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Author |
Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez |
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Title |
Occlusion Aware Hand Pose Recovery from Sequences of Depth Images |
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Conference Article |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs. |
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HUPBA; ISE; 602.143; 600.098; 600.119 |
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Admin @ si @ MEC2017 |
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2970 |
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Author |
Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo |
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Title |
Image vignetting reduction via a maximization of fuzzy entropy |
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Conference Article |
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2017 |
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IEEE International Conference on Fuzzy Systems |
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In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view. |
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Napoles; Italia; July 2017 |
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FUZZ-IEEE |
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LAMP; 600.120 |
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Admin @ si @ LMG2017 |
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2972 |
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Author |
Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen |
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Title |
Bandwidth Limited Object Recognition in High Resolution Imagery |
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2017 |
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IEEE Winter conference on Applications of Computer Vision |
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This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance. |
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Santa Rosa; CA; USA; March 2017 |
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WACV |
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LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 |
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Admin @ si @ LBW2017 |
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2973 |
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Author |
Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen |
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Title |
Multi-modal Deep Learning Approach for Flood Detection |
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Conference Article |
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2017 |
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MediaEval Benchmarking Initiative for Multimedia Evaluation |
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In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task. |
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Dublin; Ireland; September 2017 |
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MediaEval |
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LAMP; 600.084; 600.109; 600.120 |
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Admin @ si @ LWB2017a |
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2974 |
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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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Polyp detection; colonoscopy; real time; spatio temporal coherence |
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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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MV; 600.096; 600.075 |
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Call Number |
Admin @ si @ ABS2017b |
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2977 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Clinical Usability Quantification Of a Real-Time Polyp Detection Method In Videocolonoscopy |
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Conference Article |
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2017 |
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25th United European Gastroenterology Week |
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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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Admin @ si @ ABS2017c |
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2978 |
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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 |
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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 |
Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno |
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Title |
Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" |
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Conference Article |
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Year |
2017 |
Publication |
Workshop on Big Data Analytics and Machine Learning for Data Communication Networks |
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43-48 |
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mobile networks; call detail records; human mobility |
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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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UCLA; USA; August 2017 |
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978-1-4503-5054-9 |
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ACMW (SIGCOMM) |
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HuPBA; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ FPT2017 |
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2980 |
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Author |
Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera |
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A survey on deep learning based approaches for action and gesture recognition in image sequences |
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Conference Article |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning
for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions.
We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research. |
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Washington; USA; May 2017 |
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HUPBA; no proj |
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no |
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Admin @ si @ ACB2017b |
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2982 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title |
Color representation in CNNs: parallelisms with biological vision |
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Conference Article |
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2017 |
Publication |
ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision |
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Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features
are efficiently represented. Here, we dissect a trained CNN
[2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions
of them to quantify color tuning properties of artificial neurons to provide a classification of the network population.
We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first
layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT). |
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Venice; Italy; October 2017 |
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ICCV-MBCC |
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CIC; 600.087; 600.051 |
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Call Number |
Admin @ si @ RaV2017 |
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2984 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
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Title |
Learning structural loss parameters on graph embedding applied on symbolic graphs |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. |
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Kyoto; Japan; November 2017 |
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GREC |
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DAG; 600.097; 600.121 |
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no |
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Call Number |
Admin @ si @ JRL2017b |
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3073 |
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Author |
Xavier Soria; Angel Sappa; Arash Akbarinia |
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Title |
Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities |
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Conference Article |
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2017 |
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7th International Conference on Image Processing Theory, Tools & Applications |
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Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset |
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Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. |
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Montreal; Canada; November 2017 |
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IPTA |
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Notes |
NEUROBIT; MSIAU; 600.122 |
Approved |
no |
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Call Number |
Admin @ si @ SSA2017 |
Serial |
3074 |
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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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Year |
2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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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. |
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Mountain View; CA; USA; November 2017 |
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ADAS; 600.085; 600.118 |
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Admin @ si @ DRC2017 |
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2988 |
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Arash Akbarinia; Raquel Gil Rodriguez; C. Alejandro Parraga |
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Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism |
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2017 |
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28th British Machine Vision Conference |
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Pooling is a ubiquitous operation in image processing algorithms that allows for higher-level processes to collect relevant low-level features from a region of interest. Currently, max-pooling is one of the most commonly used operators in the computational literature. However, it can lack robustness to outliers due to the fact that it relies merely on the peak of a function. Pooling mechanisms are also present in the primate visual cortex where neurons of higher cortical areas pool signals from lower ones. The receptive fields of these neurons have been shown to vary according to the contrast by aggregating signals over a larger region in the presence of low contrast stimuli. We hypothesise that this contrast-variant-pooling mechanism can address some of the shortcomings of maxpooling. We modelled this contrast variation through a histogram clipping in which the percentage of pooled signal is inversely proportional to the local contrast of an image. We tested our hypothesis by applying it to the phenomenon of colour constancy where a number of popular algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and Double-Opponency). For each of these methods, we investigated the consequences of replacing their original max-pooling by the proposed contrast-variant-pooling. Our experiments on three colour constancy benchmark datasets suggest that previous results can significantly improve by adopting a contrast-variant-pooling mechanism. |
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London; September 2017 |
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NEUROBIT; 600.068; 600.072 |
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Admin @ si @ AGP2017 |
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2992 |
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Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu |
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Can biological solutions help computers detect symmetry? |
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2017 |
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40th European Conference on Visual Perception |
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Berlin; Germany; August 2017 |
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2995 |
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