In-loop Feature Tracking for Structure and Motion with Out-of-core Optimization

In this paper, a novel and approach for obtaining 3D models from video sequences captured with hand-held cameras is addressed. We define a pipeline that robustly deals with different types of sequences and acquiring devices. Our system follows a divide and conquer approach: after a frame decimation that pre-conditions the input sequence, the video is split into short-length clips. This allows to parallelize the reconstruction step which translates into a reduction in the amount of computational resources required. The short length of the clips allows an intensive search for the best solution at each step of reconstruction which robustifies the system. The process of feature tracking is embedded within the reconstruction loop for each clip as opposed to other approaches. A final registration step, merges all the processed clips to the same coordinate frame

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

Correlating a documentary record of historical floods with the dendrochronological dating of a wooden decking in the old mint of Segovia (Spain)

the aim of this study is to apply an integrated methodological approximation where dendrochronology and documentary analysis allow us to reconstruct the historical flood record of the Segovia Mint. Our hypothesis is that differences between the dendrochronological data of the wooden decking pieces can be related to historical floods and, therefore, they could be used as proxy-source data in future palaeoflood research.

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

Multi-Camera very wide baseline feature matching based on view-adaptive junction detection

This paper presents a strategy for solving the feature matching problem in calibrated very wide-baseline camera settings. In this kind of settings, perspective distortion, depth discontinuities and occlusion represent enormous challenges. The proposed strategy addresses them by using geometrical information, specifically by exploiting epipolar-constraints. As a result it provides a sparse number of reliable feature points for which 3D position is accurately recovered. Special features known as junctions are used for robust matching. In particular, a strategy for refinement of junction end-point matching is proposed which enhances usual junction-based approaches. This allows to compute cross-correlation between perfectly aligned plane patches in both images, thus yielding better matching results. Evaluation of experimental results proves the effectiveness of the proposed algorithm in very wide-baseline environments.

Reality bites: Documentary photography and architectural realism = Fragmentos de realidad: Fotografía documental y realismo en arquitectura

En el día de hoy nos enfrentamos a una serie de desafíos económicos, geopolíticos y ambientales que apuntan hacia una transformación profunda del mundo tal y como lo conocemos. La arquitectura esta – siempre lo ha estado – imbricada en todos estos problemas. En nuestra actual condición de recursos limitados e injusticia global surge la necesidad de superar la dicotomía entre progreso y tradición, entre innovación y preservación – la urgencia de redefinir incluso cada uno de estos términos. Los tipos de crisis que encaramos ahora no se resolverán con mas tecnología y más crecimiento – se necesita también una profunda reevaluación de nuestros sistemas de valores e incluso de nuestra sensibilidad. En este contexto, la humilde contribución de esta tesis consiste en proponer la idea de un realismo en arquitectura, entendido como una actitud progresista y atenta a lo que ya existe, dispuesta a abrir los ojos al presente, a las condiciones y los problemas reales que nos rodean. Pensando en esta idea, resulta evidente que si bien el realismo carece de una definición clara en el campo de la arquitectura hoy, cuenta sin embargo con numerosos precedentes parciales, entre ellos algunos de los más notorios episodios de la historia de la arquitectura reciente. Al intentar confeccionar una lista provisional de arquitectos realistas en el último siglo, otro hecho se nos presenta: muchos, por no decir todos estos arquitectos, comparten una fijación por la fotografía, bien apropiándose del trabajo de fotógrafos contemporáneos, bien tomando sus propias imágenes como una forma de mirar fuera de sus estudios e incorporar la realidad del medio construido a sus proyectos. Parece entonces lógico pensar que la arquitectura, como disciplina visual, ha acudido a otra disciplina visual en búsqueda de su propia respuesta al problema del realismo – dentro de este campo la fotografía reverbera con la arquitectura especialmente, ya que ambas son consideradas simultáneamente como practicas utilitarias y como parte de las bellas artes. Parece entonces lógico el organizar la investigación como una serie de casos, con la esperanza de que la acumulación de diversas instancias en las que la arquitectura ha acudido a la fotografía en su búsqueda de un realismo arrojara luz sobre las ideas más generales a debate. Por tanto, cada uno de los episodios en este texto se examina en sus propios términos, si bien una serie de interconexiones emergen a medida que el estudio procede con un suave orden cronológico. Hacia el final del texto cuestiones más grandes recobran protagonismo, a medida que las repercusiones políticas de nuestro estudio se hacen más patentes y comenzamos a interrogar las implicaciones contemporáneas y el potencial futuro de nuestra discusión sobre el realismo. ABSTRACT Today we are faced with a series of economic, geopolitical and environmental challenges that outline a deep transformation of the world as we know it. Architecture is – it has always been – ingrained in all of these problems. In our current condition of limited resources and global inequalities there is a necessity to overcome the dichotomy between progress and tradition, between innovation and preservation – an urgency to even redefine these terms altogether. The types of crises that we are facing will not be solved with more technology and more growth – a deep reevaluation of our systems of values and our sensibilities are also needed. In this context, the humble contribution of this text is to put forward the idea of an architectural realism, understood as an attitude that is both progressive and attentive to what is already in place, willing to open its eyes to the present and accept the real conditions and problems around us. In thinking about this prospect, it immediately becomes apparent that even if realism lacks a clear definition in the field of architecture today, there are numerous partial precedents for it, among them some of the most notorious episodes in the recent history of architecture. In crafting a tentative list of realist architects in the last century, another realization takes place: many, not to say all of these architects, have had a fixation with photography, either appropriating the work of contemporary photographers or taking photographs themselves as a way to look out their windows and bring the reality of the built environment into their practices. It seems then logical to think that architecture, as a visual discipline, has looked to other visual disciplines in search for its own take on the problem of realism – within this field, photography specially resonates with architecture, since both are regarded simultaneously as utilitarian practices and belonging to the fine arts. The idea then becomes to organize the research as a series of cases, with the hope that the accumulation of diverse instances in which architecture has approached photography in its realist drive will shed some light on the larger ideas at stake. Therefore, each of the episodes in this text is examined on its own terms, with a series of interconnections slowly emerging as our survey proceeds with a soft chronologic order. Towards the end of the study, larger issues regain relevance as the political repercussions of our inquiry become more pressing and we start to question the contemporary implications and future potentials of our discussion on realism.

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.