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Author |
Ivet Rafegas |
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Title  |
Color in Visual Recognition: from flat to deep representations and some biological parallelisms |
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Year |
2017 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations.
Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
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November 2017 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Maria Vanrell |
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978-84-945373-7-0 |
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CIC |
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no |
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Admin @ si @ Raf2017 |
Serial |
3100 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Clinical Usability Quantification Of a Real-Time Polyp Detection Method In Videocolonoscopy |
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Conference Article |
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Year |
2017 |
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25th United European Gastroenterology Week |
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Barcelona, October 2017 |
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ESGE |
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MV; no menciona |
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no |
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Admin @ si @ ABS2017c |
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2978 |
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Author |
Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos |
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Title |
Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
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Conference Article |
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Year |
2017 |
Publication |
18th World Conference on Lung Cancer |
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Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Yokohama; Japan; October 2017 |
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IASLC WCLC |
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IAM; 600.096; 600.075; 600.145 |
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Call Number |
Admin @ si @ OGM2017 |
Serial |
3044 |
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Author |
Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell |
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Title |
Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
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Conference Article |
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Year |
2017 |
Publication |
6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging |
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Volume |
10550 |
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Pages |
151-159 |
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Abstract |
Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Quebec; Canada; September 2017 |
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LNCS |
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CLIP |
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Notes |
IAM; 600.096; 600.075; 600.145 |
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no |
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Call Number |
Admin @ si @ GRM2017 |
Serial |
2957 |
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Author |
Fernando Vilariño |
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Title |
Citizen experience as a powerful communication tool: Open Innovation and the role of Living Labs in EU |
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Conference Article |
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Year |
2017 |
Publication |
European Conference of Science Journalists |
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The Open Innovation 2.0 model spearheaded by the European Commission introduces conceptual changes in how innovation processes should be developed. The notion of an innovation ecosystem, and the active participation of the citizens (and all the different actors of the quadruple helix) in innovation processes, opens up new channels for scientific communication, where the citizens (and all actors) can be naturally reached and facilitate the spread of the scientific message in their communities. Unleashing the power of such mechanisms, while maintaining control over the scientific communication done through such channels presents an opportunity and a challenge at the same time.
This workshop will look into key concepts that the Open Innovation 2.0 EU model introduces, and what new opportunities for communication they bring about. Specifically, we will focus on Living Labs, as a key instrument for implementing this innovation model at the regional level, and their potential in creating scientific dissemination spaces. |
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Copenhagen; June 2017 |
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ECSJ |
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Notes |
MV; 600.097;SIAI |
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no |
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Call Number |
Admin @ si @ Vil2017a |
Serial |
3032 |
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Author |
Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate |
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Title |
Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study |
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Conference Article |
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Year |
2017 |
Publication |
11th European CytoGenesis Conference |
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Florencia; Italia; July 2017 |
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ECA |
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IAM; 600.096; 600.145 |
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no |
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Admin @ si @ SBG2017a |
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2936 |
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Author |
Sergio Escalera; Vassilis Athitsos; Isabelle Guyon |
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Title |
Challenges in Multi-modal Gesture Recognition |
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Book Chapter |
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2017 |
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1-60 |
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Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM |
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Abstract |
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. |
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HuPBA; no proj |
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no |
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Call Number |
Admin @ si @ EAG2017 |
Serial |
3008 |
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Author |
Sergio Escalera; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon |
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Title |
ChaLearn Looking at People: A Review of Events and Resources |
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Conference Article |
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2017 |
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30th International Joint Conference on Neural Networks |
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This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities. |
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Anchorage; Alaska; USA; May 2017 |
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IJCNN |
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HuPBA; 602.143 |
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no |
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Call Number |
Admin @ si @ EBE2017 |
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3012 |
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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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Mountain View; CA; USA; November 2017 |
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CORL |
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ADAS; 600.085; 600.118 |
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no |
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Admin @ si @ DRC2017 |
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2988 |
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Author |
Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu |
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Can biological solutions help computers detect symmetry? |
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Conference Article |
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2017 |
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40th European Conference on Visual Perception |
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Berlin; Germany; August 2017 |
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ECVP |
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NEUROBIT |
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no |
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Admin @ si @ APE2017 |
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2995 |
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Author |
Adria Rico; Alicia Fornes |
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Camera-based Optical Music Recognition using a Convolutional Neural Network |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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27-28 |
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optical music recognition; document analysis; convolutional neural network; deep learning |
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Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results |
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GREC |
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DAG;600.097; 600.121 |
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Admin @ si @ RiF2017 |
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3059 |
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Author |
Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno |
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Title |
Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" |
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Conference Article |
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2017 |
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Workshop on Big Data Analytics and Machine Learning for Data Communication Networks |
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43-48 |
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mobile networks; call detail records; human mobility |
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The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes,
have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users.
By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “
always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations. |
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UCLA; USA; August 2017 |
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978-1-4503-5054-9 |
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ACMW (SIGCOMM) |
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HuPBA; no menciona |
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no |
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Admin @ si @ FPT2017 |
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2980 |
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Author |
Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis |
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Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification |
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2017 |
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3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition |
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Thessaloniki; Greece; October 2017 |
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IMEKOFOODS |
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ADAS; 600.118 |
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Admin @ si @ GDM2017 |
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3081 |
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Fernando Vilariño |
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Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation |
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2017 |
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Open Living Lab Days Report |
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Krakow; August 2017 |
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MV; no menciona;SIAI |
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3033 |
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F. Javier Sanchez; Jorge Bernal; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach |
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Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos |
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2017 |
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Machine Vision and Applications |
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MVAP |
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1-20 |
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Specular highlights; bright spot regions segmentation; region classification; colonoscopy |
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A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance de ning specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classi cation
of bright spot regions. The former de nes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classi cation stage performed by a linear SVM classi er using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being
closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology. |
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MV; 600.096; 600.175 |
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Admin @ si @ SBS2017 |
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2975 |
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