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Bogdan Raducanu, Alireza Bosaghzadeh, & Fadi Dornaika. (2014). Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics. In 1st Workshop on Computer Vision for Affective Computing (pp. 1–8).
Abstract: Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression.
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C. Alejandro Parraga. (2015). Perceptual Psychophysics. In G.Cristobal, M.Keil, & L.Perrinet (Eds.), Biologically-Inspired Computer Vision: Fundamentals and Applications.
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Firat Ismailoglu, Ida G. Sprinkhuizen-Kuyper, Evgueni Smirnov, Sergio Escalera, & Ralf Peeters. (2015). Fractional Programming Weighted Decoding for Error-Correcting Output Codes. In Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 (pp. 38–50). Springer International Publishing.
Abstract: In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
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Hugo Jair Escalante, Jose Martinez, Sergio Escalera, Victor Ponce, & Xavier Baro. (2015). Improving Bag of Visual Words Representations with Genetic Programming. In IEEE International Joint Conference on Neural Networks IJCNN2015.
Abstract: The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.
In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image
categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm.
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Isabelle Guyon, Kristin Bennett, Gavin Cawley, Hugo Jair Escalante, Sergio Escalera, Tin Kam Ho, et al. (2015). Design of the 2015 ChaLearn AutoML Challenge. In IEEE International Joint Conference on Neural Networks IJCNN2015.
Abstract: ChaLearn is organizing for IJCNN 2015 an Automatic Machine Learning challenge (AutoML) to solve classification and regression problems from given feature representations, without any human intervention. This is a challenge with code
submission: the code submitted can be executed automatically on the challenge servers to train and test learning machines on new datasets. However, there is no obligation to submit code. Half of the prizes can be won by just submitting prediction results.
There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is no requirement to participate in previous rounds to enter a new round. The rounds alternate AutoML phases in which submitted code is “blind tested” on
datasets the participants have never seen before, and Tweakathon phases giving time (' 1 month) to the participants to improve their methods by tweaking their code on those datasets. This challenge will push the state-of-the-art in fully automatic machine learning on a wide range of problems taken from real world
applications. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML
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Frederic Sampedro, Sergio Escalera, Anna Domenech, & Ignasi Carrio. (2014). A computational framework for cancer response assessment based on oncological PET-CT scans. CBM - Computers in Biology and Medicine, 55, 92–99.
Abstract: In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks.
Keywords: Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis
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Maedeh Aghaei, & Petia Radeva. (2014). Bag-of-Tracklets for Person Tracking in Life-Logging Data. In 17th International Conference of the Catalan Association for Artificial Intelligence (Vol. 269, pp. 35–44).
Abstract: By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data.
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Enric Marti, J.Roncaries, Debora Gil, Aura Hernandez-Sabate, Antoni Gurgui, & Ferran Poveda. (2015). PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities. JOTSE - Journal of Technology and Science Education, 87–96.
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Carles Sanchez, Oriol Ramos Terrades, Patricia Marquez, Enric Marti, J.Roncaries, & Debora Gil. (2015). Automatic evaluation of practices in Moodle for Self Learning in Engineering. JOTSE - Journal of Technology and Science Education, 97–106.
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Carles Sanchez, Jorge Bernal, F. Javier Sanchez, Antoni Rosell, Marta Diez-Ferrer, & Debora Gil. (2015). Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy. IJCAR - International Journal of Computer Assisted Radiology and Surgery, 10(6), 935–945.
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Carles Sanchez, Debora Gil, R. Tazi, Jorge Bernal, Y. Ruiz, L. Planas, et al. (2015). Quasi-real time digital assessment of Central Airway Obstruction. In 3rd European congress for bronchology and interventional pulmonology ECBIP2015.
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Carles Sanchez, Jorge Bernal, F. Javier Sanchez, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2015). Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy. In 6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 (Vol. 10, pp. 935–945).
Abstract: PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service.
METHODS:
Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion.
RESULTS:
Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room.
CONCLUSIONS:
Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done.
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Antoni Gurgui, Debora Gil, & Enric Marti. (2015). Laplacian Unitary Domain for Texture Morphing. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications VISIGRAPP2015 (Vol. 1, pp. 693–699). SciTePress.
Abstract: Deformation of expressive textures is the gateway to realistic computer synthesis of expressions. By their good mathematical properties and flexible formulation on irregular meshes, most texture mappings rely on solutions to the Laplacian in the cartesian space. In the context of facial expression morphing, this approximation can be seen from the opposite point of view by neglecting the metric. In this paper, we use the properties of the Laplacian in manifolds to present a novel approach to warping expressive facial images in order to generate a morphing between them.
Keywords: Facial; metamorphosis;LaplacianMorphing
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Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2015). A Novel Cochlear Reference Frame Based On The Laplace Equation. In 29th international Congress and Exhibition on Computer Assisted Radiology and Surgery (Vol. 10, pp. 1–312).
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Hanne Kause, Patricia Marquez, Andrea Fuster, Aura Hernandez-Sabate, Luc Florack, Debora Gil, et al. (2015). Quality Assessment of Optical Flow in Tagging MRI. In 5th Dutch Bio-Medical Engineering Conference BME2015.
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