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Author |
Carles Sanchez; Debora Gil; R. Tazi; Jorge Bernal; Y. Ruiz; L. Planas; F. Javier Sanchez; Antoni Rosell |
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Title |
Quasi-real time digital assessment of Central Airway Obstruction |
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Conference Article |
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2015 |
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3rd European congress for bronchology and interventional pulmonology ECBIP2015 |
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Barcelona; Spain; April 2015 |
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IAM; MV; 600.075 |
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SGT2015 |
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2612 |
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Hanne Kause; Patricia Marquez; Andrea Fuster; Aura Hernandez-Sabate; Luc Florack; Debora Gil; Hans van Assen |
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Title |
Quality Assessment of Optical Flow in Tagging MRI |
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2015 |
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5th Dutch Bio-Medical Engineering Conference BME2015 |
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The Netherlands; January 2015 |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ KMF2015 |
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2616 |
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Author |
Olivier Lefebvre; Pau Riba; Charles Fournier; Alicia Fornes; Josep Llados; Rejean Plamondon; Jules Gagnon-Marchand |
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Title |
Monitoring neuromotricity on-line: a cloud computing approach |
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Conference Article |
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2015 |
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17th Conference of the International Graphonomics Society IGS2015 |
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The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain. |
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Pointe-à-Pitre; Guadeloupe; June 2015 |
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IGS |
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DAG; 600.077 |
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no |
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Admin @ si @ LRF2015 |
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2617 |
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Author |
Jorge Bernal; F. Javier Sanchez; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach |
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Title |
Bulding up the future of colonoscopy: A synergy between clinicians and computer scientists |
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2015 |
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Colonoscopy and Colorectal Cancer |
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Intelligent systems; Image properties; Validation; Clinical drawbacks; Endoluminal scene description |
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Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly. |
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978-953-51-2225-8 |
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MV |
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no |
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Admin @ si @ BSR2015 |
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2624 |
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Author |
Youssef El Rhabi; Simon Loic; Brun Luc |
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Title |
Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel |
Type |
Conference Article |
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Year |
2015 |
Publication |
15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 |
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Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration |
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Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data. |
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Amiens; France; June 2015 |
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ORASIS |
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DAG; 600.077 |
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no |
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Admin @ si @ RLL2015 |
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2626 |
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Author |
Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika |
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Title |
Multi-observation Face Recognition in Videos based on Label Propagation |
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Conference Article |
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Year |
2015 |
Publication |
6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 |
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10-17 |
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In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the
selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video
sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods. |
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Boston; USA; June 2015 |
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CVPRW |
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Notes |
LAMP; 600.068; 600.072; |
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no |
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Call Number |
Admin @ si @ RBD2015 |
Serial |
2627 |
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Author |
M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Cross-spectral image registration and fusion: an evaluation study |
Type |
Conference Article |
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Year |
2015 |
Publication |
2nd International Conference on Machine Vision and Machine Learning |
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multispectral imaging; image registration; data fusion; infrared and visible spectra |
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This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Barcelona; July 2015 |
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MVML |
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ADAS; 600.076 |
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no |
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Admin @ si @ CAV2015 |
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2629 |
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Author |
Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo |
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Title |
LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations |
Type |
Conference Article |
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Year |
2015 |
Publication |
22th IEEE International Conference on Image Processing |
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178 - 181 |
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Quebec; Canada; September 2015 |
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ICIP |
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ADAS; 600.076 |
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Admin @ si @ AST2015 |
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2630 |
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Author |
Jiaolong Xu |
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Title |
Domain Adaptation of Deformable Part-based Models |
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2015 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
On-board pedestrian detection is crucial for Advanced Driver Assistance Systems
(ADAS). An accurate classication is fundamental for vision-based pedestrian detection.
The underlying assumption for learning classiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classiers. However, in practice, there are dierent reasons that can break this constancy assumption. Accordingly, reusing existing classiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset.
We start by exploiting the maximum detection accuracy of the virtual-world
trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still suer from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require
revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area. |
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April 2015 |
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Ph.D. thesis |
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Antonio Lopez |
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978-84-943427-1-4 |
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ADAS; 600.076 |
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no |
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Admin @ si @ Xu2015 |
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2631 |
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Author |
Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris |
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Title |
Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code |
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2015 |
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European Conference on Visual Perception ECVP2015 |
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Liverpool; uk; August 2015 |
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ECVP |
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NEUROBIT; |
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Admin @ si @ POW2015 |
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2633 |
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Author |
Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort |
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2015 |
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Barcelona Computational, Cognitive and Systems Neuroscience |
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Barcelona; June 2015 |
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BARCCSYN |
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NEUROBIT; |
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Admin @ si @ OPC2015b |
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2634 |
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Author |
Santiago Segui; Oriol Pujol; Jordi Vitria |
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Title |
Learning to count with deep object features |
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Conference Article |
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2015 |
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Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop |
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90-96 |
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Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation.
To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training.
We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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Boston; USA; June 2015 |
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MILAB; HuPBA; OR;MV |
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no |
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Admin @ si @ SPV2015 |
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2636 |
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Author |
Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva |
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Title |
Visual Summary of Egocentric Photostreams by Representative Keyframes |
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Conference Article |
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2015 |
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IEEE International Conference on Multimedia and Expo ICMEW2015 |
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1-6 |
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egocentric; lifelogging; summarization; keyframes |
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Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries. |
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Torino; italy; July 2015 |
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978-1-4799-7079-7 |
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978-1-4799-7079-7 |
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ICME |
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MILAB |
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no |
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Admin @ si @ BMT2015 |
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2638 |
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Author |
Nuria Cirera; Alicia Fornes; Josep Llados |
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Hidden Markov model topology optimization for handwriting recognition |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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626-630 |
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In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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Nancy; France; August 2015 |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ CFL2015 |
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2639 |
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Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados |
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Document Analysis Techniques for Automatic Electoral Document Processing: A Survey |
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2015 |
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E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 |
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139-141 |
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Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally |
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In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. |
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Bern; Switzerland; September 2015 |
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VoteID |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ TCP2015 |
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2641 |
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