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| Records | |||||
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| Author | Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes | ||||
| Title | ASTRA: An Action Spotting TRAnsformer for Soccer Videos | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
| Volume | Issue | Pages | 93–102 | ||
| Keywords | |||||
| Abstract | In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. | ||||
Address  |
Otawa; Canada; October 2023 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | MMSports | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ XEM2023 | Serial | 3970 | ||
| Permanent link to this record | |||||
| Author | Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | Self-Supervised Visual Representations for Cross-Modal Retrieval | Type | Conference Article | ||
| Year | 2019 | Publication | ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
| Volume | Issue | Pages | 182–186 | ||
| Keywords | |||||
| Abstract | Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. | ||||
| Address |
Otawa; Canada; june 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICMR | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ PGR2019 | Serial | 3288 | ||
| Permanent link to this record | |||||
| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Impact of Image Preprocessing Methods on Polyp Localization in Colonoscopy Frames | Type | Conference Article | ||
| Year | 2013 | Publication | 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society | Abbreviated Journal | |
| Volume | Issue | Pages | 7350 - 7354 | ||
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| Abstract | In this paper we present our image preprocessing methods as a key part of our automatic polyp localization scheme. These methods are used to assess the impact of different endoluminal scene elements when characterizing polyps. More precisely we tackle the influence of specular highlights, blood vessels and black mask surrounding the scene. Experimental results prove that the appropriate handling of these elements leads to a great improvement in polyp localization results. | ||||
| Address |
Osaka; Japan; July 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1557-170X | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | EMBC | |
| Notes | MV; 600.047; 600.060;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BSV2013 | Serial | 2286 | ||
| Permanent link to this record | |||||
| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| Title | Cascade analysis for intestinal contraction detection | Type | Conference Article | ||
| Year | 2006 | Publication | 20th International Congress and exhibition Computer Assisted Radiology and Surgery | Abbreviated Journal | |
| Volume | Issue | Pages | 9-10 | ||
| Keywords | intestine video analysis, anisotropic features, support vector machine, cascade of classifiers | ||||
| Abstract | In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy. Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions. | ||||
| Address |
Osaka (Japan) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | 800 | Expedition | Conference | CARS | |
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSV2006a; IAM @ iam @ VSV2006h | Serial | 726 | ||
| Permanent link to this record | |||||
| Author | Kamal Nasrollahi; Sergio Escalera; P. Rasti; Gholamreza Anbarjafari; Xavier Baro; Hugo Jair Escalante; Thomas B. Moeslund | ||||
| Title | Deep Learning based Super-Resolution for Improved Action Recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 5th International Conference on Image Processing Theory, Tools and Applications IPTA2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 67 - 72 | ||
| Keywords | |||||
| Abstract | Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos. | ||||
| Address |
Orleans; France; November 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IPTA | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ NER2015 | Serial | 2648 | ||
| Permanent link to this record | |||||
| Author | Ekaterina Zaytseva; Jordi Vitria | ||||
| Title | A search based approach to non maximum suppression in face detection | Type | Conference Article | ||
| Year | 2012 | Publication | 19th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Poster
paper TA.P5.12 Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results. |
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| Address |
Orlando; USA; September 2012 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1522-4880 | ISBN | 978-1-4673-2534-9 | Medium | |
| Area | Expedition | Conference | ICIP | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ZaV2012 | Serial | 2060 | ||
| Permanent link to this record | |||||
| Author | Miquel Ferrer; Ernest Valveny; F. Serratosa; Horst Bunke | ||||
| Title | Exact Median Graph Computation via Graph Embedding | Type | Conference Article | ||
| Year | 2008 | Publication | 12th International Workshop on Structural and Syntactic Pattern Recognition | Abbreviated Journal | |
| Volume | 5324 | Issue | Pages | 15–24 | |
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| Address |
Orlando – Florida (USA) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | SSPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ FVS2008b | Serial | 1076 | ||
| Permanent link to this record | |||||
| Author | T. Alejandra Vidal; Andrew J. Davison; Juan Andrade; David W. Murray | ||||
| Title | Active Control for Single Camera SLAM | Type | Miscellaneous | ||
| Year | 2006 | Publication | IEEE International Conference on Robotics and Automation, 1930–1936 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | |||||
| Address |
Orlando (Florida) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | DAG @ dag @ VDA2006 | Serial | 666 | ||
| Permanent link to this record | |||||
| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Multi-Image Super-Resolution for Thermal Images | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 635-642 | |
| Keywords | Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block | ||||
| Abstract | This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two
attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches. |
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| Address |
Online; Feb 6-8, 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VISAPP | ||
| Notes | MSIAU; 601.349 | Approved | no | ||
| Call Number | Admin @ si @ RSV2022a | Serial | 3690 | ||
| Permanent link to this record | |||||
| Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui | ||||
| Title | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2021 | Publication | Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might no longer align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors, and propose a self regularization loss to decrease the negative impact of noisy neighbors. Furthermore, to aggregate information with more context, we consider expanded neighborhoods with small affinity values. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. Code is available in https://github.com/Albert0147/SFDA_neighbors. | ||||
| Address |
Online; December 7-10, 2021 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | NIPS | ||
| Notes | LAMP; 600.147; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3691 | ||
| Permanent link to this record | |||||
| Author | Jorge Charco; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Human Pose Estimation through a Novel Multi-view Scheme | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
| Volume | 5 | Issue | Pages | 855-862 | |
| Keywords | Multi-view Scheme; Human Pose Estimation; Relative Camera Pose; Monocular Approach | ||||
| Abstract | This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations. |
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| Address |
On line; Feb 6, 2022 – Feb 8, 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2184-4321 | ISBN | 978-989-758-555-5 | Medium | |
| Area | Expedition | Conference | VISAPP | ||
| Notes | MSIAU; 600.160 | Approved | no | ||
| Call Number | Admin @ si @ CSV2022 | Serial | 3689 | ||
| Permanent link to this record | |||||
| Author | Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva | ||||
| Title | Bayesian deep learning for semantic segmentation of food images | Type | Journal Article | ||
| Year | 2022 | Publication | Computers and Electrical Engineering | Abbreviated Journal | CEE |
| Volume | 103 | Issue | Pages | 108380 | |
| Keywords | Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis | ||||
| Abstract | Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. | ||||
| Address |
October 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Science Direct | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANR2022 | Serial | 3763 | ||
| Permanent link to this record | |||||
| Author | Javad Zolfaghari Bengar; Joost Van de Weijer; Bartlomiej Twardowski; Bogdan Raducanu | ||||
| Title | Reducing Label Effort: Self- Supervised Meets Active Learning | Type | Conference Article | ||
| Year | 2021 | Publication | International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 1631-1639 | ||
| Keywords | |||||
| Abstract | Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent developments in self-training have achieved very impressive results rivaling supervised learning on some datasets. The current work focuses on whether the two paradigms can benefit from each other. We studied object recognition datasets including CIFAR10, CIFAR100 and Tiny ImageNet with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. The performance gap between active learning trained either with self-training or from scratch diminishes as we approach to the point where almost half of the dataset is labeled. | ||||
| Address |
October 2021 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCVW | ||
| Notes | LAMP; OR | Approved | no | ||
| Call Number | Admin @ si @ ZVT2021 | Serial | 3672 | ||
| Permanent link to this record | |||||
| Author | Antonio Esteban Lansaque | ||||
| Title | An Endoscopic Navigation System for Lung Cancer Biopsy | Type | Book Whole | ||
| Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Lung cancer is one of the most diagnosed cancers among men and women. Actually,
lung cancer accounts for 13% of the total cases with a 5-year global survival rate in patients. Although Early detection increases survival rate from 38% to 67%, accurate diagnosis remains a challenge. Pathological confirmation requires extracting a sample of the lesion tissue for its biopsy. The preferred procedure for tissue biopsy is called bronchoscopy. A bronchoscopy is an endoscopic technique for the internal exploration of airways which facilitates the performance of minimal invasive interventions with low risk for the patient. Recent advances in bronchoscopic devices have increased their use for minimal invasive diagnostic and intervention procedures, like lung cancer biopsy sampling. Despite the improvement in bronchoscopic device quality, there is a lack of intelligent computational systems for supporting in-vivo clinical decision during examinations. Existing technologies fail to accurately reach the lesion due to several aspects at intervention off-line planning and poor intra-operative guidance at exploration time. Existing guiding systems radiate patients and clinical staff,might be expensive and achieve a suboptimlal 70% of yield boost. Diagnostic yield could be improved reducing radiation and costs by developing intra-operative support systems able to guide the bronchoscopist to the lesion during the intervention. The goal of this PhD thesis is to develop an image-based navigation systemfor intra-operative guidance of bronchoscopists to a target lesion across a path previously planned on a CT-scan. We propose a 3D navigation system which uses the anatomy of video bronchoscopy frames to locate the bronchoscope within the airways. Once the bronchoscope is located, our navigation system is able to indicate the bifurcation which needs to be followed to reach the lesion. In order to facilitate an off-line validation as realistic as possible, we also present a method for augmenting simulated virtual bronchoscopies with the appearance of intra-operative videos. Experiments performed on augmented and intra-operative videos, prove that our algorithm can be speeded up for an on-line implementation in the operating room. |
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| Address |
October 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Debora Gil;Carles Sanchez | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-121011-0-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ Est2019 | Serial | 3392 | ||
| Permanent link to this record | |||||
| Author | Aymen Azaza | ||||
| Title | Context, Motion and Semantic Information for Computational Saliency | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of explicit context modelling for saliency estimation. Several important works in saliency are based on the usage of object proposals. However, these methods focus on the saliency of the object proposal itself and ignore the context. To introduce context in such saliency approaches, we couple every object proposal with its direct context. This allows us to evaluate the importance of the immediate surround (context) for its saliency. We propose several saliency features which are computed from the context proposals including features based on omni-directional and horizontal context continuity. Secondly, we investigate the usage of top-downmethods (high-level semantic information) for the task of saliency prediction since most computational methods are bottom-up or only include few semantic classes. We propose to consider a wider group of object classes. These objects represent important semantic information which we will exploit in our saliency prediction approach. Thirdly, we develop a method to detect video saliency by computing saliency from supervoxels and optical flow. In addition, we apply the context features developed in this thesis for video saliency detection. The method combines shape and motion features with our proposed context features. To summarize, we prove that extending object proposals with their direct context improves the task of saliency detection in both image and video data. Also the importance of the semantic information in saliency estimation is evaluated. Finally, we propose a newmotion feature to detect saliency in video data. The three proposed novelties are evaluated on standard saliency benchmark datasets and are shown to improve with respect to state-of-the-art. |
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| Address |
October 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Ali Douik | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-945373-9-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Aza2018 | Serial | 3218 | ||
| Permanent link to this record | |||||