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Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as thermostatically controlled loads (TCLs) can arbitrage energy prices and provide reserves due to their thermal energy storage capacity. The quantity of reserves depends not only on the aggregate power capacity, but also on information and communication technology, exogenous parameters, and system operator requirements. Specifically, the practical limitations origin from (i) communication constraints, (ii) ambient temperature, and (iii) the dispatch time of the activation signal. This paper explores the impact of these parameters on the amount of reserves that an aggregator of TCLs can provide to the system operator based on centralized control of a TCL population. We propose a decision support tool that can be used by aggregators to decide on maximum dispatchable reserve bids. The method can accommodate the specific control algorithm and TCL population of an aggregator and is based on offline computation. It constitutes a powerful reserve bid library to be used when optimization tools become computationally intractable due to the increased number of decentralized flexible loads., QC 20201216

This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data., QC 20140423

This paper contributes a real time method for recovering facial shape and expression from a single depth image. The method also estimates an accurate and dense correspondence field between the input depth image and a generic face model. Both outputs are a result of minimizing the error in reconstructing the depth image, achieved by applying a set of identity and expression blend shapes to the model. Traditionally, such a generative approach has shown to be computationally expensive and non-robust because of the non-linear nature of the reconstruction error. To overcome this problem, we use a discriminatively trained prediction pipeline that employs random forests to generate an initial dense but noisy correspondence field. Our method then exploits a fast ICP-like approximation to update these correspondences, allowing us to quickly obtain a robust initial fit of our model. The model parameters are then fine tuned to minimize the true reconstruction error using a stochastic optimization technique. The correspondence field resulting from our hybrid generative-discriminative pipeline is accurate and useful for a variety of applications such as mesh deformation and retexturing. Our method works in real-time on a single depth image i.e. without temporal tracking, is free from per-user calibration, and works in low-light conditions., QC 20140911

Projecte realitzat en el marc d’un programa de mobilitat amb la KTH Royal Institute of Technology, [ANGLÈS] Face recognition is a fundamental task in computer vision and has been an important field of study for many years. This report presents a unified process for automatically extract a set of face landmarks and remove all differences related to pose, expression and environment by bringing faces to a neutral pose-centered state. Landmark detection is based on a multiple viewpoint Pictorial Structure model. In this project we address both the problem of how to find a set of landmarks from a model and the problem of training such a model from a set of labelled examples. We show how such a model successfully captures a great range of deformations needing far less training examples than common commercial face detectors. The alignment process basically aims to remove differences between multiple faces so they all can be analysed under the same criteria. It is carried out with Thin-plate Splines to adjust the detected set of landmarks to the desired configuration. We present results of our algorithms both in a constrained environment and in the challenging LFPW face database. Successful outcomes are shown that prove our method to be a solid process for unitedly recognise and warp faces in the wild and to be on a par with other state-of-the-art procedures., [CASTELLÀ] El reconocimiento y clasificación de caras representa uno de los campos de estudio más importantes dentro la visión artificial por computadora y el procesado de imagen. En este proyecto se presenta un proceso unificado que extrae automáticamente, a partir de imágenes, un conjunto de puntos faciales característicos, eliminando al mismo tiempo las diferencias relacionadas con la orientación, expresión e iluminación. En una primera fase, se utiliza un modelo de Estructuras Pictográficas Multivista para la detección de puntos característicos. En este informe, se atienden con detalle tanto el problema de extraer puntos con este modelo, como el proceso de aprendizaje a partir de imágenes anotadas. En una segunda fase, se eliminan las diferencias entre caras mediante el uso de Thin-Plate Splines, para que todas las imágenes puedan ser analizadas bajo el mismo criterio en posibles aplicaciones futuras. Los algoritmos implementados, representan hasta la fecha, el primer sistema desarrollado, que simultáneamente extrae y alinea caras, y demuestran un comportamiento muy fiable y preciso tanto en entornos controlados como libres (base de datos LFPW)., [CATALÀ] reconeixement i classificació de cares és un dels camps d'estudi més importants dintre la visió artificial per computadora i el processament d'imatge. En aquest projecte es presenta un procés unificat que extreu automàticament, a partir d'imatges, un conjunt de punts facials característics eliminant al mateix temps les diferències relacionades amb l'orientació, expressió i il·luminació. En una primera fase, s'utilitza un model d'Estructures Pictogràfiques Multivista per a la detecció de punts característics. En aquest informe, s'adreça amb detall tant el problema d'extreure punts amb aquest model com el procés d'aprenentatge a partir d'imatges anotades. En una segona fase, s'eliminen les diferències entre cares mitjançant Thin-Plate Splines, per tal que totes les imatges puguin ser analitzades sota el mateix criteri en possibles aplicacions futures. Els algoritmes implementats, representen fins al moment, el primer sistema desenvolupat que unificadament extreu i alinea cares, i demostren un comportament molt fiable i acurat tant en entorns controlats com lliures (base de dades LFPW).

This paper addresses the problem of human pose estimation, given images taken from multiple dynamic but calibrated cameras. We consider solving this task using a part-based model and focus on the part appearance component of such a model. We use a random forest classifier to capture the variation in appearance of body parts in 2D images. The result of these 2D part detectors are then aggregated across views to produce consistent 3D hypotheses for parts. We solve correspondences across views for mirror symmetric parts by introducing a latent variable. We evaluate our part detectors qualitatively and quantitatively on a dataset gathered from a professional football game., QC 20131217

We present a fully automatic procedure for reconstructing the pose of a person in 3Dfrom images taken from multiple views. We demonstrate a novel approach for learningmore complex models using SVM-Rank, to reorder a set of high scoring configurations.The new model in many cases can resolve the problem of double counting of limbswhich happens often in the pictorial structure based models. We address the problemof flipping ambiguity to find the correct correspondences of 2D predictions across allviews. We obtain improvements for 2D prediction over the state of art methods on ourdataset. We show that the results in many cases are good enough for a fully automatic3D reconstruction with uncalibrated cameras., QC 20121114

We propose a new method for face alignment with part-based modeling. This method is competitive in terms of precision with existing methods such as Active Appearance Models, but is more robust and has a superior generalization ability due to its part-based nature. A variation of the Histogram of Oriented Gradients descriptor is used to model the appearance of each part and the shape information is represented with a set of landmark points around the major facial features. Multiple linear regression models are learnt to estimate the position of the landmarks from the appearance of each part. We verify our algorithm with a set of experiments on human faces and these show the competitive performance of our method compared to existing methods., QC 20111116