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| Author | Debora Gil;Agnes Borras;Ruth Aris;Mariano Vazquez;Pierre Lafortune; Guillame Houzeaux | ||||
Title  |
What a difference in biomechanics cardiac fiber makes | Type | Conference Article | ||
| Year | 2012 | Publication | Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges | Abbreviated Journal | |
| Volume | 7746 | Issue | Pages | 253-260 | |
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| Abstract | Computational simulations of the heart are a powerful tool for a comprehensive understanding of cardiac function and its intrinsic relationship with its muscular architecture. Cardiac biomechanical models require a vector field representing the orientation of cardiac fibers. A wrong orientation of the fibers can lead to a
non-realistic simulation of the heart functionality. In this paper we explore the impact of the fiber information on the simulated biomechanics of cardiac muscular anatomy. We have used the John Hopkins database to perform a biomechanical simulation using both a synthetic benchmark fiber distribution and the data obtained experimentally from DTI. Results illustrate how differences in fiber orientation affect heart deformation along cardiac cycle. |
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| Address | Nice, France | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-36960-5 | Medium | |
| Area | Expedition | Conference | STACOM | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GBA2012 | Serial | 1987 | ||
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| Author | R. Valenti; N. Sebe; Theo Gevers | ||||
| Title |
What are you looking at? Improving Visual gaze Estimation by Saliency | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 98 | Issue | 3 | Pages | 324-334 |
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| Abstract | Impact factor 2010: 5.15
Impact factor 2011/12?: 5.36 In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems. |
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0920-5691 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ VSG2012 | Serial | 1848 | ||
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| Author | Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix M. Martinez | ||||
| Title |
What does it mean to learn in deep networks? And, how does one detect adversarial attacks? | Type | Conference Article | ||
| Year | 2019 | Publication | 32nd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 4752-4761 | ||
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| Abstract | The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed computer vision. But, the fact that we do not know when a specific DNN will work and when it will fail has resulted in a lack of trust. A clear example is self-driving cars; people are uncomfortable sitting in a car driven by algorithms that may fail under some unknown, unpredictable conditions. Interpretability and explainability approaches attempt to address this by uncovering what a DNN models, i.e., what each node (cell) in the network represents and what images are most likely to activate it. This can be used to generate, for example, adversarial attacks. But these approaches do not generally allow us to determine where a DNN will succeed or fail and why. i.e., does this learned representation generalize to unseen samples? Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks. We show how this defines the ability of a network to generalize to unseen testing samples and, most importantly, why this is the case. | ||||
| Address | California; June 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ CME2019 | Serial | 3332 | ||
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| Author | Jordi Roca; Maria Vanrell; C. Alejandro Parraga | ||||
| Title |
What is constant in colour constancy? | Type | Conference Article | ||
| Year | 2012 | Publication | 6th European Conference on Colour in Graphics, Imaging and Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 337-343 | ||
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| Abstract | Color constancy refers to the ability of the human visual system to stabilize
the color appearance of surfaces under an illuminant change. In this work we studied how the interrelations among nine colors are perceived under illuminant changes, particularly whether they remain stable across 10 different conditions (5 illuminants and 2 backgrounds). To do so we have used a paradigm that measures several colors under an immersive state of adaptation. From our measures we defined a perceptual structure descriptor that is up to 87% stable over all conditions, suggesting that color category features could be used to predict color constancy. This is in agreement with previous results on the stability of border categories [1,2] and with computational color constancy algorithms [3] for estimating the scene illuminant. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 9781622767014 | Medium | ||
| Area | Expedition | Conference | CGIV | ||
| Notes | CIC | Approved | no | ||
| Call Number | RVP2012 | Serial | 2189 | ||
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| Author | Olivier Penacchio; C. Alejandro Parraga | ||||
| Title |
What is the best criterion for an efficient design of retinal photoreceptor mosaics? | Type | Journal Article | ||
| Year | 2011 | Publication | Perception | Abbreviated Journal | PER |
| Volume | 40 | Issue | Pages | 197 | |
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| Abstract | The proportions of L, M and S photoreceptors in the primate retina are arguably determined by evolutionary pressure and the statistics of the visual environment. Two information theory-based approaches have been recently proposed for explaining the asymmetrical spatial densities of photoreceptors in humans. In the first approach Garrigan et al (2010 PLoS ONE 6 e1000677), a model for computing the information transmitted by cone arrays which considers the differential blurring produced by the long-wavelength accommodation of the eye’s lens is proposed. Their results explain the sparsity of S-cones but the optimum depends weakly on the L:M cone ratio. In the second approach (Penacchio et al, 2010 Perception 39 ECVP Supplement, 101), we show that human cone arrays make the visual representation scale-invariant, allowing the total entropy of the signal to be preserved while decreasing individual neurons’ entropy in further retinotopic representations. This criterion provides a thorough description of the distribution of L:M cone ratios and does not depend on differential blurring of the signal by the lens. Here, we investigate the similarities and differences of both approaches when applied to the same database. Our results support a 2-criteria optimization in the space of cone ratios whose components are arguably important and mostly unrelated.
[This work was partially funded by projects TIN2010-21771-C02-1 and Consolider-Ingenio 2010-CSD2007-00018 from the Spanish MICINN. CAP was funded by grant RYC-2007-00484] |
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ PeP2011a | Serial | 1719 | ||
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| Author | Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer | ||||
| Title |
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning | Type | Conference Article | ||
| Year | 2021 | Publication | 19th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 13052 | Issue | 1 | Pages | 403-413 |
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| Abstract | Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results. | ||||
| Address | September 2021 | ||||
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| Area | Expedition | Conference | CAIP | ||
| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ ZRV2021 | Serial | 3673 | ||
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| Author | Patricia Marquez; Debora Gil; Aura Hernandez-Sabate; Daniel Kondermann | ||||
| Title |
When Is A Confidence Measure Good Enough? | Type | Conference Article | ||
| Year | 2013 | Publication | 9th International Conference on Computer Vision Systems | Abbreviated Journal | |
| Volume | 7963 | Issue | Pages | 344-353 | |
| Keywords | Optical flow, confidence measure, performance evaluation | ||||
| Abstract | Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality. |
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| Address | St Petersburg; Russia; July 2013 | ||||
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| Publisher | Springer Link | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-39401-0 | Medium | |
| Area | Expedition | Conference | ICVS | ||
| Notes | IAM;ADAS; 600.044; 600.057; 600.060; 601.145 | Approved | no | ||
| Call Number | IAM @ iam @ MGH2013a | Serial | 2218 | ||
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| Author | Joakim Bruslund Haurum; Sergio Escalera; Graham W. Taylor; Thomas B. | ||||
| Title |
Which Tokens to Use? Investigating Token Reduction in Vision Transformers | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets. To close this gap, we set out to understand the reduction patterns of 10 different token reduction methods using four image classification datasets. By systematically comparing these methods on the different classification tasks, we find that the Top-K pruning method is a surprisingly strong baseline. Through in-depth analysis of the different methods, we determine that: the reduction patterns are generally not consistent when varying the capacity of the backbone model, the reduction patterns of pruning-based methods significantly differ from fixed radial patterns, and the reduction patterns of pruning-based methods are correlated across classification datasets. Finally we report that the similarity of reduction patterns is a moderate-to-strong proxy for model performance. Project page at https://vap.aau.dk/tokens. | ||||
| Address | Paris; France; October 2023 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BET2023 | Serial | 3940 | ||
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| Author | Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu | ||||
| Title |
Which tone-mapping is the best? A comparative study of tone-mapping perceived quality | Type | Abstract | ||
| Year | 2014 | Publication | Perception | Abbreviated Journal | |
| Volume | 43 | Issue | Pages | 106 | |
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| Abstract | Perception 43 ECVP Abstract Supplement
High-dynamic-range (HDR) imaging refers to the methods designed to increase the brightness dynamic range present in standard digital imaging techniques. This increase is achieved by taking the same picture under di erent exposure values and mapping the intensity levels into a single image by way of a tone-mapping operator (TMO). Currently, there is no agreement on how to evaluate the quality of di erent TMOs. In this work we psychophysically evaluate 15 di erent TMOs obtaining rankings based on the perceived properties of the resulting tone-mapped images. We performed two di erent experiments on a CRT calibrated display using 10 subjects: (1) a study of the internal relationships between grey-levels and (2) a pairwise comparison of the resulting 15 tone-mapped images. In (1) observers internally matched the grey-levels to a reference inside the tone-mapped images and in the real scene. In (2) observers performed a pairwise comparison of the tone-mapped images alongside the real scene. We obtained two rankings of the TMOs according their performance. In (1) the best algorithm was ICAM by J.Kuang et al (2007) and in (2) the best algorithm was a TMO by Krawczyk et al (2005). Our results also show no correlation between these two rankings. |
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ CPO2014 | Serial | 2527 | ||
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| Author | Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu | ||||
| Title |
Which tone-mapping operator is the best? A comparative study of perceptual quality | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 35 | Issue | 4 | Pages | 626-638 |
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| Abstract | Tone-mapping operators (TMO) are designed to generate perceptually similar low-dynamic range images from high-dynamic range ones. We studied the performance of fifteen TMOs in two psychophysical experiments where observers compared the digitally-generated tone-mapped images to their corresponding physical scenes. All experiments were performed in a controlled environment and the setups were
designed to emphasize different image properties: in the first experiment we evaluated the local relationships among intensity-levels, and in the second one we evaluated global visual appearance among physical scenes and tone-mapped images, which were presented side by side. We ranked the TMOs according to how well they reproduced the results obtained in the physical scene. Our results show that ranking position clearly depends on the adopted evaluation criteria, which implies that, in general, these tone-mapping algorithms consider either local or global image attributes but rarely both. Regarding the question of which TMO is the best, KimKautz [1] and Krawczyk [2] obtained the better results across the different experiments. We conclude that a more thorough and standardized evaluation criteria is needed to study all the characteristics of TMOs, as there is ample room for improvement in future developments. |
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| Notes | NEUROBIT; 600.120; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ CPO2018 | Serial | 3088 | ||
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| Author | Anders Skaarup Johansen; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund | ||||
| Title |
Who Cares about the Weather? Inferring Weather Conditions for Weather-Aware Object Detection in Thermal Images | Type | Journal Article | ||
| Year | 2023 | Publication | Applied Sciences | Abbreviated Journal | AS |
| Volume | 13 | Issue | 18 | Pages | |
| Keywords | thermal; object detection; concept drift; conditioning; weather recognition | ||||
| Abstract | Deployments of real-world object detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, two types of weather-aware latent conditioning methods are investigated. The proposed method aims to guide two object detectors, (YOLOv5 and Deformable DETR) to become weather-aware. This is achieved by leveraging an auxiliary branch that predicts weather-related information while conditioning intermediate layers of the object detector. While the conditioning methods proposed do not directly improve the accuracy of baseline detectors, it can be observed that conditioned networks manage to extract a weather-related signal from the thermal images, thus resulting in a decreased miss rate at the cost of increased false positives. The extracted signal appears noisy and is thus challenging to regress accurately. This is most likely a result of the qualitative nature of the thermal sensor; thus, further work is needed to identify an ideal method for optimizing the conditioning branch, as well as to further improve the accuracy of the system. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ SNE2023 | Serial | 3983 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell | ||||
| Title |
Who Painted this Painting? | Type | Conference Article | ||
| Year | 2010 | Publication | Proceedings of The CREATE 2010 Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 329–333 | ||
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| Address | Gjovik (Norway) | ||||
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| Area | Expedition | Conference | CREATE | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ KWV2010 | Serial | 1329 | ||
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| Author | Matthias Eisenmann; Annika Reinke; Vivienn Weru; Minu D. Tizabi; Fabian Isensee; Tim J. Adler; Sharib Ali; Vincent Andrearczyk; Marc Aubreville; Ujjwal Baid; Spyridon Bakas; Niranjan Balu; Sophia Bano; Jorge Bernal; Sebastian Bodenstedt; Alessandro Casella; Veronika Cheplygina; Marie Daum; Marleen de Bruijne | ||||
| Title |
Why Is the Winner the Best? | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 19955-19966 | ||
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| Abstract | International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ ERW2023 | Serial | 3842 | ||
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| Author | Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi | ||||
| Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop | Type | Conference Article | ||
| Year | 2018 | Publication | 4th Women in Computer Vision Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 1941-19412 | ||
| Keywords | Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning | ||||
| Abstract | We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | WiCV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DBR2018 | Serial | 3222 | ||
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| Author | Xavier Soria; Angel Sappa; Riad I. Hammoud | ||||
| Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images | Type | Journal Article | ||
| Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 18 | Issue | 7 | Pages | 2059 |
| Keywords | RGB-NIR sensor; multispectral imaging; deep learning; CNNs | ||||
| Abstract | Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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| Notes | ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSH2018 | Serial | 3145 | ||
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