|
Records |
Links |
|
Author |
Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo |
![goto web page url](img/www.gif)
|
|
Title |
Subspace Procrustes Analysis |
Type |
Journal Article |
|
Year |
2017 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
121 |
Issue |
3 |
Pages |
327–343 |
|
|
Keywords |
|
|
|
Abstract |
Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB; HuPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ PTI2017 |
Serial |
2841 |
|
Permanent link to this record |
|
|
|
|
Author |
Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
![goto web page url](img/www.gif)
|
|
Title |
Looking at People Special Issue |
Type |
Journal Article |
|
Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
126 |
Issue |
2-4 |
Pages |
141-143 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HUPBA; ISE; 600.119 |
Approved |
no |
|
|
Call Number |
Admin @ si @ EGJ2018 |
Serial |
3093 |
|
Permanent link to this record |
|
|
|
|
Author |
Arash Akbarinia; C. Alejandro Parraga |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Feedback and Surround Modulated Boundary Detection |
Type |
Journal Article |
|
Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
126 |
Issue |
12 |
Pages |
1367–1380 |
|
|
Keywords |
Boundary detection; Surround modulation; Biologically-inspired vision |
|
|
Abstract |
Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
NEUROBIT; 600.068; 600.072 |
Approved |
no |
|
|
Call Number |
Admin @ si @ AkP2018b |
Serial |
2991 |
|
Permanent link to this record |
|
|
|
|
Author |
Adrien Gaidon; Antonio Lopez; Florent Perronnin |
![goto web page url](img/www.gif)
|
|
Title |
The Reasonable Effectiveness of Synthetic Visual Data |
Type |
Journal Article |
|
Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
126 |
Issue |
9 |
Pages |
899–901 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GLP2018 |
Serial |
3180 |
|
Permanent link to this record |
|
|
|
|
Author |
Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models |
Type |
Journal Article |
|
Year |
2020 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
128 |
Issue |
|
Pages |
1505–1536 |
|
|
Keywords |
Procedural generation; Human action recognition; Synthetic data; Physics |
|
|
Abstract |
Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SGC2019 |
Serial |
3303 |
|
Permanent link to this record |
|
|
|
|
Author |
Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Slanted Stixels: A way to represent steep streets |
Type |
Journal Article |
|
Year |
2019 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
127 |
Issue |
|
Pages |
1643–1658 |
|
|
Keywords |
|
|
|
Abstract |
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.118; 600.124 |
Approved |
no |
|
|
Call Number |
Admin @ si @ HSC2019 |
Serial |
3304 |
|
Permanent link to this record |
|
|
|
|
Author |
Yunan Li; Jun Wan; Qiguang Miao; Sergio Escalera; Huijuan Fang; Huizhou Chen; Xiangda Qi; Guodong Guo |
![goto web page url](img/www.gif)
|
|
Title |
CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis |
Type |
Journal Article |
|
Year |
2020 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
128 |
Issue |
|
Pages |
2763–2780 |
|
|
Keywords |
|
|
|
Abstract |
First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HuPBA; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ LWM2020 |
Serial |
3413 |
|
Permanent link to this record |
|
|
|
|
Author |
Yaxing Wang; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Mix and match networks: multi-domain alignment for unpaired image-to-image translation |
Type |
Journal Article |
|
Year |
2020 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
128 |
Issue |
|
Pages |
2849–2872 |
|
|
Keywords |
|
|
|
Abstract |
This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). The main challenge lies in the alignment of the latent representations at the bottlenecks of encoder-decoder pairs. We propose an architecture with several tools to encourage alignment, including autoencoders and robust side information and latent consistency losses. We show the benefits of our approach in terms of effectiveness and scalability compared with other pairwise image-to-image translation approaches. We also propose zero-pair cross-modal image translation, a challenging setting where the objective is inferring semantic segmentation from depth (and vice-versa) without explicit segmentation-depth pairs, and only from two (disjoint) segmentation-RGB and depth-RGB training sets. We observe that a certain part of the shared information between unseen modalities might not be reachable, so we further propose a variant that leverages pseudo-pairs which allows us to exploit this shared information between the unseen modalities |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; 600.109; 600.106; 600.141; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WHW2020 |
Serial |
3424 |
|
Permanent link to this record |
|
|
|
|
Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks |
Type |
Journal Article |
|
Year |
2021 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
129 |
Issue |
|
Pages |
2499–2512 |
|
|
Keywords |
|
|
|
Abstract |
In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HUPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ MBE2021 |
Serial |
3654 |
|
Permanent link to this record |
|
|
|
|
Author |
Yaxing Wang; Abel Gonzalez-Garcia; Chenshen Wu; Luis Herranz; Fahad Shahbaz Khan; Shangling Jui; Jian Yang; Joost Van de Weijer |
![goto web page url](img/www.gif)
|
|
Title |
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains |
Type |
Journal Article |
|
Year |
2024 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJCV |
|
|
Volume |
132 |
Issue |
|
Pages |
490–514 |
|
|
Keywords |
|
|
|
Abstract |
Given the often enormous effort required to train GANs, both computationally as well as in dataset collection, the re-use of pretrained GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods, such as mode collapse and lack of flexibility. Furthermore, to prevent overfitting on small target domains, we introduce sparse subnetwork selection, that restricts the set of trainable neurons to those that are relevant for the target dataset. We perform comprehensive experiments on several challenging datasets using various GAN architectures (BigGAN, Progressive GAN, and StyleGAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. MineGAN. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; MACO |
Approved |
no |
|
|
Call Number |
Admin @ si @ WGW2024 |
Serial |
3888 |
|
Permanent link to this record |
|
|
|
|
Author |
Marçal Rusiñol; Josep Llados |
![goto web page url](img/www.gif)
|
|
Title |
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices |
Type |
Journal Article |
|
Year |
2009 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJDAR |
|
|
Volume |
12 |
Issue |
2 |
Pages |
83-96 |
|
|
Keywords |
Performance evaluation; Symbol Spotting; Graphics Recognition |
|
|
Abstract |
Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG |
Approved |
no |
|
|
Call Number |
DAG @ dag @ RuL2009a |
Serial |
1166 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Dimosthenis Karatzas |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model |
Type |
Journal Article |
|
Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJDAR |
|
|
Volume |
13 |
Issue |
3 |
Pages |
229–241 |
|
|
Keywords |
|
|
|
Abstract |
One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; IF 2009: 1,213 |
Approved |
no |
|
|
Call Number |
DAG @ dag @ FLS2010a |
Serial |
1288 |
|
Permanent link to this record |
|
|
|
|
Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Tony Pridmore; Dimosthenis Karatzas |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems |
Type |
Journal Article |
|
Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJDAR |
|
|
Volume |
13 |
Issue |
3 |
Pages |
187-207 |
|
|
Keywords |
|
|
|
Abstract |
This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG |
Approved |
no |
|
|
Call Number |
DAG @ dag @ DVP2010 |
Serial |
1289 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Xavier Otazu; Horst Bunke |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores |
Type |
Journal Article |
|
Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJDAR |
|
|
Volume |
13 |
Issue |
4 |
Pages |
243-259 |
|
|
Keywords |
|
|
|
Abstract |
The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; CAT;CIC |
Approved |
no |
|
|
Call Number |
FLS2010b |
Serial |
1319 |
|
Permanent link to this record |
|
|
|
|
Author |
Lluis Pere de las Heras; Ahmed Sheraz; Marcus Liwicki; Ernest Valveny; Gemma Sanchez |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Statistical Segmentation and Structural Recognition for Floor Plan Interpretation |
Type |
Journal Article |
|
Year |
2014 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](img/sort_asc.gif) |
IJDAR |
|
|
Volume |
17 |
Issue |
3 |
Pages |
221-237 |
|
|
Keywords |
|
|
|
Abstract |
A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; ADAS; 600.076; 600.077 |
Approved |
no |
|
|
Call Number |
HSL2014 |
Serial |
2370 |
|
Permanent link to this record |