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Debora Gil; Petia Radeva; Jordi Saludes; J. Mauri |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Segmentation of Artery Wall in Coronary IVUS Images: A Probabilistic Approach |
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Conference Article |
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Year |
2000 |
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International Conference on Pattern Recognition |
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4 |
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352-355 |
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Intravascular ultrasound images represent a unique tool to analyze the morphology of arteries and vessels (plaques, restenosis, etc). The poor quality of these images makes unsupervised segmentation based on traditional segmentation algorithms (such as edge or ridge/valley detection) fail to achieve the expected results. In this paper we present a probabilistic flexible template to separate different regions in the image. In particular, we use elliptic templates to model and detect the shape of the vessel inner wall in IVUS images. We present the results of successful segmentation obtained from patients undergoing stent treatment. A physician team has validated these results. |
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IAM @ iam @ GRS2000a |
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1537 |
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Author |
Debora Gil; Petia Radeva; Jordi Saludes; J. Mauri |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Segmentation of Artery Wall in Coronary IVUS Images: a Probabilistic Approach |
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Conference Article |
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2000 |
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Proceedings of CIC’2000 |
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Intravascular ultrasound images represent a unique tool to analyze the morphology of arteries and vessels (plaques, restenosis, etc). The poor quality of these images makes unsupervised segmentation based on traditional segmentation algorithms (such as edge or ridge/valley detection) fail to achieve the expected results. In this paper we present a probabilistic flexible template to separate different regions in the image. In particular, we use elliptic templates to model and detect the shape of the vessel inner wall in IVUS images. We present the results of successful segmentation obtained from patients undergoing stent treatment. A physician team has validated these results. |
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Cambridge, Massachussets |
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CIC |
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IAM;MILAB |
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IAM @ iam @ GRS2000 |
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1538 |
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Author |
Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey |
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Automatic segmentation and inpainting of specular highlights for endoscopic imaging |
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Journal Article |
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2010 |
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EURASIP Journal on Image and Video Processing |
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EURASIP JIVP |
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2010 |
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9 |
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800 |
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MV |
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fernando @ fernando @ |
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2423 |
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Oriol Pujol; Oriol Rodriguez-Leor; J. Mauri; E. Fernandez; V. Valle; Petia Radeva |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic segmentation and characterization of IVUS images by texture analysis |
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Miscellaneous |
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2004 |
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European Society of Cardiology Congress 2004 |
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Munich |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PRM2004 |
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478 |
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Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Reminiscence Therapy for Dementia |
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Conference Article |
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2020 |
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10th ACM International Conference on Multimedia Retrieval |
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383-387 |
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With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. |
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Virtual; October 2020 |
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ICRM |
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no |
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Admin @ si @ CGR2020 |
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3529 |
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Author |
Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis |
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2015 |
Publication |
Artificial Intelligence Research and Development |
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Volume |
277 |
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247 - 256 |
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Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms. |
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IOS Press |
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Frontiers in Artificial Intelligence and Applications |
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MILAB |
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no |
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Admin @ si @TVR2015 |
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2780 |
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Author |
Kaustubh Kulkarni; Ciprian Corneanu; Ikechukwu Ofodile; Sergio Escalera; Xavier Baro; Sylwia Hyniewska; Juri Allik; Gholamreza Anbarjafari |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Recognition of Facial Displays of Unfelt Emotions |
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Journal Article |
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Year |
2021 |
Publication |
IEEE Transactions on Affective Computing |
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TAC |
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12 |
Issue |
2 |
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377 - 390 |
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Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average, it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase. |
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HUPBA; no proj |
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no |
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Admin @ si @ KCO2021 |
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3658 |
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Author |
Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription |
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Journal |
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2017 |
Publication |
Journal of Medical Engineering & Technology |
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JMET |
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41 |
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6 |
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486-497 |
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INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question.
OBJECTIVES:
To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination.
METHODS:
Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows.
RESULTS:
A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation).
CONCLUSIONS:
Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs. |
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HUPBA; no menciona |
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no |
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Admin @ si @ EPB2017 |
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3010 |
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Author |
Mario Rojas; David Masip; A. Todorov; Jordi Vitria |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models |
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Journal Article |
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2011 |
Publication |
PloS one |
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Plos |
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6 |
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8 |
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e23323 |
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JCR Impact Factor 2010: 4.411
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions |
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Public Library of Science |
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OR;MV |
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no |
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Admin @ si @ RMT2011 |
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1883 |
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Author |
Mario Rojas; David Masip; A. Todorov; Jordi Vitria |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Point-based Facial Trait Judgments Evaluation |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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2715–2720 |
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Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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OR;MV |
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BCNPCL @ bcnpcl @ RMT2010 |
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1282 |
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Author |
J.L. Pech-Pacheco; J. Alvarez-Borrego; Gabriel Cristobal; Matthias S. Keil |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic object identification irrespective to geometric changes |
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2003 |
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Optical Engineering, 42(2): 551–559 (IF: 0.877) |
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Admin @ si @ PAC2003 |
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632 |
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Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic non-verbal communication skills analysis: a quantitative evaluation |
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Journal Article |
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2015 |
Publication |
AI Communications |
Abbreviated Journal |
AIC |
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28 |
Issue |
1 |
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87-101 |
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Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
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Abstract |
The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
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0921-7126 |
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HUPBA;MILAB |
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no |
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Admin @ si @ CCE2015 |
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2549 |
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Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation |
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Journal Article |
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2013 |
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Ultrasound in Medicine and Biology |
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UMB |
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39 |
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9 |
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1698-712 |
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Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy |
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Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages. |
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Admin @ si @ ABC2013 |
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2313 |
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Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Automatic Non-Rigid Temporal Alignment of IVUS Sequences |
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Conference Article |
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2012 |
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15th International Conference on Medical Image Computing and Computer Assisted Intervention |
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1 |
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642-650 |
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Clinical studies on atherosclerosis regression/progression performed by Intravascular Ultrasound analysis require the alignment of pullbacks of the same patient before and after clinical interventions. In this paper, a methodology for the automatic alignment of IVUS sequences based on the Dynamic Time Warping technique is proposed. The method is adapted to the specific IVUS alignment task by applying the non-rigid alignment technique to multidimensional morphological signals, and by introducing a sliding window approach together with a regularization term. To show the effectiveness of our method, an extensive validation is performed both on synthetic data and in-vivo IVUS sequences. The proposed method is robust to stent deployment and post dilation surgery and reaches an alignment error of approximately 0.7 mm for in-vivo data, which is comparable to the inter-observer variability. |
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Nice, France |
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Springer-Verlag Berlin, Heidelberg |
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978-3-642-33414-6 |
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MICCAI |
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MILAB |
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Admin @ si @ ABC2012 |
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2168 |
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Author |
Joan Mas; B. Lamiroy; Gemma Sanchez; Josep Llados |
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Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities |
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Miscellaneous |
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2006 |
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3rd Eurographics Workshop on Sketch Based Interfaces and Modeling (SBIM´06), 27–34 |
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Vienna (Austria) |
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DAG |
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DAG @ dag @ MLS2006b |
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710 |
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