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Author |
Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva |
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Title |
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering |
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Conference Article |
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Year |
2017 |
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8th Iberian Conference on Pattern Recognition and Image Analysis |
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Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks |
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In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. |
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Faro; Portugal; June 2017 |
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IbPRIA |
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MILAB; no proj |
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no |
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Admin @ si @ BPC2017 |
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2939 |
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Author |
Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Towards a Videobronchoscopy Localization System from Airway Centre Tracking |
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Conference Article |
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Year |
2017 |
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12th International Conference on Computer Vision Theory and Applications |
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352-359 |
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Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation |
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Abstract |
Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a
feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations. |
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Porto; Portugal; February 2017 |
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VISAPP |
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IAM; 600.096; 600.075; 600.145 |
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Admin @ si @ SEB2017 |
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2943 |
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Author |
Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera |
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Title |
XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system |
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Conference Article |
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Year |
2017 |
Publication |
4th Congreso de la Sociedad Española para las Ciencias del Videojuego |
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1957 |
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13-28 |
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Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris |
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Abstract |
XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively.
Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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Barcelona; June 2017 |
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COSECIVI; CEUR-WS |
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HUPBA; no menciona |
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no |
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Call Number |
Admin @ si @ AED2017 |
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3065 |
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Author |
Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso |
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Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
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Journal Article |
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Year |
2017 |
Publication |
Journal of Thoracic Oncology |
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JTO |
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12 |
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1S |
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S596-S597 |
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Thorax CT; diagnosis; Peripheral Pulmonary Nodule |
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A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP). |
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IAM; 600.096; 600.075; 600.145 |
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no |
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Admin @ si @ DGC2017a |
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2883 |
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Author |
Bojana Gajic; Eduard Vazquez; Ramon Baldrich |
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Title |
Evaluation of Deep Image Descriptors for Texture Retrieval |
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Conference Article |
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Year |
2017 |
Publication |
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) |
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251-257 |
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Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation |
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The increasing complexity learnt in the layers of a Convolutional Neural Network has proven to be of great help for the task of classification. The topic has received great attention in recently published literature.
Nonetheless, just a handful of works study low-level representations, commonly associated with lower layers. In this paper, we explore recent findings which conclude, counterintuitively, the last layer of the VGG convolutional network is the best to describe a low-level property such as texture. To shed some light on this issue, we are proposing a psychophysical experiment to evaluate the adequacy of different layers of the VGG network for texture retrieval. Results obtained suggest that, whereas the last convolutional layer is a good choice for a specific task of classification, it might not be the best choice as a texture descriptor, showing a very poor performance on texture retrieval. Intermediate layers show the best performance, showing a good combination of basic filters, as in the primary visual cortex, and also a degree of higher level information to describe more complex textures. |
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Porto, Portugal; 27 February – 1 March 2017 |
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VISIGRAPP |
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CIC; 600.087 |
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Admin @ si @ |
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3710 |
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Author |
German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez |
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Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
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Book Chapter |
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Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
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12 |
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227-241 |
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SYNTHIA; Virtual worlds; Autonomous Driving |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. |
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Springer |
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Editor |
Gabriela Csurka |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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ADAS @ adas @ RSV2017 |
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2882 |
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Author |
F. Javier Sanchez; Jorge Bernal; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach |
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Title |
Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos |
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Journal Article |
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Year |
2017 |
Publication |
Machine Vision and Applications |
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MVAP |
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1-20 |
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Keywords |
Specular highlights; bright spot regions segmentation; region classification; colonoscopy |
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A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance dening specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classication
of bright spot regions. The former denes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classication stage performed by a linear SVM classier using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being
closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology. |
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MV; 600.096; 600.175 |
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Admin @ si @ SBS2017 |
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2975 |
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Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera |
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Title |
Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis |
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Journal Article |
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2017 |
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International Journal of Computer Vision |
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IJCV |
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122 |
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2 |
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212–227 |
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Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation |
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This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures. |
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HuPBA;MILAB; 303.100 |
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Admin @ si @ PEB2017 |
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2765 |
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Author |
Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio |
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Title |
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation |
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2017 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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Semantic Segmentation |
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State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.
In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
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Honolulu; USA; July 2017 |
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CVPRW |
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MILAB; ADAS; 600.076; 600.085; 601.281 |
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ADAS @ adas @ JDV2016 |
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2866 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
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Combining Models from Multiple Sources for RGB-D Scene Recognition |
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2017 |
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26th International Joint Conference on Artificial Intelligence |
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4523-4529 |
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Robotics and Vision; Vision and Perception |
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Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
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Melbourne; Australia; August 2017 |
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IJCAI |
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LAMP; 600.120 |
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Admin @ si @ SJH2017b |
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2966 |
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Xinhang Song; Luis Herranz; Shuqiang Jiang |
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Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs |
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2017 |
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31st AAAI Conference on Artificial Intelligence |
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RGB-D scene recognition; weakly supervised; fine tune; CNN |
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Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data. |
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San Francisco CA; February 2017 |
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AAAI |
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LAMP; 600.120 |
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Admin @ si @ SHJ2017 |
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2967 |
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Cristina Sanchez Montes; F. Javier Sanchez; Cristina Rodriguez de Miguel; Henry Cordova; Jorge Bernal; Maria Lopez Ceron; Josep Llach; Gloria Fernandez Esparrach |
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Title |
Histological Prediction Of Colonic Polyps By Computer Vision. Preliminary Results |
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Conference Article |
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2017 |
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25th United European Gastroenterology Week |
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polyps; histology; computer vision |
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during colonoscopy, clinicians perform visual inspection of the polyps to predict histology. Kudo’s pit pattern classification is one of the most commonly used for optical diagnosis. These surface patterns present a contrast with respect to their neighboring regions and they can be considered as bright regions in the image that can attract the attention of computational methods. |
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Barcelona; October 2017 |
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ESGE |
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MV; no menciona |
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no |
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Call Number |
Admin @ si @ SSR2017 |
Serial |
2979 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Gloria Fernandez Esparrach; Xavier Gray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis |
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Conference Article |
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Year |
2017 |
Publication |
4th International Workshop on Computer Assisted and Robotic Endoscopy |
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29-41 |
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Keywords |
Polyp detection; colonoscopy; real time; spatio temporal coherence |
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Abstract |
Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all
polyps under real time constraints, increasing its performance due to our
adaptation strategy. |
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Quebec; Canada; September 2017 |
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CARE |
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Notes |
MV; 600.096; 600.075 |
Approved |
no |
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Call Number |
Admin @ si @ ABS2017b |
Serial |
2977 |
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Author |
Zhijie Fang; David Vazquez; Antonio Lopez |
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Title |
On-Board Detection of Pedestrian Intentions |
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Journal Article |
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Year |
2017 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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Volume |
17 |
Issue |
10 |
Pages |
2193 |
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Keywords |
pedestrian intention; ADAS; self-driving |
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Abstract |
Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
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Notes |
ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 |
Approved |
no |
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Call Number |
Admin @ si @ FVL2017 |
Serial |
2983 |
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Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Title |
Optical Music Recognition by Recurrent Neural Networks |
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Conference Article |
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Year |
2017 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
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Pages |
25-26 |
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Keywords |
Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
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Abstract |
Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level |
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ICDAR |
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DAG; 600.097; 601.302; 600.121 |
Approved |
no |
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Call Number |
Admin @ si @ BRC2017 |
Serial |
3056 |
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