37 results on '"Torre, Fernando De La"'
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2. Special Issue Editorial: Plant Nitrogen Assimilation and Metabolism
- Author
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Torre, Fernando de la, primary and Ávila, Concepción, additional
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- 2021
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3. Multibiomarker responses in Danio rerio after exposure to sediment spiked with triclosan
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SAGER, EMANUEL, primary, ROSSI, ANDREA, additional, LOUGHLIN, TOMÁS MAC, additional, MARINO, DAMIÁN, additional, and TORRE, FERNANDO DE LA, additional
- Published
- 2021
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4. Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database
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Torre, Fernando De La, Hodgins, Jessica, Bargteil, Adam, Martin, Xavier, Macey, Justin, Collado, Alex, and Pep Beltran
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FOS: Computer and information sciences ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,80101 Adaptive Agents and Intelligent Robotics - Abstract
This document summarizes the technology, procedures, and database organization of the CMU Multi-Modal Activity Database (CMU-MMAC). The CMU-MMAC database contains multimodal measures of the human activity of subjects performing the tasks involved in cooking and food preparation. The CMU-MMAC database was collected in Carnegie Mellon University’s Motion Capture Lab. A kitchen was built and to date five subjects have been recorded cooking five different recipes: brownies, pizza, sandwich, salad and scrambled eggs. The following modalities were recorded: • Video: (1) Three high spatial resolution (1024 × 768) color video cameras at low temporal resolution (30 Hertz). (2) Two low spatial resolution (640 × 480) color video cameras at high temporal resolution (60 Hertz). (3) One wearable low spatial resolution (640×480) camera at low temporal resolution (12 Hertz). • Audio: (1) Five balanced microphones. (2) Wearable watch. • Motion capture: A Vicon motion capture system with 12 infrared MX-40 cameras. Each camera records images of 4 megapixel resolution at 120 Hertz. • Five 3-axis accelerometers and gyroscopes. Several computers were used for recording the various modalities. The computers were synchronized using the Network Time Protocol (NTP).
- Published
- 2018
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5. Robust Kernel Principal Component Analysis
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Nguyen, Minh Hoai and Torre, Fernando De La
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to higher (usually) dimensional feature space where the data can be linearly modeled. The feature space is typically induced implicitly by a kernel function, and linear PCA in the feature space is performed via the kernel trick. However, due to the implicitness of the feature space, some extensions of PCA such as robust PCA cannot be directly generalized to KPCA. This paper presents a technique to overcome this problem, and extends it to a unified framework for treating noise, missing data, and outliers in KPCA. Our method is based on a novel cost function to perform inference in KPCA. Extensive experiments, in both synthetic and real data, show that our algorithm outperforms existing methods.
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- 2018
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6. Filtered Component Analysis to Increase Robustness to Locla Minima in Appearance Models
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Torre, Fernando De La, Collet, Alvaro, Quero, Manuel, Cohn, Jeffery F., and Kanade, Takeo
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Appearance Models (AM) are commonly used to model appearance and shape variation of objects in images. In particular, they have proven useful to detection, tracking, and synthesis of people’s faces from video. While AM have numerous advantages relative to alternative approaches, they have at least two important drawbacks. First, they are especially prone to local minima in fitting; this problem becomes increasingly problematic as the number of parameters to estimate grows. Second, often few if any of the local minima correspond to the correct location of the model error. To address these problems, we propose Filtered Component Analysis (FCA), an extension of traditional Principal Component Analysis (PCA). FCA learns an optimal set of filters with which to build a multi-band representation of the object. FCA representations were found to be more robust than either grayscale or Gabor filters to problems of local minima. The effectiveness and robustness of the proposed algorithm is demonstrated in both synthetic and real data.
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- 2018
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7. Omnidirectional Video Capturing, Multiple People Tracking and Identification for Meeting Monitoring
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Torre, Fernando De La, Vallespi, Carlos, Rybski, Paul E., Veloso, Manuela, and Kanade, Takeo
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Meetings are a very important part of every days life for professionals working in universities, companies or governmental institutions. In fact, it is estimated that a midlevel manager or professional spends around 35% of his/her time in meetings. We have designed a physical awareness system called CAMEO (Camera Assisted Meeting Event Observer), a hardware/software component to record and monitor people’s activities in meetings. CAMEO captures the audio and a high resolution omnidirectional view of the meeting by stitching images coming from almost concentric cameras. Besides recording capabilities, CAMEO automatically detects people and automatically learns a personspecific facial appearance model (PSFAM) for each of the participants. The PSFAMs allow more robust, reliable and faster tracking and identification. Several novelties in the video capturing device, multiple person identification and tracking are proposed. The effectiveness and robustness of the proposed system is demonstrated over several real time experiments and a large data set of videos.
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- 2018
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8. A Tomato Universal Stress Protein Involved in Oxidative Stress is a Cipk6 Phosphorylation Target
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Gutiérrez-Beltrán, Emilio, Personat, José María, Torre, Fernando de la, and Pozo, Olga del
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fungi ,food and beverages - Abstract
Calcineurin B-like interacting protein kinases (CIPKs) decode calcium signals upon interaction with the calcium sensors calcineurin B like proteins into phosphorylation events that result into adaptation to environmental stresses. Few phosphorylation targets of CIPKs are known and therefore the molecular mechanisms underlying their downstream output responses are not fully understood. Tomato (Solanum lycopersicum) Cipk6 regulates immune and susceptible Programmed cell death in immunity transforming Ca signals into reactive oxygen species (ROS) signaling. To investigate SlCipk6-induced molecular mechanisms and identify putative substrates, a yeast two-hybrid approach was carried on and a protein was identified that contained a Universal stress protein (Usp) domain present in bacteria, protozoa and plants, which we named “SlRd2”. SlRd2 was an ATP-binding protein that formed homodimers in planta. SlCipk6 and SlRd2 interacted using coimmunoprecipitation and bimolecular fluorescence complementation (BiFC) assays in Nicotiana benthamiana leaves and the complex localized in the cytosol. SlCipk6 phosphorylated SlRd2 in vitro, thus defining, to our knowledge, a novel target for CIPKs. Heterologous SlRd2 overexpression in yeast conferred resistance to highly toxic LiCl, whereas SlRd2 expression in Escherichia coli UspA mutant restored bacterial viability in response to HO treatment. Finally, transient expression of SlCipk6 in transgenic N. benthamiana SlRd2 overexpressors resulted in reduced ROS accumulation as compared to wild-type plants. Taken together, our results establish that SlRd2, a tomato UspA, is, to our knowledge, a novel interactor and phosphorylation target of a member of the CIPK family, SlCipk6, and functionally regulates SlCipk6-mediated ROS generation.
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- 2017
9. ALCARRIA INFINITA.
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TORRE, FERNANDO DE LA
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- 2020
10. Error-Correcting Factorization.
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Bautista Martin, Miguel Angel, Pujol, Oriol, Torre, Fernando De la, and Escalera, Sergio
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ERROR-correcting codes ,FACTORIZATION ,PATTERN perception ,MACHINE learning ,DATABASES - Abstract
Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method. Our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
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- 2018
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11. A Functional Regression Approach to Facial Landmark Tracking.
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Sanchez-Lozano, Enrique, Tzimiropoulos, Georgios, Martinez, Brais, Torre, Fernando De la, and Valstar, Michel
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MULTIPLE correspondence analysis (Statistics) ,CORRESPONDENCE analysis (Statistics) ,ANALYSIS of variance ,RANDOM variables ,DECISION making - Abstract
Linear regression is a fundamental building block in many face detection and tracking algorithms, typically used to predict shape displacements from image features through a linear mapping. This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker. Contrary to prior work in Functional Regression, in which B-splines or Fourier series were used, we propose to approximate the input space by its first-order Taylor expansion, yielding a closed-form solution for the continuous domain of displacements. We then extend the continuous least squares problem to correlated variables, and demonstrate the generalisation of our approach. We incorporate Continuous Regression into the cascaded regression framework, and show its computational benefits for both training and testing. We then present a fast approach for incremental learning within Cascaded Continuous Regression, coined iCCR, and show that its complexity allows real-time face tracking, being 20 times faster than the state of the art. To the best of our knowledge, this is the first incremental face tracker that is shown to operate in real-time. We show that iCCR achieves state-of-the-art performance on the 300-VW dataset, the most recent, large-scale benchmark for face tracking. [ABSTRACT FROM AUTHOR]
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- 2018
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12. Multimodal feature analysis for quantitative performance evaluation of endotracheal intubation (ETI)
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Samarjit Das, Jestin N. Carlson, Torre, Fernando De La, and Hodgins, Jessica K
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Endotracheal intubation (ETI) is a very crucial medical procedure performed on critically ill patients. It involves insertion of a breathing tube into the trachea i.e. the windpipe connecting the larynx and the lungs. Often, this procedure is performed by the paramedics (aka providers) under challenging prehospital settings e.g. roadside, ambulances or helicopters. Successful intubations could be lifesaving, whereas, failed intubation could potentially be fatal. Under prehospital environments, ETI success rates among the paramedics are surprisingly low and this necessitates better training and performance evaluation of ETI skills. Currently, few objective metrics exist to quantify the differences in ETI techniques between providers of various skill levels. In this pilot study, we develop a quantitative framework for discriminating the kinematic characteristics of providers with different experience levels. The system utilizes statistical analysis on spatio-temporal multimodal features extracted from optical motion capture, accelerometers and electromyography (EMG) sensors. Our experiments involved three individuals performing intubations on a dummy, each with different levels of expertise. Quantitative performance analysis on multimodal features revealed distinctive differences among different skill levels. In future work, the feedback from these analysis could potentially be harnessed towards enhancing ETI training.
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- 2012
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13. A least-squares framework for Component Analysis
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Torre, Fernando De La
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Over the last century, Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Locality Preserving Projections (LPP), and Spectral Clustering (SC) have been extensively used as a feature extraction step for modeling, classification, visualization, and clustering. CA techniques are appealing because many can be formulated as eigen-problems, offering great potential for learning linear and nonlinear representations of data in closed-form. However, the eigen-formulation often conceals important analytic and computational drawbacks of CA techniques, such as solving generalized eigen-problems with rank deficient matrices (e.g., small sample size problem), lacking intuitive interpretation of normalization factors, and understanding commonalities and differences between CA methods. This paper proposes a unified least-squares framework to formulate many CA methods. We show how PCA, LDA, CCA, LPP, SC, and its kernel and regularized extensions correspond to a particular instance of least-squares weighted kernel reduced rank regression (LS--WKRRR). The LS-WKRRR formulation of CA methods has several benefits: 1) provides a clean connection between many CA techniques and an intuitive framework to understand normalization factors; 2) yields efficient numerical schemes to solve CA techniques; 3) overcomes the small sample size problem; 4) provides a framework to easily extend CA methods. We derive weighted generalizations of PCA, LDA, SC, and CCA, and several new CA techniques.
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- 2012
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14. Learning Image Alignment without Local Minima for Face Detection and Tracking
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Nguyen, Minh Hoai and Torre, Fernando De La
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Active Appearance Models (AAMs) have been extensively used for face alignment during the last 20 years. While AAMs have numerous advantages relative to alternate approaches, they suffer from two major drawbacks: (i) AAMs are especially prone to local minima in the fitting process; (ii) few if any of the local minima of the cost function correspond to acceptable solutions. To minimize these problems, this paper proposes a method to learn the fitting cost function that explicitly optimizes that the local minima occur at and only at the places corresponding to the correct fitting parameters. The paper explores two methods to parameterize the cost function: pixel weighting and subspace learning. Experiments on synthetic and real data show the effectiveness of our approach for face alignment.
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- 2008
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15. Parameterized Kernel Principal Component Analysis: Theory and Applications to Supervised and Unsupervised Image Alignment
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Torre, Fernando De La and Nguyen, Minh Hoai
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FOS: Computer and information sciences ,ComputingMethodologies_PATTERNRECOGNITION ,Computer Science::Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of objects in images. Given a new image with an unknown appearance/shape configuration, PAMs can detect and track the object by optimizing the model’s parameters that best match the image. While PAMs have numerous advantages for image registration relative to alternative approaches, they suffer from two major limitations: First, PCA cannot model non-linear structure in the data. Second, learning PAMs requires precise manually labeled training data. This paper proposes Parameterized Kernel Principal Component Analysis (PKPCA), an extension of PAMs that uses Kernel PCA (KPCA) for learning a non-linear appearance model invariant to rigid and/or non-rigid deformations. We demonstrate improved performance in supervised and unsupervised image registration, and present a novel application to improve the quality of manual landmarks in faces. In addition, we suggest a clean and effective matrix formulation for PKPCA.
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- 2008
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16. Local Minima Free Parameterized Appearance Models
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Nguyen, Minh Hoai and Torre, Fernando De La
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FOS: Computer and information sciences ,ComputingMethodologies_PATTERNRECOGNITION ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Parameterized Appearance Models (PAMs) (e.g. Eigen-tracking, Active Appearance Models, Morphable Models) are commonly used to model the appearance and shapevariation of objects in images. While PAMs have numerous advantages relative to alternate approaches, they have at least two drawbacks. First, they are especially prone to local minima in the fitting process. Second, often few if any of the local minima of the cost function correspond to acceptable solutions. To solve these problems, this paper proposes a method to learn a cost function by explicitly optimizing that the local minima occur at and only at the places corresponding to the correct fitting parameters. To the best of our knowledge, this is the first paper to address the problem of learning a cost function to explicitly model local properties of the error surface to fit PAMs. Synthetic and real examples show improvement in alignment performance in comparison with traditional approaches.
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- 2008
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17. Motion from Structure (MfS): Searching for 3D Objects in Cluttered Point Trajectories.
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Vongkulbhisal, Jayakorn, Cabral, Ricardo, Torre, Fernando De la, and Costeira, Joao P.
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- 2016
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18. Laerning Kernel Expansions for Image Classification
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Torre, Fernando De La and Vinyals, Oriol
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Kernel machines (e.g. SVM, KLDA) have shown state-ofthe- art performance in several visual classification tasks. The classification performance of kernel machines greatly depends on the choice of kernels and its parameters. In this paper, we propose a method to search over a space of parameterized kernels using a gradient-descent based method. Our method effectively learns a non-linear representation of the data useful for classification and simultaneously performs dimensionality reduction. In addition, we suggest a new matrix formulation that simplifies and unifies previous approaches. The effectiveness and robustness of the proposed algorithm is demonstrated in both synthetic and real examples of pedestrian and mouth detection in images.
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- 2007
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19. Selective Transfer Machine for Personalized Facial Expression Analysis.
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Chu, Wen-Sheng, Torre, Fernando De La, and Cohn, Jeffrey F.
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FACIAL expression , *WEB personalization , *SUPPORT vector machines , *GENERIC programming (Computer science) , *MACHINE learning - Abstract
Automatic facial action unit (AU) and expression detection from videos is a long-standing problem. The problem is challenging in part because classifiers must generalize to previously unknown subjects that differ markedly in behavior and facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) from those on which the classifiers are trained. While some progress has been achieved through improvements in choices of features and classifiers, the challenge occasioned by individual differences among people remains. Person-specific classifiers would be a possible solution but for a paucity of training data. Sufficient training data for person-specific classifiers typically is unavailable. This paper addresses the problem of how to personalize a generic classifier without additional labels from the test subject. We propose a transductive learning method, which we refer to as a Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific mismatches. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. We compared STM to both generic classifiers and cross-domain learning methods on four benchmarks: CK+
[44] , GEMEP-FERA[67] , RU-FACS[4] and GFT[57] . STM outperformed generic classifiers in all. [ABSTRACT FROM PUBLISHER]- Published
- 2017
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20. Multiple Face Recognition from Omnidirectional Video
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Torre, Fernando De La, Vallespi, Carlos, Rybski, Paul, Veloso, Manuela, and Kanade, Takeo
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Meetings are an integral part of business life. In previous work, we have developed a physical awareness system called CAMEO (Camera Assisted Meeting Event Observer) to record and process audio/visual information of a meeting. A very important task in meeting understanding is to know who is attending to the meeting and CAMEO's task is to infer people's identity from video. In this paper, we present an approach to identify people from an omnidirectional video sequence. Two main novelties are proposed: First a new dimensionality reduction technique MODA (Multimodal Oriented Discriminant Analysis) is used to perform fast matching and second we show that using multiple spatiotemporal constraints the recognition performance greatly improves. The effectiveness and robustness of the proposed system is demonstrated over several real time experiments and a large data set of videos.
- Published
- 2005
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21. CAMEO: The Camera Assisted Meeting Event Observer
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Rybski, Paul E., Torre, Fernando De La, Patil, Raju, Vallespi, Carlos, Veloso, Manuela M., and Browning, Brett
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FOS: Computer and information sciences ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Static cameras are pervasive in a variety of environments. However it remains a challenging problem to extract and reason about high-level features from real-time and continuous observation of an environment. In this paper, we present CAMEO, the Camera Assisted Meeting Event Observer, which is a physical awareness system designed for use by an agent-based electronic assistant. CAMEO is an inexpensive high-resolution omnidirectional vision system designed to be used in meeting environments. The multiple camera design achieves the desired high image resolution and lower cost that can be achieved when compared to traditional omnicameras that make use of a single camera and mirror solution.
- Published
- 2004
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22. Oriented Discriminant Analysis (ODA)
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Torre, Fernando De La and Kanade, Takeo
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FOS: Computer and information sciences ,80101 Adaptive Agents and Intelligent Robotics - Abstract
Linear discriminant analysis (LDA) has been an active topic of research during the last century. However, the existing algorithms have several limitations when applied to visual data. LDA is only optimal for gaussian distributed classes with equal covariance matrices and just classes-1 features can be extracted. On the other hand, LDA does not scale well to high dimensional data (over-fitting) and it does not necessarily minimize the classification error. In this paper, we introduce Oriented Discriminant Analysis (ODA), a LDA extension which can overcome these drawbacks. Three main novelties are proposed: • An optimal dimensionality reduction which maximizes the Kullback- Liebler divergence between classes is proposed. This allows us to model class covariances and to extract more than classes-1 features. • Several covariance approximations are introduced to improve classification in the small sample case. • A linear time iterative majorization method is introduced in order to find a local optimal solution. Several synthetic and real experiments on face recognition are reported.
- Published
- 2004
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23. Segmentation and Classification of Meetings Using Multiple Information Streams
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Rybski, Paul E., Satanjeev Banerjee, Torre, Fernando De La, Vallespi, Carlos, Rudnicky, Alexander, and Veloso, Manuela M.
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FOS: Computer and information sciences ,89999 Information and Computing Sciences not elsewhere classified - Abstract
We present a meeting recorder infrastructure used to record and annotate events that occur in meetings. Multiple data streams are recorded and analyzed in order to infer a higher-level state of the group’s activities. We describe the hardware and software systems used to capture people’s activities as well as the methods used to characterize them.
- Published
- 2003
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24. Confidence Preserving Machine for Facial Action Unit Detection.
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Zeng, Jiabei, Chu, Wen-Sheng, Torre, Fernando De la, Cohn, Jeffrey F., and Xiong, Zhang
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- 2015
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25. Unsupervised Synchrony Discovery in Human Interaction.
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Chu, Wen-Sheng, Zeng, Jiabei, Torre, Fernando De la, Cohn, Jeffrey F., and Messinger, Daniel S.
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- 2015
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26. Semantic Component Analysis.
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Murdock, Calvin and Torre, Fernando De la
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- 2015
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27. Spatio-Temporal Matching for Human Pose Estimation in Video.
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Zhou, Feng and Torre, Fernando De la
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DETECTORS , *COMPUTER vision , *SPATIOTEMPORAL processes , *MOTION capture (Human mechanics) , *TRAJECTORIES (Mechanics) - Abstract
Detection and tracking humans in videos have been long-standing problems in computer vision. Most successful approaches (e.g., deformable parts models) heavily rely on discriminative models to build appearance detectors for body joints and generative models to constrain possible body configurations (e.g., trees). While these $2$
D models have been successfully applied to images (and with less success to videos), a major challenge is to generalize these models to cope with camera views. In order to achieve view-invariance, these $2$ D motion capture model and trajectories in videos. Our algorithm estimates the camera view and selects a subset of tracked trajectories that matches the motion of the $3$ D model. The STM is efficiently solved with linear programming, and it is robust to tracking mismatches, occlusions and outliers. To the best of our knowledge this is the first paper that solves the correspondence between video and $3$- Published
- 2016
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28. Generalized Canonical Time Warping.
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Zhou, Feng and Torre, Fernando De la
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ARTIFICIAL intelligence , *HUMAN mechanics , *MOTION capture (Human mechanics) , *MOTION capture (Cinematography) , *ANIMATION (Cinematography) , *COMPUTER graphics - Abstract
Temporal alignment of human motion has been of recent interest due to its applications in animation, tele-rehabilitation and activity recognition. This paper presents generalized canonical time warping (GCTW), an extension of dynamic time warping (DTW) and canonical correlation analysis (CCA) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GCTW extends previous work on DTW and CCA in several ways: (1) it combines CCA with DTW to align multi-modal data (e.g., video and motion capture data); (2) it extends DTW by using a linear combination of monotonic functions to represent the warping path, providing a more flexible temporal warp. Unlike exact DTW, which has quadratic complexity, we propose a linear time algorithm to minimize GCTW. (3) GCTW allows simultaneous alignment of multiple sequences. Experimental results on aligning multi-modal data, facial expressions, motion capture data and video illustrate the benefits of GCTW. The code is available at
http://humansensing.cs.cmu.edu/ctw . [ABSTRACT FROM PUBLISHER]- Published
- 2016
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29. Complex Non-rigid Motion 3D Reconstruction by Union of Subspaces.
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Zhu, Yingying, Huang, Dong, Torre, Fernando De La, and Lucey, Simon
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- 2014
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30. Matrix Completion for Weakly-Supervised Multi-Label Image Classification.
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Cabral, Ricardo, Torre, Fernando De la, Costeira, Joao Paulo, and Bernardino, Alexandre
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MATRICES (Mathematics) , *ART students , *ART publishing , *ACOUSTIC transients , *DATA analysis , *MATHEMATICAL programming - Abstract
In the last few years, image classification has become an incredibly active research topic, with widespread applications. Most methods for visual recognition are fully supervised, as they make use of bounding boxes or pixelwise segmentations to locate objects of interest. However, this type of manual labeling is time consuming, error prone and it has been shown that manual segmentations are not necessarily the optimal spatial enclosure for object classifiers. This paper proposes a weakly-supervised system for multi-label image classification. In this setting, training images are annotated with a set of keywords describing their contents, but the visual concepts are not explicitly segmented in the images. We formulate the weakly-supervised image classification as a low-rank matrix completion problem. Compared to previous work, our proposed framework has three advantages: (1) Unlike existing solutions based on multiple-instance learning methods, our model is convex. We propose two alternative algorithms for matrix completion specifically tailored to visual data, and prove their convergence. (2) Unlike existing discriminative methods, our algorithm is robust to labeling errors, background noise and partial occlusions. (3) Our method can potentially be used for semantic segmentation. Experimental validation on several data sets shows that our method outperforms state-of-the-art classification algorithms, while effectively capturing each class appearance. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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31. Selective Transfer Machine for Personalized Facial Action Unit Detection.
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Chu, Wen-Sheng, Torre, Fernando De La, and Cohn, Jeffery F.
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- 2013
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32. Insulin allergy and resistance successfully treated by desensitisation with Aspart insulin
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Matheu, Victor, primary, Perez, Eva, additional, Hernández, Marta, additional, Díaz, Elisa, additional, Darias, Ricardo, additional, González, Abel, additional, García, Jose C, additional, Sánchez, Inmaculada, additional, Feliciano, Laura, additional, Caballero, Agueda, additional, and Torre, Fernando de la, additional
- Published
- 2005
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33. Detecting Parkinsons' symptoms in uncontrolled home environments: A multiple instance learning approach.
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Das, Samarjit, Amoedo, Breogan, Torre, Fernando De la, and Hodgins, Jessica
- Abstract
In this paper, we propose to use a weakly supervised machine learning framework for automatic detection of Parkinson's Disease motor symptoms in daily living environments. Our primary goal is to develop a monitoring system capable of being used outside of controlled laboratory settings. Such a system would enable us to track medication cycles at home and provide valuable clinical feedback. Most of the relevant prior works involve supervised learning frameworks (e.g., Support Vector Machines). However, in-home monitoring provides only coarse ground truth information about symptom occurrences, making it very hard to adapt and train supervised learning classifiers for symptom detection. We address this challenge by formulating symptom detection under incomplete ground truth information as a multiple instance learning (MIL) problem. MIL is a weakly supervised learning framework that does not require exact instances of symptom occurrences for training; rather, it learns from approximate time intervals within which a symptom might or might not have occurred on a given day. Once trained, the MIL detector was able to spot symptom-prone time windows on other days and approximately localize the symptom instances. We monitored two Parkinson's disease (PD) patients, each for four days with a set of five triaxial accelerometers and utilized a MIL algorithm based on axis parallel rectangle (APR) fitting in the feature space. We were able to detect subject specific symptoms (e.g. dyskinesia) that conformed with a daily log maintained by the patients. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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34. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.
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Zhou, Feng, Torre, Fernando De la, and Hodgins, Jessica K.
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HUMAN mechanics , *CLUSTER analysis (Statistics) , *KERNEL functions , *TIME series analysis , *DYNAMIC programming - Abstract
Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k--means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
35. Identification and functional analysis of a prokaryotic-type aspartate aminotransferase: implications for plant amino acid metabolism.
- Author
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Torre, Fernando de la, Santis, Laura De, Suárez, María Fernanda, Crespillo, Remedios, and Cánovas, Francisco M.
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ASPARTATE aminotransferase , *ASPARTIC acid , *AMINO acid metabolism , *PROKARYOTES , *PLANT enzymes , *FUNCTIONAL analysis - Abstract
In this paper, we report the identification of genes from pine ( PpAAT), Arabidopsis ( AtAAT) and rice ( OsAAT) encoding a novel class of aspartate aminotransferase (AAT, EC 2.6.1.1) in plants. The enzyme is unrelated to other eukaryotic AATs from plants and animals but similar to bacterial enzymes. Phylogenetic analysis indicates that this prokaryotic-type AAT is closely related to cyanobacterial enzymes, suggesting it might have an endosymbiotic origin. Interestingly, most of the essential residues involved in the interaction with the substrate and the attachment of pyridoxal phosphate cofactor in the active site of the enzyme were conserved in the deduced polypeptide. The polypeptide is processed in planta to a mature subunit of 45 kDa that is immunologically distinct from the cytosolic, mitochondrial and chloroplastic isoforms of AAT previously characterized in plants. Functional expression of PpAAT sequences in Escherichia coli showed that the processed precursor is assembled into a catalytically active homodimeric holoenzyme that is strictly specific for aspartate. These atypical genes are predominantly expressed in green tissues of pine, Arabidopsis and rice, suggesting a key role of this AAT in nitrogen metabolism associated with photosynthetic activity. Moreover, immunological analyses revealed that the plant prokaryotic-type AAT is a nuclear-encoded chloroplast protein. This implies that two plastidic AAT co-exist in plants: a eukaryotic type previously characterized and the prokaryotic type described here. The respective roles of these two enzymes in plant amino acid metabolism are discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
36. San Pedro de Arbues
- Author
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Torre, Fernando de la
- Abstract
A 250/101(11)
- Published
- 1665
37. EgoSign: A First-Person View Dataset for Sign Language
- Author
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Puntí Álvarez, Cristina, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Giró Nieto, Xavier, and Torre, Fernando de la
- Subjects
Conjunts de dades ,sign language ,dataset ,Data sets ,Enginyeria de la telecomunicació::Processament del senyal [Àrees temàtiques de la UPC] ,Llenguatge de signes - Abstract
Wearable headsets for interacting in virtual worlds are becoming more and more popular as new interaction platforms, such as Metaverse, are rising. An opportunity to overcome communication gaps between the Deaf and Hearing communities is presented. Sign language recognition, translation and production are challenging problems difficult to assess, among other reasons, due to the absence of good datasets. Towards this end, in 2021 How2Sign was published, a multimodal and multiview continuous American Sign Language dataset. In this project we introduce, EgoSign, an egocentric dataset built up as an extension of the existing How2Sign. This new dataset uses the hand tracking functionality of the new Oculus Quest 2 headset to obtain high quality hand pose estimation, a central feature for sign language understanding that cannot be obtained from the How2Sign videos with the publicly available human pose estimators.
- Published
- 2022
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