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Author |
Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
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Title |
Hierarchical online domain adaptation of deformable part-based models |
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2016 |
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IEEE International Conference on Robotics and Automation |
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5536-5541 |
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Domain Adaptation; Pedestrian Detection |
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We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points. |
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Stockholm; Sweden; May 2016 |
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ICRA |
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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XVM2016 |
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2728 |
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Carlos David Martinez Hinarejos; Josep Llados; Alicia Fornes; Francisco Casacuberta; Lluis de Las Heras; Joan Mas; Moises Pastor; Oriol Ramos Terrades; Joan Andreu Sanchez; Enrique Vidal; Fernando Vilariño |
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Context, multimodality, and user collaboration in handwritten text processing: the CoMUN-HaT project |
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2016 |
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3rd IberSPEECH |
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Processing of handwritten documents is a task that is of wide interest for many
purposes, such as those related to preserve cultural heritage. Handwritten text recognition techniques have been successfully applied during the last decade to obtain transcriptions of handwritten documents, and keyword spotting techniques have been applied for searching specific terms in image collections of handwritten documents. However, results on transcription and indexing are far from perfect. In this framework, the use of new data sources arises as a new paradigm that will allow for a better transcription and indexing of handwritten documents. Three main different data sources could be considered: context of the document (style, writer, historical time, topics,. . . ), multimodal data (representations of the document in a different modality, such as the speech signal of the dictation of the text), and user feedback (corrections, amendments,. . . ). The CoMUN-HaT project aims at the integration of these different data sources into the transcription and indexing task for handwritten documents: the use of context derived from the analysis of the documents, how multimodality can aid the recognition process to obtain more accurate transcriptions (including transcription in a modern version of the language), and integration into a userin-the-loop assisted text transcription framework. This will be reflected in the construction of a transcription and indexing platform that can be used by both professional and nonprofessional users, contributing to crowd-sourcing activities to preserve cultural heritage and to obtain an accessible version of the involved corpus. |
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Lisboa; Portugal; November 2016 |
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IberSPEECH |
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DAG; MV; 600.097;SIAI |
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Admin @ si @MLF2016 |
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2813 |
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Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez |
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Title |
GPU-based pedestrian detection for autonomous driving |
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2016 |
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GPU Technology Conference |
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Pedestrian Detection; GPU |
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Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. |
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Silicon Valley; San Francisco; USA; April 2016 |
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GTC |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ CSM2016 |
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2737 |
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Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez |
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Title |
Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching |
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Conference Article |
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2016 |
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GPU Technology Conference |
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Stereo; Autonomous Driving; GPU; 3d reconstruction |
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Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms |
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Silicon Valley; San Francisco; USA; April 2016 |
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GTC |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ HME2016 |
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2738 |
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Author |
German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
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Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
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Conference Article |
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Year |
2016 |
Publication |
29th IEEE Conference on Computer Vision and Pattern Recognition |
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3234-3243 |
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Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
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Las Vegas; USA; June 2016 |
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CVPR |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ RSM2016 |
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2739 |
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Author |
Onur Ferhat; Fernando Vilariño |
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Title |
Low Cost Eye Tracking: The Current Panorama |
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Journal Article |
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2016 |
Publication |
Computational Intelligence and Neuroscience |
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CIN |
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Article ID 8680541 |
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Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools. |
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MV; 605.103; 600.047; 600.097;SIAI |
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Admin @ si @ FeV2016 |
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2744 |
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Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez |
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Title |
Comparison of two non-linear model-based control strategies for autonomous vehicles |
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2016 |
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24th Mediterranean Conference on Control and Automation |
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846-851 |
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Autonomous Driving; Control |
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This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation. |
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Athens; Greece; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ ASP2016 |
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2750 |
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Joan Mas; Alicia Fornes; Josep Llados |
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An Interactive Transcription System of Census Records using Word-Spotting based Information Transfer |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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54-59 |
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This paper presents a system to assist in the transcription of historical handwritten census records in a crowdsourcing platform. Census records have a tabular structured layout. They consist in a sequence of rows with information of homes ordered by street address. For each household snippet in the page, the list of family members is reported. The censuses are recorded in intervals of a few years and the information of individuals in each household is quite stable from a point in time to the next one. This redundancy is used to assist the transcriber, so the redundant information is transferred from the census already transcribed to the next one. Household records are aligned from one year to the next one using the knowledge of the ordering by street address. Given an already transcribed census, a query by string word spotting is applied. Thus, names from the census in time t are used as queries in the corresponding home record in time t+1. Since the search is constrained, the obtained precision-recall values are very high, with an important reduction in the transcription time. The proposed system has been tested in a real citizen-science experience where non expert users transcribe the census data of their home town. |
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Santorini; Greece; April 2016 |
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DAG; 603.053; 602.006; 600.061; 600.077; 600.097 |
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Admin @ si @ MFL2016 |
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2751 |
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Juan Ignacio Toledo; Alicia Fornes; Jordi Cucurull; Josep Llados |
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Title |
Election Tally Sheets Processing System |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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364-368 |
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In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost. |
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Santorini; Greece; April 2016 |
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DAG; 602.006; 600.061; 601.225; 600.077; 600.097 |
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TFC2016 |
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2752 |
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Anders Hast; Alicia Fornes |
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A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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150-155 |
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The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results. |
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Santorini; Greece; April 2016 |
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DAG; 602.006; 600.061; 600.077; 600.097 |
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HaF2016 |
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2753 |
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Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Filtrage de descripteurs locaux pour l'amélioration de la détection de documents |
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2016 |
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Colloque International Francophone sur l'Écrit et le Document |
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Local descriptors; mobile capture; document matching; keypoint selection |
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In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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Toulouse; France; March 2016 |
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CIFED |
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DAG; 600.084; 600.077 |
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Admin @ si @ RCO2016 |
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2755 |
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Dimosthenis Karatzas; V. Poulain d'Andecy; Marçal Rusiñol |
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Human-Document Interaction – a new frontier for document image analysis |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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369-374 |
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All indications show that paper documents will not cede in favour of their digital counterparts, but will instead be used increasingly in conjunction with digital information. An open challenge is how to seamlessly link the physical with the digital – how to continue taking advantage of the important affordances of paper, without missing out on digital functionality. This paper
presents the authors’ experience with developing systems for Human-Document Interaction based on augmented document interfaces and examines new challenges and opportunities arising for the document image analysis field in this area. The system presented combines state of the art camera-based document
image analysis techniques with a range of complementary tech-nologies to offer fluid Human-Document Interaction. Both fixed and nomadic setups are discussed that have gone through user testing in real-life environments, and use cases are presented that span the spectrum from business to educational application |
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Santorini; Greece; April 2016 |
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DAS |
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DAG; 600.084; 600.077 |
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KPR2016 |
Serial |
2756 |
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Author |
Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Title |
Delaunay triangulation-based features for Camera-based document image retrieval system |
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Conference Article |
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2016 |
Publication |
12th IAPR Workshop on Document Analysis Systems |
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1-6 |
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Keywords |
Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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Abstract |
In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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Notes |
DAG; 600.061; 600.084; 600.077 |
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no |
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Call Number |
Admin @ si @ BRC2016 |
Serial |
2757 |
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Author |
Marc Masana; Joost Van de Weijer; Andrew Bagdanov |
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Title |
On-the-fly Network pruning for object detection |
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Conference Article |
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Year |
2016 |
Publication |
International conference on learning representations |
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Object detection with deep neural networks is often performed by passing a few
thousand candidate bounding boxes through a deep neural network for each image.
These bounding boxes are highly correlated since they originate from the same
image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result. |
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Address |
Puerto Rico; May 2016 |
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ICLR |
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Notes |
LAMP; 600.068; 600.106; 600.079 |
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no |
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Call Number |
Admin @ si @MWB2016 |
Serial |
2758 |
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Author |
Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer |
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Title |
Does Multimodality Help Human and Machine for Translation and Image Captioning? |
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Conference Article |
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Year |
2016 |
Publication |
1st conference on machine translation |
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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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Berlin; Germany; August 2016 |
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WMT |
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LAMP; 600.106 ; 600.068 |
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no |
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Call Number |
Admin @ si @ CAW2016 |
Serial |
2761 |
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