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Ariel Amato, Angel Sappa, Alicia Fornes, Felipe Lumbreras and Josep Llados. 2013. Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform. 2nd International ACM Workshop on Crowdsourcing for Multimedia.21–22.
Abstract: In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.
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M. Cruz, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla, Ricardo Toledo and Angel Sappa. 2015. Cross-spectral image registration and fusion: an evaluation study. 2nd International Conference on Machine Vision and Machine Learning.
Abstract: This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented.
Keywords: multispectral imaging; image registration; data fusion; infrared and visible spectra
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Guim Perarnau, Joost Van de Weijer, Bogdan Raducanu and Jose Manuel Alvarez. 2016. Invertible conditional gans for image editing. 30th Annual Conference on Neural Information Processing Systems Worshops.
Abstract: Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications.
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Cesar de Souza, Adrien Gaidon, Yohann Cabon and Antonio Lopez. 2017. Procedural Generation of Videos to Train Deep Action Recognition Networks. 30th IEEE Conference on Computer Vision and Pattern Recognition.2594–2604.
Abstract: Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos.
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David Vazquez and 7 others. 2017. A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images. 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery.
Abstract: Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.
Keywords: Deep Learning; Medical Imaging
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Marçal Rusiñol, David Aldavert, Dimosthenis Karatzas, Ricardo Toledo and Josep Llados. 2011. Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval. In P. Clough and 6 others, eds. 33rd European Conference on Information Retrieval. Berlin, Springer, 314–325. (LNCS.)
Abstract: In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.
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Lorenzo Porzi, Markus Hofinger, Idoia Ruiz, Joan Serrat, Samuel Rota Bulo and Peter Kontschieder. 2020. Learning Multi-Object Tracking and Segmentation from Automatic Annotations. 33rd IEEE Conference on Computer Vision and Pattern Recognition.6845–6854.
Abstract: In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data.
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Joan Serrat, Ferran Diego, Felipe Lumbreras and Jose Manuel Alvarez. 2007. Synchronization of Video Sequences from Free-moving Cameras. In J. Marti et al., ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis.620–627. (LNCS.)
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Antonio Lopez, Joan Serrat, Cristina Cañero and Felipe Lumbreras. 2007. Robust Lane Lines Detection and Quantitative Assessment. In J. Marti et al, ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis.274–281. (LNCS.)
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Jose Manuel Alvarez, Antonio Lopez and Ramon Baldrich. 2007. Shadow Resistant Road Segmentation from a Mobile Monocular System. 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16.
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