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Fernando Vilariño |
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Title |
Public Libraries Exploring how technology transforms the cultural experience of people |
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2019 |
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Workshop on Social Impact of AI. Open Living Lab Days Conference. |
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Thessaloniki; Grecia; September 2019 |
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MV; DAG; 600.140; 600.121;SIAI |
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Admin @ si @ Vil2019b |
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3458 |
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Author |
Fernando Vilariño |
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Title |
3D Scanning of Capitals at Library Living Lab |
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2019 |
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“Living Lab Projects 2019”. ENoLL. |
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MV; DAG; 600.140; 600.121;SIAI |
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Admin @ si @ Vil2019c |
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3463 |
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Author |
Fernando Vilariño |
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Title |
Unveiling the Social Impact of AI |
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2020 |
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Workshop at Digital Living Lab Days Conference |
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September 2020 |
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MV; DAG; 600.121; 600.140;SIAI |
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Admin @ si @ Vil2020 |
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3459 |
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Author |
Gabriel Villalonga; Antonio Lopez |
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Title |
Co-Training for On-Board Deep Object Detection |
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2020 |
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IEEE Access |
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ACCESS |
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194441 - 194456 |
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Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. |
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ADAS; 600.118 |
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Admin @ si @ ViL2020 |
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3488 |
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Author |
Gabriel Villalonga |
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Title |
Leveraging Synthetic Data to Create Autonomous Driving Perception Systems |
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2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Manually annotating images to develop vision models has been a major bottleneck
since computer vision and machine learning started to walk together. This has
been more evident since computer vision falls on the shoulders of data-hungry
deep learning techniques. When addressing on-board perception for autonomous
driving, the curse of data annotation is exacerbated due to the use of additional
sensors such as LiDAR. Therefore, any approach aiming at reducing such a timeconsuming and costly work is of high interest for addressing autonomous driving
and, in fact, for any application requiring some sort of artificial perception. In the
last decade, it has been shown that leveraging from synthetic data is a paradigm
worth to pursue in order to minimizing manual data annotation. The reason is
that the automatic process of generating synthetic data can also produce different
types of associated annotations (e.g. object bounding boxes for synthetic images
and LiDAR pointclouds, pixel/point-wise semantic information, etc.). Directly
using synthetic data for training deep perception models may not be the definitive
solution in all circumstances since it can appear a synth-to-real domain shift. In
this context, this work focuses on leveraging synthetic data to alleviate manual
annotation for three perception tasks related to driving assistance and autonomous
driving. In all cases, we assume the use of deep convolutional neural networks
(CNNs) to develop our perception models.
The first task addresses traffic sign recognition (TSR), a kind of multi-class
classification problem. We assume that the number of sign classes to be recognized
must be suddenly increased without having annotated samples to perform the
corresponding TSR CNN re-training. We show that leveraging synthetic samples of
such new classes and transforming them by a generative adversarial network (GAN)
trained on the known classes (i.e. without using samples from the new classes), it is
possible to re-train the TSR CNN to properly classify all the signs for a ∼ 1/4 ratio of
new/known sign classes. The second task addresses on-board 2D object detection,
focusing on vehicles and pedestrians. In this case, we assume that we receive a set
of images without the annotations required to train an object detector, i.e. without
object bounding boxes. Therefore, our goal is to self-annotate these images so
that they can later be used to train the desired object detector. In order to reach
this goal, we leverage from synthetic data and propose a semi-supervised learning
approach based on the co-training idea. In fact, we use a GAN to reduce the synthto-real domain shift before applying co-training. Our quantitative results show
that co-training and GAN-based image-to-image translation complement each
other up to allow the training of object detectors without manual annotation, and still almost reaching the upper-bound performances of the detectors trained from
human annotations. While in previous tasks we focus on vision-based perception,
the third task we address focuses on LiDAR pointclouds. Our initial goal was to
develop a 3D object detector trained on synthetic LiDAR-style pointclouds. While
for images we may expect synth/real-to-real domain shift due to differences in
their appearance (e.g. when source and target images come from different camera
sensors), we did not expect so for LiDAR pointclouds since these active sensors
factor out appearance and provide sampled shapes. However, in practice, we have
seen that it can be domain shift even among real-world LiDAR pointclouds. Factors
such as the sampling parameters of the LiDARs, the sensor suite configuration onboard the ego-vehicle, and the human annotation of 3D bounding boxes, do induce
a domain shift. We show it through comprehensive experiments with different
publicly available datasets and 3D detectors. This redirected our goal towards the
design of a GAN for pointcloud-to-pointcloud translation, a relatively unexplored
topic.
Finally, it is worth to mention that all the synthetic datasets used for these three
tasks, have been designed and generated in the context of this PhD work and will
be publicly released. Overall, we think this PhD presents several steps forward to
encourage leveraging synthetic data for developing deep perception models in the
field of driving assistance and autonomous driving. |
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February 2021 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Antonio Lopez;German Ros |
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978-84-122714-2-3 |
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ADAS; 600.118 |
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Call Number |
Admin @ si @ Vil2021 |
Serial |
3599 |
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Author |
Fernando Vilariño; Dan Norton |
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Title |
Using mutimedia tools to spread poetry collections |
Type |
Conference Article |
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Year |
2017 |
Publication |
Internet librarian International Conference |
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London; UK; October 2017 |
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ILI |
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MV; 600.097;SIAI |
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Call Number |
Admin @ si @ ViN2017 |
Serial |
3031 |
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Author |
Gioacchino Vino; Angel Sappa |
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Title |
Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach |
Type |
Conference Article |
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Year |
2013 |
Publication |
10th International Conference on Image Analysis and Recognition |
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7950 |
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354-363 |
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This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach. |
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Póvoa de Varzim; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-39093-7 |
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ICIAR |
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ADAS; 600.055 |
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Admin @ si @ ViS2013 |
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2562 |
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Author |
Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa |
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Title |
A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
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Conference Article |
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2017 |
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IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
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In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point
that minimizes the vacuum force needed to guarantee a grasp.
Experimental results in real scenarios are presented to show the validity of the proposed approach. |
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San Sebastian; Spain; May 2017 |
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ECMSM |
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ADAS; 600.086; 600.118 |
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Admin @ si @ VIS2017 |
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2917 |
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Author |
M. Visani; V.C.Kieu; Alicia Fornes; N.Journet |
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Title |
The ICDAR 2013 Music Scores Competition: Staff Removal |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1439-1443 |
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The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 600.061 |
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Admin @ si @ VKF2013 |
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2338 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce |
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The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces |
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2018 |
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Technology Innovation Management Review |
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DAG; MV; 600.097; 600.121; 600.129;SIAI |
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Admin @ si @ VKV2018a |
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3153 |
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Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce |
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Title |
Libraries as New Innovation Hubs: The Library Living Lab |
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Conference Article |
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2018 |
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30th ISPIM Innovation Conference |
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Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation. |
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Stockholm; May 2018 |
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DAG; MV; 600.097; 600.121; 600.129;SIAI |
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Admin @ si @ VKV2018b |
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3154 |
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Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich |
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Perception Based Representations for Computational Colour |
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2011 |
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3rd International Workshop on Computational Color Imaging |
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6626 |
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16-30 |
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colour perception, induction, naming, psychophysical data, saliency, segmentation |
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The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. |
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Milan, Italy |
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Springer-Verlag |
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Raimondo Schettini, Shoji Tominaga, Alain Trémeau |
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978-3-642-20403-6 |
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Admin @ si @ VMB2011 |
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1733 |
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Henry Velesaca; Raul Mira; Patricia Suarez; Christian X. Larrea; Angel Sappa |
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Deep Learning Based Corn Kernel Classification |
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2020 |
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1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture |
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This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learningbased approach, the Mask R-CNN architecture, while the classification is performed hrough a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been
performed and comparisons with other approaches are provided showing improvements with the proposed pipeline. |
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Virtual CVPR |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ VMS2020 |
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3430 |
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Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson |
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A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation |
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2012 |
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Journal of Vision |
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VSS |
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12 |
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6 (7) |
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1-14 |
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When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data. |
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1998 |
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Diego Velazquez; Pau Rodriguez; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez |
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A Closer Look at Embedding Propagation for Manifold Smoothing |
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2022 |
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Journal of Machine Learning Research |
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JMLR |
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23 |
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252 |
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1-27 |
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Regularization; emi-supervised learning; self-supervised learning; adversarial robustness; few-shot classification |
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Supervised training of neural networks requires a large amount of manually annotated data and the resulting networks tend to be sensitive to out-of-distribution (OOD) data.
Self- and semi-supervised training schemes reduce the amount of annotated data required during the training process. However, OOD generalization remains a major challenge for most methods. Strategies that promote smoother decision boundaries play an important role in out-of-distribution generalization. For example, embedding propagation (EP) for manifold smoothing has recently shown to considerably improve the OOD performance for few-shot classification. EP achieves smoother class manifolds by building a graph from sample embeddings and propagating information through the nodes in an unsupervised manner. In this work, we extend the original EP paper providing additional evidence and experiments showing that it attains smoother class embedding manifolds and improves results in settings beyond few-shot classification. Concretely, we show that EP improves the robustness of neural networks against multiple adversarial attacks as well as semi- and
self-supervised learning performance. |
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9/2022 |
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Admin @ si @ VRG2022 |
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3762 |
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