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| Author | Cristhian Aguilera | ||||
| Title | Local feature description in cross-spectral imagery | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Over the last few years, the number of consumer computer vision applications has increased dramatically. Today, computer vision solutions can be found in video game consoles, smartphone applications, driving assistance – just to name a few. Ideally, we require the performance of those applications, particularly those that are safety critical to remain constant under any external environment factors, such as changes in illumination or weather conditions. However, this is not always possible or very difficult to obtain by only using visible imagery, due to the inherent limitations of the images from that spectral band. For that reason, the use of images from different or multiple spectral bands is becoming more appealing.
The aforementioned possible advantages of using images from multiples spectral bands on various vision applications make multi-spectral image processing a relevant topic for research and development. Like in visible image processing, multi-spectral image processing needs tools and algorithms to handle information from various spectral bands. Furthermore, traditional tools such as local feature detection, which is the basis of many vision tasks such as visual odometry, image registration, or structure from motion, must be adjusted or reformulated to operate under new conditions. Traditional feature detection, description, and matching methods tend to underperform in multi-spectral settings, in comparison to mono-spectral settings, due to the natural differences between each spectral band. The work in this thesis is focused on the local feature description problem when cross-spectral images are considered. In this context, this dissertation has three main contributions. Firstly, the work starts by proposing the usage of a combination of frequency and spatial information, in a multi-scale scheme, as feature description. Evaluations of this proposal, based on classical hand-made feature descriptors, and comparisons with state of the art cross-spectral approaches help to find and understand limitations of such strategy. Secondly, different convolutional neural network (CNN) based architectures are evaluated when used to describe cross-spectral image patches. Results showed that CNN-based methods, designed to work with visible monocular images, could be successfully applied to the description of images from two different spectral bands, with just minor modifications. In this framework, a novel CNN-based network model, specifically intended to describe image patches from two different spectral bands, is proposed. This network, referred to as Q-Net, outperforms state of the art in the cross-spectral domain, including both previous hand-made solutions as well as L2 CNN-based architectures. The third contribution of this dissertation is in the cross-spectral feature description application domain. The multispectral odometry problem is tackled showing a real application of cross-spectral descriptors In addition to the three main contributions mentioned above, in this dissertation, two different multi-spectral datasets are generated and shared with the community to be used as benchmarks for further studies. |
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| Address | October 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa | |
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| ISSN | ISBN | 978-84-945373-6-3 | Medium | ||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ Agu2017 | Serial | 3020 | ||
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| Author | I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi | ||||
| Title | Learning quantification from images: A structured neural architecture | Type | Journal Article | ||
| Year | 2018 | Publication | Natural Language Engineering | Abbreviated Journal | NLE |
| Volume | 24 | Issue | 3 | Pages | 363-392 |
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| Abstract | Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SPH2018 | Serial | 3021 | ||
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| Author | Alejandro Cartas; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Batch-based activity recognition from egocentric photo-streams | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International workshop on Egocentric Perception, Interaction and Computing | Abbreviated Journal | |
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| Abstract | Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries. | ||||
| Address | Venice; Italy; October 2017; | ||||
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| Area | Expedition | Conference | ICCV - EPIC | ||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ CDR2017 | Serial | 3023 | ||
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| Author | Aniol Lidon; Marc Bolaños; Mariella Dimiccoli; Petia Radeva; Maite Garolera; Xavier Giro | ||||
| Title | Semantic Summarization of Egocentric Photo-Stream Events | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd Workshop on Lifelogging Tools and Applications | Abbreviated Journal | |
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| Address | San Francisco; USA; October 2017 | ||||
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| ISSN | ISBN | 978-1-4503-5503-2 | Medium | ||
| Area | Expedition | Conference | ACMW (LTA) | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ LBD2017 | Serial | 3024 | ||
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| Author | Laura Igual; Santiago Segui | ||||
| Title | Introduction to Data Science – A Python Approach to Concepts, Techniques and Applications. Undergraduate Topics in Computer Science | Type | Book Whole | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 1-215 | ||
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| Publisher | 978-3-319-50016-4 | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-3-319-50016-4 | Medium | ||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ IgS2017 | Serial | 3027 | ||
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| Author | Mireia Forns-Nadal; Federico Sem; Anna Mane; Laura Igual; Dani Guinart; Oscar Vilarroya | ||||
| Title | Increased Nucleus Accumbens Volume in First-Episode Psychosis | Type | Journal Article | ||
| Year | 2017 | Publication | Psychiatry Research-Neuroimaging | Abbreviated Journal | PRN |
| Volume | 263 | Issue | Pages | 57-60 | |
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| Abstract | Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSM2017 | Serial | 3028 | ||
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| Author | Fernando Vilariño; Dan Norton | ||||
| Title | Using mutimedia tools to spread poetry collections | Type | Conference Article | ||
| Year | 2017 | Publication | Internet librarian International Conference | Abbreviated Journal | |
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| Address | London; UK; October 2017 | ||||
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| Area | Expedition | Conference | ILI | ||
| Notes | MV; 600.097;SIAI | Approved | no | ||
| Call Number | Admin @ si @ ViN2017 | Serial | 3031 | ||
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| Author | Fernando Vilariño | ||||
| Title | Citizen experience as a powerful communication tool: Open Innovation and the role of Living Labs in EU | Type | Conference Article | ||
| Year | 2017 | Publication | European Conference of Science Journalists | Abbreviated Journal | |
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| Abstract | 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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| Address | Copenhagen; June 2017 | ||||
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| Area | Expedition | Conference | ECSJ | ||
| Notes | MV; 600.097;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2017a | Serial | 3032 | ||
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| Author | Fernando Vilariño | ||||
| Title | Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation | Type | Miscellaneous | ||
| Year | 2017 | Publication | Open Living Lab Days Report | Abbreviated Journal | |
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| Address | Krakow; August 2017 | ||||
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| Notes | MV; no menciona;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2017b | Serial | 3033 | ||
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| Author | Marc Masana; Joost Van de Weijer; Luis Herranz;Andrew Bagdanov; Jose Manuel Alvarez | ||||
| Title | Domain-adaptive deep network compression | Type | Conference Article | ||
| Year | 2017 | Publication | 17th IEEE International Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller target domains. However, networks designed to be optimal for the source task are often prohibitively large for the target task. In this work we address the compression of networks after domain transfer.
We focus on compression algorithms based on low-rank matrix decomposition. Existing methods base compression solely on learned network weights and ignore the statistics of network activations. We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing. We demonstrate that considering activation statistics when compressing weights leads to a rank-constrained regression problem with a closed-form solution. Because our method takes into account the target domain, it can more optimally remove the redundancy in the weights. Experiments show that our Domain Adaptive Low Rank (DALR) method significantly outperforms existing low-rank compression techniques. With our approach, the fc6 layer of VGG19 can be compressed more than 4x more than using truncated SVD alone – with only a minor or no loss in accuracy. When applied to domain-transferred networks it allows for compression down to only 5-20% of the original number of parameters with only a minor drop in performance. |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | LAMP; 601.305; 600.106; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3034 | ||
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| Author | Xialei Liu; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title | RankIQA: Learning from Rankings for No-reference Image Quality Assessment | Type | Conference Article | ||
| Year | 2017 | Publication | 17th IEEE International Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images. We demonstrate how our approach can be made significantly more efficient than traditional Siamese Networks by forward propagating a batch of images through a single network and backpropagating gradients derived from all pairs of images in the batch. Experiments on the TID2013 benchmark show that we improve the state-of-the-art by over 5%. Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA. | ||||
| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | LAMP; 600.106; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LWB2017b | Serial | 3036 | ||
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| Author | Rada Deeb; Damien Muselet; Mathieu Hebert; Alain Tremeau; Joost Van de Weijer | ||||
| Title | 3D color charts for camera spectral sensitivity estimation | Type | Conference Article | ||
| Year | 2017 | Publication | 28th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Estimating spectral data such as camera sensor responses or illuminant spectral power distribution from raw RGB camera outputs is crucial in many computer vision applications.
Usually, 2D color charts with various patches of known spectral reflectance are used as reference for such purpose. Deducing n-D spectral data (n»3) from 3D RGB inputs is an ill-posed problem that requires a high number of inputs. Unfortunately, most of the natural color surfaces have spectral reflectances that are well described by low-dimensional linear models, i.e. each spectral reflectance can be approximated by a weighted sum of the others. It has been shown that adding patches to color charts does not help in practice, because the information they add is redundant with the information provided by the first set of patches. In this paper, we propose to use spectral data of higher dimensionality by using 3D color charts that create inter-reflections between the surfaces. These inter-reflections produce multiplications between natural spectral curves and so provide non-linear spectral curves. We show that such data provide enough information for accurate spectral data estimation. |
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| Address | London; September 2017 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ DMH2017b | Serial | 3037 | ||
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| Author | N.Nayef; F.Yin; I.Bizid; H.Choi; Y.Feng; Dimosthenis Karatzas; Z.Luo; Umapada Pal; Christophe Rigaud; J. Chazalon; W.Khlif; Muhammad Muzzamil Luqman; Jean-Christophe Burie; C.L.Liu; Jean-Marc Ogier | ||||
| Title | ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1454-1459 | ||
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| Abstract | Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge. | ||||
| Address | Kyoto; Japan; November 2017 | ||||
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| ISSN | ISBN | 978-1-5386-3586-5 | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ NYB2017 | Serial | 3097 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate | ||||
| Title | Análisis 3d de la territorialidad cromosómica en células espermatogénicas: explorando la infertilidad desde un nuevo prisma | Type | Journal | ||
| Year | 2017 | Publication | Revista Asociación para el Estudio de la Biología de la Reproducción | Abbreviated Journal | ASEBIR |
| Volume | 22 | Issue | 2 | Pages | 105 |
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| Notes | IAM; 600.096; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SBG2017d | Serial | 3042 | ||
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| Author | Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos | ||||
| Title | Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer | Type | Conference Article | ||
| Year | 2017 | Publication | 18th World Conference on Lung Cancer | Abbreviated Journal | |
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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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| Address | Yokohama; Japan; October 2017 | ||||
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| Area | Expedition | Conference | IASLC WCLC | ||
| Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ OGM2017 | Serial | 3044 | ||
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