries many intrinsic shape properties explicitly. Therefore, it is widely used in a large amount of applications in various fields. However, medial axis is unstable to the boundary noise, often referred to as its instability. A small amount of change on the object boundary can cause a dramatic change in the medial axis. To tackle this problem, a significance measure is often associated with the medial axis, so that medial points with small significance are removed and only the stable part remains. In addition to this problem, many applications prefer even lower dimensional medial forms, e.g., shape centers of 2D shapes, and medial curves of 3D shapes. Unfortunately, good significance measures and good definitions of lower dimensional medial forms are still lacking. In this dissertation, we extended Blum\u27s grassfire burning to the medial axis in both 2D and 3D to define a significance measure as a distance function on the medial axis. We show that this distance function is well behaved and it has nice properties. In 2D, we also define a shape center based on this distance function. We then devise an iterative algorithm to compute the distance function and the shape center. We demonstrate usefulness of this distance function and shape center in various applications. Finally we point out the direction for future research based on this dissertation

Communicative function and prosodic form in speech timing

Listeners can use variation in speech segment duration to interpret the structure of spoken utterances, but there is no systematic description of how speakers manipulate timing for communicative ends. Here I propose a functional approach to prosodic speech timing, with particular reference to English. The disparate findings regarding the production of timing effects are evaluated against the functional requirement that communicative durational variation should be perceivable and interpretable by the listener. In the resulting framework, prosodic structure is held to influence speech timing directly only at the heads and edges of prosodic domains, through large, consistent lengthening effects. As each such effect has a characteristic locus within its domain, speech timing cues are potentially disambiguated for the listener, even in the absence of other information. Diffuse timing effects – in particular, quasi-rhythmical compensatory processes implying a relationship between structure and timing throughout the utterance – are found to be weak and inconsistently observed. Furthermore, it is argued that articulatory and perceptual constraints make shortening processes less useful as structural cues, and they must be regarded as peripheral, at best, in a parsimonious and functionally-informed account

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Identification, synchronisation and composition of user-generated videos

Cotutela Universitat Politècnica de Catalunya i Queen Mary University of LondonThe increasing availability of smartphones is facilitating people to capture videos of their experience when attending events such as concerts, sports competitions and public rallies. Smartphones are equipped with inertial sensors which could be beneficial for event understanding. The captured User-Generated Videos (UGVs) are made available on media sharing websites. Searching and mining of UGVs of the same event are challenging due to inconsistent tags or incorrect timestamps. A UGV recorded from a fixed location contains monotonic content and unintentional camera motions, which may make it less interesting to playback. In this thesis, we propose the following identification, synchronisation and video composition frameworks for UGVs. We propose a framework for the automatic identification and synchronisation of unedited multi-camera UGVs within a database. The proposed framework analyses the sound to match and cluster UGVs that capture the same spatio-temporal event, and estimate their relative time-shift to temporally align them. We design a novel descriptor derived from the pairwise matching of audio chroma features of UGVs. The descriptor facilitates the definition of a classification threshold for automatic query-by-example event identification. We contribute a database of 263 multi-camera UGVs of 48 real-world events. We evaluate the proposed framework on this database and compare it with state-of-the-art methods. Experimental results show the effectiveness of the proposed approach in the presence of audio degradations (channel noise, ambient noise, reverberations). Moreover, we present an automatic audio and visual-based camera selection framework for composing uninterrupted recording from synchronised multi-camera UGVs of the same event. We design an automatic audio-based cut-point selection method that provides a common reference for audio and video segmentation. To filter low quality video segments, spatial and spatio-temporal assessments are computed. The framework combines segments of UGVs using a rank-based camera selection strategy by considering visual quality scores and view diversity. The proposed framework is validated on a dataset of 13 events (93~UGVs) through subjective tests and compared with state-of-the-art methods. Suitable cut-point selection, specific visual quality assessments and rank-based camera selection contribute to the superiority of the proposed framework over the existing methods. Finally, we contribute a method for Camera Motion Detection using Gyroscope for UGVs captured from smartphones and design a gyro-based quality score for video composition. The gyroscope measures the angular velocity of the smartphone that can be use for camera motion analysis. We evaluate the proposed camera motion detection method on a dataset of 24 multi-modal UGVs captured by us, and compare it with existing visual and inertial sensor-based methods. By designing a gyro-based score to quantify the goodness of the multi-camera UGVs, we develop a gyro-based video composition framework. A gyro-based score substitutes the spatial and spatio-temporal scores and reduces the computational complexity. We contribute a multi-modal dataset of 3 events (12~UGVs), which is used to validate the proposed gyro-based video composition framework.El incremento de la disponibilidad de teléfonos inteligentes o smartphones posibilita a la gente capturar videos de sus experiencias cuando asisten a eventos así como como conciertos, competiciones deportivas o mítines públicos. Los Videos Generados por Usuarios (UGVs) pueden estar disponibles en sitios web públicos especializados en compartir archivos. La búsqueda y la minería de datos de los UGVs del mismo evento son un reto debido a que los etiquetajes son incoherentes o las marcas de tiempo erróneas. Por otra parte, un UGV grabado desde una ubicación fija, contiene información monótona y movimientos de cámara no intencionados haciendo menos interesante su reproducción. En esta tesis, se propone una identificación, sincronización y composición de tramas de vídeo para UGVs. Se ha propuesto un sistema para la identificación y sincronización automática de UGVs no editados provenientes de diferentes cámaras dentro de una base de datos. El sistema propuesto analiza el sonido con el fin de hacerlo coincidir e integrar UGVs que capturan el mismo evento en el espacio y en el tiempo, estimando sus respectivos desfases temporales y alinearlos en el tiempo. Se ha diseñado un nuevo descriptor a partir de la coincidencia por parejas de características de la croma del audio de los UGVs. Este descriptor facilita la determinación de una clasificación por umbral para una identificación de eventos automática basada en búsqueda mediante ejemplo (en inglés, query by example). Se ha contribuido con una base de datos de 263 multi-cámaras UGVs de un total de 48 eventos reales. Se ha evaluado la trama propuesta en esta base de datos y se ha comparado con los métodos elaborados en el estado del arte. Los resultados experimentales muestran la efectividad del enfoque propuesto con la presencia alteraciones en el audio. Además, se ha presentado una selección automática de tramas en base a la reproducción de video y audio componiendo una grabación ininterrumpida de multi-cámaras UGVs sincronizadas en el mismo evento. También se ha diseñado un método de selección de puntos de corte automático basado en audio que proporciona una referencia común para la segmentación de audio y video. Con el fin de filtrar segmentos de videos de baja calidad, se han calculado algunas medidas espaciales y espacio-temporales. El sistema combina segmentos de UGVs empleando una estrategia de selección de cámaras basadas en la evaluación a través de un ranking considerando puntuaciones de calidad visuales y diversidad de visión. El sistema propuesto se ha validado con un conjunto de datos de 13 eventos (93 UGVs) a través de pruebas subjetivas y se han comparado con los métodos elaborados en el estado del arte. La selección de puntos de corte adecuados, evaluaciones de calidad visual específicas y la selección de cámara basada en ranking contribuyen en la mejoría de calidad del sistema propuesto respecto a otros métodos existentes. Finalmente, se ha realizado un método para la Detección de Movimiento de Cámara usando giróscopos para las UGVs capturadas desde smartphones y se ha diseñado un método de puntuación de calidad basada en el giro. El método de detección de movimiento de la cámara con una base de datos de 24 UGVs multi-modales y se ha comparado con los métodos actuales basados en visión y sistemas inerciales. A través del diseño de puntuación para cuantificar con el giróscopo cuán bien funcionan los sistemas de UGVs con multi-cámara, se ha desarrollado un sistema de composición de video basada en el movimiento del giroscopio. Este sistema basado en la puntuación a través del giróscopo sustituye a los sistemas de puntuaciones basados en parámetros espacio-temporales reduciendo la complejidad computacional. Además, se ha contribuido con un conjunto de datos de 3 eventos (12 UGVs), que se han empleado para validar los sistemas de composición de video basados en giróscopo.Postprint (published version

Broadband adaptive beamforming with low complexity and frequency invariant response

This thesis proposes different methods to reduce the computational complexity as well as increasing the adaptation rate of adaptive broadband beamformers. This is performed exemplarily for the generalised sidelobe canceller (GSC) structure. The GSC is an alternative implementation of the linearly constrained minimum variance beamformer, which can utilise well-known adaptive filtering algorithms, such as the least mean square (LMS) or the recursive least squares (RLS) to perform unconstrained adaptive optimisation.A direct DFT implementation, by which broadband signals are decomposed into frequency bins and processed by independent narrowband beamforming algorithms, is thought to be computationally optimum. However, this setup fail to converge to the time domain minimum mean square error (MMSE) if signal components are not aligned to frequency bins, resulting in a large worst case error. To mitigate this problem of the so-called independent frequency bin (IFB) processor, overlap-save based GSC beamforming structures have been explored. This system address the minimisation of the time domain MMSE, with a significant reduction in computational complexity when compared to time-domain implementations, and show a better convergence behaviour than the IFB beamformer. By studying the effects that the blocking matrix has on the adaptive process for the overlap-save beamformer, several modifications are carried out to enhance both the simplicity of the algorithm as well as its convergence speed. These modifications result in the GSC beamformer utilising a significantly lower computational complexity compare to the time domain approach while offering similar convergence characteristics.In certain applications, especially in the areas of acoustics, there is a need to maintain constant resolution across a wide operating spectrum that may extend across several octaves. To attain constant beamwidth is difficult, particularly if uniformly spaced linear sensor array are employed for beamforming, since spatial resolution is reciprocally proportional to both the array aperture and the frequency. A scaled aperture arrangement is introduced for the subband based GSC beamformer to achieve near uniform resolution across a wide spectrum, whereby an octave-invariant design is achieved. This structure can also be operated in conjunction with adaptive beamforming algorithms. Frequency dependent tapering of the sensor signals is proposed in combination with the overlap-save GSC structure in order to achieve an overall frequency-invariant characteristic. An adaptive version is proposed for frequency-invariant overlap-save GSC beamformer. Broadband adaptive beamforming algorithms based on the family of least mean squares (LMS) algorithms are known to exhibit slow convergence if the input signal is correlated. To improve the convergence of the GSC when based on LMS-type algorithms, we propose the use of a broadband eigenvalue decomposition (BEVD) to decorrelate the input of the adaptive algorithm in the spatial dimension, for which an increase in convergence speed can be demonstrated over other decorrelating measures, such as the Karhunen-Loeve transform. In order to address the remaining temporal correlation after BEVD processing, this approach is combined with subband decomposition through the use of oversampled filter banks. The resulting spatially and temporally decorrelated GSC beamformer provides further enhanced convergence speed over spatial or temporal decorrelation methods on their own

Detecting semantic concepts in digital photographs: low-level features vs. non-homogeneous data fusion

Semantic concepts, such as faces, buildings, and other real world objects, are the most preferred instrument that humans use to navigate through and retrieve visual content from large multimedia databases. Semantic annotation of visual content in large collections is therefore essential if ease of access and use is to be ensured. Classification of images into broad categories such as indoor/outdoor, building/non-building, urban/landscape, people/no-people, etc., allows us to obtain the semantic labels without the full knowledge of all objects in the scene. Inferring the presence of high-level semantic concepts from low-level visual features is a research topic that has been attracting a significant amount of interest lately. However, the power of lowlevel visual features alone has been shown to be limited when faced with the task of semantic scene classification in heterogeneous, unconstrained, broad-topic image collections. Multi-modal fusion or combination of information from different modalities has been identified as one possible way of overcoming the limitations of single-mode approaches. In the field of digital photography, the incorporation of readily available camera metadata, i.e. information about the image capture conditions stored in the EXIF header of each image, along with the GPS information, offers a way to move towards a better understanding of the imaged scene. In this thesis we focus on detection of semantic concepts such as artificial text in video and large buildings in digital photographs, and examine how fusion of low-level visual features with selected camera metadata, using a Support Vector Machine as an integration device, affects the performance of the building detector in a genuine personal photo collection. We implemented two approaches to detection of buildings that combine content-based and the context-based information, and an approach to indoor/outdoor classification based exclusively on camera metadata. An outdoor detection rate of 85.6% was obtained using camera metadata only. The first approach to building detection, based on simple edge orientation-based features extracted at three different scales, has been tested on a dataset of 1720 outdoor images, with a classification accuracy of 88.22%. The second approach integrates the edge orientation-based features with the camera metadata-based features, both at the feature and at the decision level. The fusion approaches have been evaluated using an unconstrained dataset of 8000 genuine consumer photographs. The experiments demonstrate that the fusion approaches outperform the visual features-only approach by of 2-3% on average regardless of the operating point chosen, while all the performance measures are approximately 4% below the upper limit of performance. The early fusion approach consistently improves all performance measures