| Records |
| Author |
Fernando Vilariño |
| Title |
Giving Value to digital collections in the Public Library |
Type |
Conference Article |
| Year |
2016 |
Publication |
Librarian 2020 |
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| Address |
Brussels; Belgium; October 2016 |
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Conference |
LIB |
| Notes |
MV; 600.097;SIAI |
Approved |
no |
Call Number  |
Admin @ si @Vil2016a |
Serial |
2802 |
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| Author |
Fernando Vilariño |
| Title |
Computer Vision and Performing Arts |
Type |
Conference Article |
| Year |
2015 |
Publication |
Korean Scholars of Marketing Science |
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Seoul; Korea; October 2015 |
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Conference |
KAMS |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @Vil2015 |
Serial |
2799 |
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| Author |
Fernando Vilariño; Dimosthenis Karatzas |
| Title |
A Living Lab approach for Citizen Science in Libraries |
Type |
Conference Article |
| Year |
2016 |
Publication |
1st International ECSA Conference |
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| Address |
Berlin; Germany; May 2016 |
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Conference |
ECSA |
| Notes |
MV; DAG; 600.084; 600.097;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ViK2016 |
Serial |
2804 |
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| Author |
Fernando Vilariño; Dimosthenis Karatzas |
| Title |
The Library Living Lab |
Type |
Conference Article |
| Year |
2015 |
Publication |
Open Living Lab Days |
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| Address |
Istanbul; Turkey; August 2015 |
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Conference |
OLLD |
| Notes |
MV; DAG;SIAI |
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no |
| Call Number |
Admin @ si @ViK2015 |
Serial |
2797 |
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| Author |
Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig |
| Title |
Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis |
Type |
Book Chapter |
| Year |
2015 |
Publication |
Artificial Intelligence Research and Development |
Abbreviated Journal |
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| Volume |
277 |
Issue |
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Pages |
247 - 256 |
| Keywords |
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| Abstract |
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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| Publisher |
IOS Press |
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Frontiers in Artificial Intelligence and Applications |
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Conference |
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| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @TVR2015 |
Serial |
2780 |
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| Author |
A. Toet; M. Henselmans; M.P. Lucassen; Theo Gevers |
| Title |
Emotional effects of dynamic textures |
Type |
Journal |
| Year |
2011 |
Publication |
i-Perception |
Abbreviated Journal |
iPER |
| Volume |
2 |
Issue |
9 |
Pages |
969 – 991 |
| Keywords |
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| Abstract |
This study explores the effects of various spatiotemporal dynamic texture characteristics on human emotions. The emotional experience of auditory (eg, music) and haptic repetitive patterns has been studied extensively. In contrast, the emotional experience of visual dynamic textures is still largely unknown, despite their natural ubiquity and increasing use in digital media. Participants watched a set of dynamic textures, representing either water or various different media, and self-reported their emotional experience. Motion complexity was found to have mildly relaxing and nondominant effects. In contrast, motion change complexity was found to be arousing and dominant. The speed of dynamics had arousing, dominant, and unpleasant effects. The amplitude of dynamics was also regarded as unpleasant. The regularity of the dynamics over the textures’ area was found to be uninteresting, nondominant, mildly relaxing, and mildly pleasant. The spatial scale of the dynamics had an unpleasant, arousing, and dominant effect, which was larger for textures with diverse content than for water textures. For water textures, the effects of spatial contrast were arousing, dominant, interesting, and mildly unpleasant. None of these effects were observed for textures of diverse content. The current findings are relevant for the design and synthesis of affective multimedia content and for affective scene indexing and retrieval. |
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Edition |
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| ISSN |
2041-6695 |
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| Notes |
ALTRES;ISE |
Approved |
no |
| Call Number |
Admin @ si @THL2011 |
Serial |
1843 |
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| Author |
E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
| Title |
Regularized Clustering for Egocentric Video Segmentation |
Type |
Book Chapter |
| Year |
2015 |
Publication |
Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
327-336 |
| Keywords |
Temporal video segmentation ; Egocentric videos ; Clustering |
| Abstract |
In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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| Publisher |
Springer International Publishing |
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LNCS |
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ISBN |
978-3-319-19390-8 |
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| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @TDB2015a |
Serial |
2781 |
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| Author |
Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla |
| Title |
Wavelet based visible and infrared image fusion: a comparative study |
Type |
Journal Article |
| Year |
2016 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
16 |
Issue |
6 |
Pages |
1-15 |
| Keywords |
Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform |
| Abstract |
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). |
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| Notes |
ADAS; 600.086; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @SCA2016 |
Serial |
2807 |
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| Author |
Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
| Title |
Monocular visual odometry: A cross-spectral image fusion based approach |
Type |
Journal Article |
| Year |
2016 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
RAS |
| Volume |
85 |
Issue |
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Pages |
26-36 |
| Keywords |
Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion |
| Abstract |
This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
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| Publisher |
Elsevier B.V. |
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| Notes |
ADAS;600.086; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @SAC2016 |
Serial |
2811 |
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| Author |
Oriol Ramos Terrades; Alejandro Hector Toselli; Nicolas Serrano; Veronica Romero; Enrique Vidal; Alfons Juan |
| Title |
Interactive layout analysis and transcription systems for historic handwritten documents |
Type |
Conference Article |
| Year |
2010 |
Publication |
10th ACM Symposium on Document Engineering |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
219–222 |
| Keywords |
Handwriting recognition; Interactive predictive processing; Partial supervision; Interactive layout analysis |
| Abstract |
The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents, waiting to be classified and finally transcribed into a textual electronic format (such as ASCII or PDF). Nevertheless, most of the available fully-automatic applications addressing this task are far from being perfect and heavy and inefficient human intervention is often required to check and correct the results of such systems. In contrast, multimodal interactive-predictive approaches may allow the users to participate in the process helping the system to improve the overall performance. With this in mind, two sets of recent advances are introduced in this work: a novel interactive method for text block detection and two multimodal interactive handwritten text transcription systems which use active learning and interactive-predictive technologies in the recognition process. |
| Address |
Manchester, United Kingdom |
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ACM |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @RTS2010 |
Serial |
1857 |
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| Author |
Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
Robust non-blind color video watermarking using QR decomposition and entropy analysis |
Type |
Journal Article |
| Year |
2016 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
| Volume |
38 |
Issue |
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Pages |
838-847 |
| Keywords |
Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition |
| Abstract |
Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks. |
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| Notes |
HuPBA;MILAB; |
Approved |
no |
| Call Number |
Admin @ si @RSA2016 |
Serial |
2766 |
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| Author |
Ivet Rafegas; Maria Vanrell |
| Title |
Color encoding in biologically-inspired convolutional neural networks |
Type |
Journal Article |
| Year |
2018 |
Publication |
Vision Research |
Abbreviated Journal |
VR |
| Volume |
151 |
Issue |
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Pages |
7-17 |
| Keywords |
Color coding; Computer vision; Deep learning; Convolutional neural networks |
| Abstract |
Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. |
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| Notes |
CIC; 600.051; 600.087 |
Approved |
no |
| Call Number |
Admin @ si @RaV2018 |
Serial |
3114 |
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| Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
| Title |
Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives |
Type |
Journal Article |
| Year |
2016 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
RAS |
| Volume |
83 |
Issue |
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Pages |
312-325 |
| Keywords |
Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives |
| Abstract |
When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. |
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Elsevier B.V. |
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| Notes |
ADAS; 600.086, 600.076 |
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no |
| Call Number |
Admin @ si @OSS2016a |
Serial |
2806 |
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| Author |
Maria Oliver; Gloria Haro; Mariella Dimiccoli; Baptiste Mazin; Coloma Ballester |
| Title |
A computational model of amodal completion |
Type |
Conference Article |
| Year |
2016 |
Publication |
SIAM Conference on Imaging Science |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
| Address |
Albuquerque; New Mexico; USA; May 2016 |
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IS |
| Notes |
MILAB; 601.235 |
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no |
| Call Number |
Admin @ si @OHD2016a |
Serial |
2788 |
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| Author |
G. de Oliveira; Mariella Dimiccoli; Petia Radeva |
| Title |
Egocentric Image Retrieval With Deep Convolutional Neural Networks |
Type |
Conference Article |
| Year |
2016 |
Publication |
19th International Conference of the Catalan Association for Artificial Intelligence |
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Issue |
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Pages |
71-76 |
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| Address |
Barcelona; Spain; October 2016 |
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CCIA |
| Notes |
MILAB |
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no |
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Admin @ si @ODR2016 |
Serial |
2790 |
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