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Author | Debora Gil; Sergio Vera; Agnes Borras; Albert Andaluz; Miguel Angel Gonzalez Ballester | ||||
Title | Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities | Type | Journal Article | ||
Year | 2017 | Publication | Medical Image Analysis | Abbreviated Journal | MIA |
Volume | 35 | Issue | Pages | 390-402 | |
Keywords | Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points | ||||
Abstract | Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a condent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | IAM; 600.060; 600.096; 600.075; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GVB2017 | Serial | 2775 | ||
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Author | Arash Akbarinia; Karl R. Gegenfurtner | ||||
Title | Metameric Mismatching in Natural and Artificial Reflectances | Type | Journal Article | ||
Year | 2017 | Publication | Journal of Vision | Abbreviated Journal | JV |
Volume | 17 | Issue | 10 | Pages | 390-390 |
Keywords | Metamer; colour perception; spectral discrimination; photoreceptors | ||||
Abstract | The human visual system and most digital cameras sample the continuous spectral power distribution through three classes of receptors. This implies that two distinct spectral reflectances can result in identical tristimulus values under one illuminant and differ under another – the problem of metamer mismatching. It is still debated how frequent this issue arises in the real world, using naturally occurring reflectance functions and common illuminants.
We gathered more than ten thousand spectral reflectance samples from various sources, covering a wide range of environments (e.g., flowers, plants, Munsell chips) and evaluated their responses under a number of natural and artificial source of lights. For each pair of reflectance functions, we estimated the perceived difference using the CIE-defined distance ΔE2000 metric in Lab color space. The degree of metamer mismatching depended on the lower threshold value l when two samples would be considered to lead to equal sensor excitations (ΔE < l), and on the higher threshold value h when they would be considered different. For example, for l=h=1, we found that 43.129 comparisons out of a total of 6×107 pairs would be considered metameric (1 in 104). For l=1 and h=5, this number reduced to 705 metameric pairs (2 in 106). Extreme metamers, for instance l=1 and h=10, were rare (22 pairs or 6 in 108), as were instances where the two members of a metameric pair would be assigned to different color categories. Not unexpectedly, we observed variations among different reflectance databases and illuminant spectra with more frequency under artificial illuminants than natural ones. Overall, our numbers are not very different from those obtained earlier (Foster et al, JOSA A, 2006). However, our results also show that the degree of metamerism is typically not very strong and that category switches hardly ever occur. |
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Address | Florida, USA; May 2017 | ||||
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Notes | NEUROBIT; no menciona | Approved | no | ||
Call Number | Admin @ si @ AkG2017 | Serial | 2899 | ||
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Author | Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno | ||||
Title | Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" | Type | Conference Article | ||
Year | 2017 | Publication | Workshop on Big Data Analytics and Machine Learning for Data Communication Networks | Abbreviated Journal | |
Volume | Issue | Pages | 43-48 | ||
Keywords | mobile networks; call detail records; human mobility | ||||
Abstract | The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes,
have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users. By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “ always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations. |
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Address | UCLA; USA; August 2017 | ||||
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ISSN | ISBN | 978-1-4503-5054-9 | Medium | ||
Area | Expedition | Conference | ACMW (SIGCOMM) | ||
Notes | HuPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ FPT2017 | Serial | 2980 | ||
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Author | Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores | ||||
Title | On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts | Type | Journal Article | ||
Year | 2017 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | Cyber |
Volume | 47 | Issue | 11 | Pages | 3980 - 3990 |
Keywords | Multicue; multimodal; multiview; object detection | ||||
Abstract | Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. | ||||
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ISSN | 2168-2267 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 2810 | ||
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Author | Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo | ||||
Title | RGBN Multispectral Images: a Novel Color Restoration Approach | Type | Conference Article | ||
Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Multispectral Imaging; Free Sensor Model; Neural Network | ||||
Abstract | This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided. |
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Address | Porto; Portugal; June 2017 | ||||
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Area | Expedition | Conference | PAAMS | ||
Notes | ADAS; MSIAU; 600.118; 600.122 | Approved | no | ||
Call Number | Admin @ si @ ASS2017 | Serial | 2918 | ||
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Author | Adria Rico; Alicia Fornes | ||||
Title | Camera-based Optical Music Recognition using a Convolutional Neural Network | Type | Conference Article | ||
Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 27-28 | ||
Keywords | optical music recognition; document analysis; convolutional neural network; deep learning | ||||
Abstract | Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results | ||||
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Area | Expedition | Conference | GREC | ||
Notes | DAG;600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RiF2017 | Serial | 3059 | ||
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Author | Pau Riba; Alicia Fornes; Josep Llados | ||||
Title | Towards the Alignment of Handwritten Music Scores | Type | Book Chapter | ||
Year | 2017 | Publication | International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 9657 | Issue | Pages | 103-116 | |
Keywords | Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment | ||||
Abstract | It is very common to nd dierent versions of the same music work in archives of Opera Theaters. These dierences correspond to modications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such dierences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
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Publisher | Place of Publication | Editor | Bart Lamiroy; R Dueire Lins | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-319-52158-9 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG; 600.097; 602.006; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RFL2017 | Serial | 2955 | ||
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Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
Title | Optical Music Recognition by Recurrent Neural Networks | Type | Conference Article | ||
Year | 2017 | Publication | 14th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 25-26 | ||
Keywords | Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory | ||||
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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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRC2017 | Serial | 3056 | ||
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Author | Zhijie Fang; David Vazquez; Antonio Lopez | ||||
Title | On-Board Detection of Pedestrian Intentions | Type | Journal Article | ||
Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 17 | Issue | 10 | Pages | 2193 |
Keywords | pedestrian intention; ADAS; self-driving | ||||
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 | ||
Call Number | Admin @ si @ FVL2017 | Serial | 2983 | ||
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Author | Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Gloria Fernandez Esparrach; Xavier Gray; Olivier Romain; F. Javier Sanchez; Aymeric Histace | ||||
Title | Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis | Type | Conference Article | ||
Year | 2017 | Publication | 4th International Workshop on Computer Assisted and Robotic Endoscopy | Abbreviated Journal | |
Volume | Issue | Pages | 29-41 | ||
Keywords | Polyp detection; colonoscopy; real time; spatio temporal coherence | ||||
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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Address | Quebec; Canada; September 2017 | ||||
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Area | Expedition | Conference | CARE | ||
Notes | MV; 600.096; 600.075 | Approved | no | ||
Call Number | Admin @ si @ ABS2017b | Serial | 2977 | ||
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Author | Cristina Sanchez Montes; F. Javier Sanchez; Cristina Rodriguez de Miguel; Henry Cordova; Jorge Bernal; Maria Lopez Ceron; Josep Llach; Gloria Fernandez Esparrach | ||||
Title | Histological Prediction Of Colonic Polyps By Computer Vision. Preliminary Results | Type | Conference Article | ||
Year | 2017 | Publication | 25th United European Gastroenterology Week | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | polyps; histology; computer vision | ||||
Abstract | 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. | ||||
Address | Barcelona; October 2017 | ||||
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Area | Expedition | Conference | ESGE | ||
Notes | MV; no menciona | Approved | no | ||
Call Number | Admin @ si @ SSR2017 | Serial | 2979 | ||
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Author | Xinhang Song; Luis Herranz; Shuqiang Jiang | ||||
Title | Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs | Type | Conference Article | ||
Year | 2017 | Publication | 31st AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | RGB-D scene recognition; weakly supervised; fine tune; CNN | ||||
Abstract | 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. | ||||
Address | San Francisco CA; February 2017 | ||||
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Area | Expedition | Conference | AAAI | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ SHJ2017 | Serial | 2967 | ||
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Author | Xinhang Song; Shuqiang Jiang; Luis Herranz | ||||
Title | Combining Models from Multiple Sources for RGB-D Scene Recognition | Type | Conference Article | ||
Year | 2017 | Publication | 26th International Joint Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | Issue | Pages | 4523-4529 | ||
Keywords | Robotics and Vision; Vision and Perception | ||||
Abstract | 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. | ||||
Address | Melbourne; Australia; August 2017 | ||||
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Area | Expedition | Conference | IJCAI | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ SJH2017b | Serial | 2966 | ||
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Author | Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio | ||||
Title | The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation | Type | Conference Article | ||
Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Semantic Segmentation | ||||
Abstract | 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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Address | Honolulu; USA; July 2017 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | MILAB; ADAS; 600.076; 600.085; 601.281 | Approved | no | ||
Call Number | ADAS @ adas @ JDV2016 | Serial | 2866 | ||
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Author | Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera | ||||
Title | Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis | Type | Journal Article | ||
Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 122 | Issue | 2 | Pages | 212–227 |
Keywords | Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation | ||||
Abstract | 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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Notes | HuPBA;MILAB; 303.100 | Approved | no | ||
Call Number | Admin @ si @ PEB2017 | Serial | 2765 | ||
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