| Records |
| Author |
Jaume Amores; N. Sebe; Petia Radeva |
Title  |
Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
Type |
Journal Article |
| Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
27 |
Issue |
3 |
Pages |
201–209 |
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ADAS;MILAB |
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no |
| Call Number |
ADAS @ adas @ ASR2006 |
Serial |
643 |
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| Author |
Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
| Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
Type |
Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
30 |
Issue |
15 |
Pages |
1424–1433 |
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| Abstract |
Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. |
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HuPBA; DAG; MILAB |
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no |
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BCNPCL @ bcnpcl @ EFP2009a |
Serial |
1180 |
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| Author |
Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit |
| Title |
Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding |
Type |
Journal Article |
| Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
149 |
Issue |
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Pages |
164-171 |
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| Abstract |
Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. |
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DAG; 600.121 |
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no |
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Admin @ si @ DGV2021 |
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3364 |
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| Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
| Title |
Augmenting Video Surveillance Footage with Virtual Agents for Incremental Event Evaluation |
Type |
Journal Article |
| Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
32 |
Issue |
6 |
Pages |
878–889 |
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The fields of segmentation, tracking and behavior analysis demand for challenging video resources to test, in a scalable manner, complex scenarios like crowded environments or scenes with high semantics. Nevertheless, existing public databases cannot scale the presence of appearing agents, which would be useful to study long-term occlusions and crowds. Moreover, creating these resources is expensive and often too particularized to specific needs. We propose an augmented reality framework to increase the complexity of image sequences in terms of occlusions and crowds, in a scalable and controllable manner. Existing datasets can be increased with augmented sequences containing virtual agents. Such sequences are automatically annotated, thus facilitating evaluation in terms of segmentation, tracking, and behavior recognition. In order to easily specify the desired contents, we propose a natural language interface to convert input sentences into virtual agent behaviors. Experimental tests and validation in indoor, street, and soccer environments are provided to show the feasibility of the proposed approach in terms of robustness, scalability, and semantics. |
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Elsevier |
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ISE |
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no |
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Admin @ si @ FBR2011b |
Serial |
1723 |
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| Author |
A. Sanfeliu; Juan J. Villanueva |
| Title |
An approach of visual motion analysis |
Type |
Journal Article |
| Year |
2005 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
26 |
Issue |
3 |
Pages |
355–368 |
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IF: 1.138 |
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no |
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ISE @ ise @ SaV2005 |
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561 |
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| Author |
Kai Wang; Joost Van de Weijer; Luis Herranz |
| Title |
ACAE-REMIND for online continual learning with compressed feature replay |
Type |
Journal Article |
| Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
150 |
Issue |
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Pages |
122-129 |
| Keywords |
online continual learning; autoencoders; vector quantization |
| Abstract |
Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. |
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LAMP; 600.147; 601.379; 600.120; 600.141 |
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no |
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Admin @ si @ WWH2021 |
Serial |
3575 |
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| Author |
Oriol Ramos Terrades; Ernest Valveny |
| Title |
A new use of the ridgelets transform for describing linear singularities in images |
Type |
Journal Article |
| Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
27 |
Issue |
6 |
Pages |
587–596 |
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DAG |
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no |
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DAG @ dag @ RaV2006a |
Serial |
635 |
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| Author |
Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados |
| Title |
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
136 |
Issue |
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Pages |
219-227 |
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| Abstract |
In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. |
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DAG; 600.140; 601.311; 600.121 |
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no |
| Call Number |
Admin @ si @ CFV2020 |
Serial |
3451 |
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| Author |
Ernest Valveny; Enric Marti |
| Title |
A model for image generation and symbol recognition through the deformation of lineal shapes |
Type |
Journal Article |
| Year |
2003 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
24 |
Issue |
15 |
Pages |
2857-2867 |
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We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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no |
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IAM @ iam @ VAM2003 |
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1653 |
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| Author |
Gemma Sanchez; Josep Llados; K. Tombre |
| Title |
A mean string algorithm to compute the average among a set of 2D shapes |
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Journal Article |
| Year |
2002 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
23 |
Issue |
1-3 |
Pages |
203–214 |
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DAG; IF: 0.409 |
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DAG @ dag @ SLT2002 |
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275 |
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