Ferran Diego, G.D. Evangelidis, & Joan Serrat. (2012). Night-time outdoor surveillance by mobile cameras. In 1st International Conference on Pattern Recognition Applications and Methods (Vol. 2, pp. 365–371).
Abstract: This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods.
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Debora Gil, Jaume Garcia, Ruth Aris, Guillaume Houzeaux, & Manuel Vazquez. (2009). A Riemmanian approach to cardiac fiber architecture modelling. In R. L. R. V. L. Nithiarasu (Ed.), 1st International Conference on Mathematical & Computational Biomedical Engineering (pp. 59–62). Swansea (UK).
Abstract: There is general consensus that myocardial fiber architecture should be modelled in order to fully understand the electromechanical properties of the Left Ventricle (LV). Diffusion Tensor magnetic resonance Imaging (DTI) is the reference image modality for rapid measurement of fiber orientations by means of the tensor principal eigenvectors. In this work, we present a mathematical framework for across subject comparison of the local geometry of the LV anatomy including the fiber architecture from the statistical analysis of DTI studies. We use concepts of differential geometry for defining a parametric domain suitable for statistical analysis of a low number of samples. We use Riemannian metrics to define a consistent computation of DTI principal eigenvector modes of variation. Our framework has been applied to build an atlas of the LV fiber architecture from 7 DTI normal canine hearts.
Keywords: cardiac fiber architecture; diffusion tensor magnetic resonance imaging; differential (Rie- mannian) geometry.
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Miguel Reyes, Gabriel Dominguez, & Sergio Escalera. (2011). Feature Weighting in Dynamic Time Warping for Gesture Recognition in Depth Data. In 1st IEEE Workshop on Consumer Depth Cameras for Computer Vision (pp. 1182–1188).
Abstract: We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach.
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Wenjuan Gong, Jürgen Brauer, Michael Arens, & Jordi Gonzalez. (2011). Modeling vs. Learning Approaches for Monocular 3D Human Pose Estimation. In 1st IEEE International Workshop on Performance Evaluation on Recognition of Human Actions and Pose Estimation Methods.
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Petia Radeva. (2020). Uncertainty Modeling within an End-to-end Framework for Food Image Analysis. In 1st DELTA.
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Josep Llados. (2006). Computer Vision: Progress of Research and Development ( J. Llados(ed.), Ed.).
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Ozan Caglayan, Walid Aransa, Yaxing Wang, Marc Masana, Mercedes Garcıa-Martinez, Fethi Bougares, et al. (2016). Does Multimodality Help Human and Machine for Translation and Image Captioning? In 1st conference on machine translation.
Abstract: This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR.
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Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, & Vladlen Koltun. (2017). CARLA: An Open Urban Driving Simulator. In 1st Annual Conference on Robot Learning. Proceedings of Machine Learning (Vol. 78, pp. 1–16).
Abstract: We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research.
Keywords: Autonomous driving; sensorimotor control; simulation
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J. Chazalon, P. Gomez-Kramer, Jean-Christophe Burie, M.Coustaty, S.Eskenazi, Muhammad Muzzamil Luqman, et al. (2017). SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode. In 1st International Workshop on Open Services and Tools for Document Analysis.
Abstract: As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of
understanding, usage and improvement.
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Agnes Borras, Francesc Tous, Josep Llados, & Maria Vanrell. (2003). High-Level Clothes Description Based on Colour-Texture and Structural Features. In 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003.
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David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2003). Ultrasound to magnetic resonance volume registration for brain sinking measurement.
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Clement Guerin, Christophe Rigaud, Karell Bertet, Jean-Christophe Burie, Arnaud Revel, & Jean-Marc Ogier. (2014). Réduction de l’espace de recherche pour les personnages de bandes dessinées. In 19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle.
Abstract: Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%.
Keywords: contextual search; document analysis; comics characters
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Patricia Suarez, & Angel Sappa. (2024). A Generative Model for Guided Thermal Image Super-Resolution. In 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
Abstract: This paper presents a novel approach for thermal super-resolution based on a fusion prior, low-resolution thermal image and H brightness channel of the corresponding visible spectrum image. The method combines bicubic interpolation of the ×8 scale target image with the brightness component. To enhance the guidance process, the original RGB image is converted to HSV, and the brightness channel is extracted. Bicubic interpolation is then applied to the low-resolution thermal image, resulting in a Bicubic-Brightness channel blend. This luminance-bicubic fusion is used as an input image to help the training process. With this fused image, the cyclic adversarial generative network obtains high-resolution thermal image results. Experimental evaluations show that the proposed approach significantly improves spatial resolution and pixel intensity levels compared to other state-of-the-art techniques, making it a promising method to obtain high-resolution thermal.
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Hector Laria Mantecon, Kai Wang, Joost Van de Weijer, Bogdan Raducanu, & Kai Wang. (2024). NeRF-Diffusion for 3D-Consistent Face Generation and Editing. In 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
Abstract: Generating high-fidelity 3D-aware images without 3D supervision is a valuable capability in various applications. Current methods based on NeRF features, SDF information, or triplane features have limited variation after training. To address this, we propose a novel approach that combines pretrained models for shape and content generation. Our method leverages a pretrained Neural Radiance Field as a shape prior and a diffusion model for content generation. By conditioning the diffusion model with 3D features, we enhance its ability to generate novel views with 3D awareness. We introduce a consistency token shared between the NeRF module and the diffusion model to maintain 3D consistency during sampling. Moreover, our framework allows for text editing of 3D-aware image generation, enabling users to modify the style over 3D views while preserving semantic content. Our contributions include incorporating 3D awareness into a text-to-image model, addressing identity consistency in 3D view synthesis, and enabling text editing of 3D-aware image generation. We provide detailed explanations, including the shape prior based on the NeRF model and the content generation process using the diffusion model. We also discuss challenges such as shape consistency and sampling saturation. Experimental results demonstrate the effectiveness and visual quality of our approach.
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Mohamed Ramzy Ibrahim, Robert Benavente, Daniel Ponsa, & Felipe Lumbreras. (2024). SWViT-RRDB: Shifted Window Vision Transformer Integrating Residual in Residual Dense Block for Remote Sensing Super-Resolution. In 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
Abstract: Remote sensing applications, impacted by acquisition season and sensor variety, require high-resolution images. Transformer-based models improve satellite image super-resolution but are less effective than convolutional neural networks (CNNs) at extracting local details, crucial for image clarity. This paper introduces SWViT-RRDB, a new deep learning model for satellite imagery super-resolution. The SWViT-RRDB, combining transformer with convolution and attention blocks, overcomes the limitations of existing models by better representing small objects in satellite images. In this model, a pipeline of residual fusion group (RFG) blocks is used to combine the multi-headed self-attention (MSA) with residual in residual dense block (RRDB). This combines global and local image data for better super-resolution. Additionally, an overlapping cross-attention block (OCAB) is used to enhance fusion and allow interaction between neighboring pixels to maintain long-range pixel dependencies across the image. The SWViT-RRDB model and its larger variants outperform state-of-the-art (SoTA) models on two different satellite datasets in terms of PSNR and SSIM.
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