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Author |
Petia Radeva |
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Title |
Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? |
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Conference Article |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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4 |
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Barcelona; October 2016 |
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CCIA |
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MILAB |
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no |
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Admin @ si @ Rad2016 |
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2832 |
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Pedro Herruzo; Marc Bolaños; Petia Radeva |
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Title |
Can a CNN Recognize Catalan Diet? |
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2016 |
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AIP Conference Proceedings |
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1773 |
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CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle.
With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes. |
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MILAB |
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Admin @ si @ HBR2016 |
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2837 |
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Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre |
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Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources |
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2016 |
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The future of historical demography. Upside down and inside out |
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127-131 |
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Acco Publishers |
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K.Matthijs; S.Hin; H.Matsuo; J.Kok |
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978-94-6292-722-3 |
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DAG; 600.097 |
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Admin @ si @ PFL2016 |
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2907 |
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Arash Akbarinia; C. Alejandro Parraga |
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Title |
Biologically plausible boundary detection |
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Conference Article |
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2016 |
Publication |
27th British Machine Vision Conference |
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Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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York; UK; September 2016 |
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BMVC |
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NEUROBIT; 600.068; 600.072 |
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Admin @ si @ AkP2016a |
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2867 |
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Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Banknote counterfeit detection through background texture printing analysis |
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Conference Article |
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Year |
2016 |
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12th IAPR Workshop on Document Analysis Systems |
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This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. |
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Rumania; May 2016 |
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DAS |
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DAG; 600.061; 601.269; 600.097 |
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Admin @ si @ BRL2016 |
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2950 |
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Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria |
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Automatic garment retexturing based on infrared information |
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Journal Article |
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2016 |
Publication |
Computers & Graphics |
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CG |
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59 |
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28-38 |
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Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading |
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This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms. |
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Elsevier |
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HuPBA;MILAB; |
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Admin @ si @ ADT2016 |
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2759 |
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Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
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Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light |
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Conference Article |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus |
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Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
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Barcelona; Spain; October 2016 |
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CCIA |
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OR;MV; |
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Admin @ si @ GMS2016 |
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2816 |
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Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas |
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Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology |
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Journal Article |
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2016 |
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IEEE Transactions on Affective Computing |
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TAC |
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9 |
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2 |
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161-175 |
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Mirroring; Nodding; Competence; Perception; Wearable Technology |
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Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras. |
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LAMP; 600.072; |
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Admin @ si @ MTR2016 |
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2826 |
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Joan Mas; Alicia Fornes; Josep Llados |
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An Interactive Transcription System of Census Records using Word-Spotting based Information Transfer |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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54-59 |
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This paper presents a system to assist in the transcription of historical handwritten census records in a crowdsourcing platform. Census records have a tabular structured layout. They consist in a sequence of rows with information of homes ordered by street address. For each household snippet in the page, the list of family members is reported. The censuses are recorded in intervals of a few years and the information of individuals in each household is quite stable from a point in time to the next one. This redundancy is used to assist the transcriber, so the redundant information is transferred from the census already transcribed to the next one. Household records are aligned from one year to the next one using the knowledge of the ordering by street address. Given an already transcribed census, a query by string word spotting is applied. Thus, names from the census in time t are used as queries in the corresponding home record in time t+1. Since the search is constrained, the obtained precision-recall values are very high, with an important reduction in the transcription time. The proposed system has been tested in a real citizen-science experience where non expert users transcribe the census data of their home town. |
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Santorini; Greece; April 2016 |
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DAG; 603.053; 602.006; 600.061; 600.077; 600.097 |
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Admin @ si @ MFL2016 |
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2751 |
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Jean-Pascal Jacob; Mariella Dimiccoli; Lionel Moisan |
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Active skeleton for bacteria modeling |
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2016 |
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Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization |
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CMBBE |
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5 |
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4 |
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274-286 |
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Bacteria modelling; medial axis; active contours; active skeleton; shape contraints |
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The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimizing an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at this http URL |
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MILAB |
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Admin @ si @ JDM2016 |
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2711 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels |
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2016 |
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29th Canadian Conference on Artificial Intelligence |
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9673 |
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3-14 |
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In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect |
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Victoria; Canada; May 2016 |
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Springer International Publishing |
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AI |
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HuPBA;MILAB; |
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Admin @ si @ BGE2016b |
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2770 |
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Anders Hast; Alicia Fornes |
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A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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150-155 |
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The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results. |
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Santorini; Greece; April 2016 |
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DAG; 602.006; 600.061; 600.077; 600.097 |
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HaF2016 |
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2753 |
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Author |
Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez |
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A reduced feature set for driver head pose estimation |
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Journal Article |
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2016 |
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Applied Soft Computing |
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ASOC |
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45 |
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98-107 |
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Head pose estimation; driving performance evaluation; subspace based methods; linear regression |
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Abstract |
Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. |
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ADAS; 600.085; 600.076; |
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Admin @ si @ DHL2016 |
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2760 |
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Author |
Gerard Canal; Sergio Escalera; Cecilio Angulo |
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Title |
A Real-time Human-Robot Interaction system based on gestures for assistive scenarios |
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Journal Article |
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Year |
2016 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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149 |
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65-77 |
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Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation |
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Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. |
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Elsevier B.V. |
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HuPBA;MILAB; |
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Admin @ si @ CEA2016 |
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2768 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas |
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Title |
A Living Lab approach for Citizen Science in Libraries |
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2016 |
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1st International ECSA Conference |
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Berlin; Germany; May 2016 |
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ECSA |
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MV; DAG; 600.084; 600.097;SIAI |
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Admin @ si @ViK2016 |
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2804 |
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