| Records |
| Author |
Axel Barroso-Laguna; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk |
| Title |
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters |
Type |
Conference Article |
| Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
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| Volume |
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Issue |
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Pages |
5835-5843 |
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| Abstract |
We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches benchmark. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance and complexity. |
| Address |
Seul; Corea; October 2019 |
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ICCV |
| Notes |
MSIAU; 600.122 |
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no |
| Call Number |
Admin @ si @ BRP2019 |
Serial |
3290 |
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| Author |
Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski |
| Title |
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch |
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Conference Article |
| Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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Aspen; Colorado; USA; March 2020 |
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WACV |
| Notes |
MSIAU; 600.122; 600.130 |
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no |
| Call Number |
Admin @ si @ RMP2020 |
Serial |
3291 |
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| Author |
Javad Zolfaghari Bengar; Abel Gonzalez-Garcia; Gabriel Villalonga; Bogdan Raducanu; Hamed H. Aghdam; Mikhail Mozerov; Antonio Lopez; Joost Van de Weijer |
| Title |
Temporal Coherence for Active Learning in Videos |
Type |
Conference Article |
| Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
914-923 |
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| Abstract |
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets. |
| Address |
Seul; Corea; October 2019 |
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Conference |
ICCVW |
| Notes |
LAMP; ADAS; 600.124; 602.200; 600.118; 600.120; 600.141 |
Approved |
no |
| Call Number |
Admin @ si @ ZGV2019 |
Serial |
3294 |
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| Author |
Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
| Title |
Smartphone picture organization: A hierarchical approach |
Type |
Journal Article |
| Year |
2019 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
187 |
Issue |
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Pages |
102789 |
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| Abstract |
We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization. |
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| Notes |
MILAB; no proj |
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no |
| Call Number |
Admin @ si @ LRD2019 |
Serial |
3297 |
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| Author |
Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter |
| Title |
Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI |
Type |
Conference Article |
| Year |
2019 |
Publication |
27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine |
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Issue |
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Pages |
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| Abstract |
The mitral valve vortex ring is a promising flow structure for analysis of diastolic function, however, methods for objective extraction of its formation to dissolution are lacking. We present a novel algorithm for objective extraction of the temporal evolution of the mitral valve vortex ring from magnetic resonance 4D flow data and validated the method against visual analysis. The algorithm successfully extracted mitral valve vortex rings during both early- and late-diastolic filling and agreed substantially with visual assessment. Early-diastolic mitral valve vortex ring properties differed between healthy subjects and patients with ischemic heart disease. |
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Conference |
ISMRM |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ KRS2019 |
Serial |
3300 |
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| Author |
Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer |
| Title |
Self-supervised blur detection from synthetically blurred scenes |
Type |
Journal Article |
| Year |
2019 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
92 |
Issue |
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Pages |
103804 |
| Keywords |
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| Abstract |
Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image. |
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| Notes |
LAMP; 600.109; 600.120 |
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no |
| Call Number |
Admin @ si @ AGG2019 |
Serial |
3301 |
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| Author |
Jiaolong Xu; Liang Xiao; Antonio Lopez |
| Title |
Self-supervised Domain Adaptation for Computer Vision Tasks |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
7 |
Issue |
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Pages |
156694 - 156706 |
| Keywords |
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| Abstract |
Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da. |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ XXL2019 |
Serial |
3302 |
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| Author |
Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure |
| Title |
Slanted Stixels: A way to represent steep streets |
Type |
Journal Article |
| Year |
2019 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
127 |
Issue |
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Pages |
1643–1658 |
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This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
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| Notes |
ADAS; 600.118; 600.124 |
Approved |
no |
| Call Number |
Admin @ si @ HSC2019 |
Serial |
3304 |
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| Author |
Zhijie Fang; Antonio Lopez |
| Title |
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
21 |
Issue |
11 |
Pages |
4773 - 4783 |
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| Abstract |
Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs. |
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ADAS; 600.118 |
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no |
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Admin @ si @ FaL2019 |
Serial |
3305 |
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| Author |
Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
| Title |
Semantic Monocular Depth Estimation Based on Artificial Intelligence |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Intelligent Transportation Systems Magazine |
Abbreviated Journal |
ITSM |
| Volume |
13 |
Issue |
4 |
Pages |
99-103 |
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| Abstract |
Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation. |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ GUH2019 |
Serial |
3306 |
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| Author |
Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Carles Sanchez |
| Title |
Enhancing virtual bronchoscopy with intra-operative data using a multi-objective GAN |
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Journal Article |
| Year |
2019 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCAR |
| Volume |
7 |
Issue |
1 |
Pages |
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This manuscript has been withdrawn by bioRxiv due to upload of an incorrect version of the manuscript by the authors. Therefore, this manuscript should not be cited as reference for this project. |
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| Notes |
IAM; 600.139; 600.145 |
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no |
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Admin @ si @ GEB2019 |
Serial |
3307 |
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| Author |
David Berga; C. Wloka; JK. Tsotsos |
| Title |
Modeling task influences for saccade sequence and visual relevance prediction |
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Journal Article |
| Year |
2019 |
Publication |
Journal of Vision |
Abbreviated Journal |
JV |
| Volume |
19 |
Issue |
10 |
Pages |
106c-106c |
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Previous work from Wloka et al. (2017) presented the Selective Tuning Attentive Reference model Fixation Controller (STAR-FC), an active vision model for saccade prediction. Although the model is able to efficiently predict saccades during free-viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns (Yarbus, 1967). These factors are considered in previous Selective Tuning architectures (Tsotsos and Kruijne, 2014)(Tsotsos, Kotseruba and Wloka, 2016)(Rosenfeld, Biparva & Tsotsos 2017), proposing a way to combine bottom-up and top-down contributions to fixation and saccade programming. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-term memory in combination with stimulus-driven eye movement neuronal correlates. Initial theories and models of these influences include (Rao, Zelinsky, Hayhoe and Ballard, 2002)(Navalpakkam and Itti, 2005)(Huang and Pashler, 2007) and show distinct ways to process the task requirements in combination with bottom-up attention. In this study we extend the STAR-FC with novel computational definitions of Long-Term Memory, Visual Task Executive and a Task Relevance Map. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memory model by processing a hierarchy of visual features learned from salient object detection datasets. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting relevance maps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions. |
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NEUROBIT; 600.128; 600.120 |
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no |
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Admin @ si @ BWT2019 |
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3308 |
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| Author |
David Berga; Xavier Otazu; Xose R. Fernandez-Vidal; Victor Leboran; Xose M. Pardo |
| Title |
Generating Synthetic Images for Visual Attention Modeling |
Type |
Journal Article |
| Year |
2019 |
Publication |
Perception |
Abbreviated Journal |
PER |
| Volume |
48 |
Issue |
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Pages |
99 |
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NEUROBIT; no menciona |
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no |
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Admin @ si @ BOF2019 |
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3309 |
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| Author |
Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias |
| Title |
Understanding trained CNNs by indexing neuron selectivity |
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Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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136 |
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318-325 |
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The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. |
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CIC; 600.087; 600.140; 600.118 |
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no |
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Admin @ si @ RVL2019 |
Serial |
3310 |
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| Author |
Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell |
| Title |
Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects |
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Journal Article |
| Year |
2020 |
Publication |
Journal of the Optical Society of America A |
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JOSA A |
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37 |
Issue |
1 |
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1-15 |
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Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
CIC; 600.140; 600.12; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ SBV2019 |
Serial |
3311 |
| Permanent link to this record |
