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Author Gemma Sanchez; Josep Llados; K. Tombre
Title (up) A mean string algorithm to compute the average among a set of 2D shapes Type Journal Article
Year 2002 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 23 Issue 1-3 Pages 203–214
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Notes DAG; IF: 0.409 Approved no
Call Number DAG @ dag @ SLT2002 Serial 275
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Author Ernest Valveny; Enric Marti
Title (up) 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
Keywords
Abstract 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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Publisher Elsevier Science Inc. Place of Publication New York, NY, USA Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 0167-8655 ISBN Medium
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Notes DAG; IAM Approved no
Call Number IAM @ iam @ VAM2003 Serial 1653
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Author Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados
Title (up) 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 Pages 219-227
Keywords
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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Notes DAG; 600.140; 601.311; 600.121 Approved no
Call Number Admin @ si @ CFV2020 Serial 3451
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Author Oriol Ramos Terrades; Ernest Valveny
Title (up) 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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Notes DAG Approved no
Call Number DAG @ dag @ RaV2006a Serial 635
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Author Kai Wang; Joost Van de Weijer; Luis Herranz
Title (up) 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 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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Notes LAMP; 600.147; 601.379; 600.120; 600.141 Approved no
Call Number Admin @ si @ WWH2021 Serial 3575
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Author A. Sanfeliu; Juan J. Villanueva
Title (up) 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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Abstract IF: 1.138
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Notes Approved no
Call Number ISE @ ise @ SaV2005 Serial 561
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Author Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez
Title (up) 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
Keywords
Abstract 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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Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
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Notes ISE Approved no
Call Number Admin @ si @ FBR2011b Serial 1723
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Author Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit
Title (up) 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 Pages 164-171
Keywords
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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Notes DAG; 600.121 Approved no
Call Number Admin @ si @ DGV2021 Serial 3364
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Author Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados
Title (up) 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
Keywords
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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Notes HuPBA; DAG; MILAB Approved no
Call Number BCNPCL @ bcnpcl @ EFP2009a Serial 1180
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Author Jaume Amores; N. Sebe; Petia Radeva
Title (up) 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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Notes ADAS;MILAB Approved no
Call Number ADAS @ adas @ ASR2006 Serial 643
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen
Title (up) Compact color texture description for texture classification Type Journal Article
Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 51 Issue Pages 16-22
Keywords
Abstract Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature.
However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This
gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive
evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.
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Notes LAMP; 600.068; 600.079;ADAS Approved no
Call Number Admin @ si @ KRW2015a Serial 2587
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Author Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca
Title (up) Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 32 Issue 13 Pages 1581-1587
Keywords
Abstract Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.

This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one.

In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster.
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Notes ISE Approved no
Call Number Admin @ si @ PGB2011a Serial 1707
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Author David Sanchez-Mendoza; David Masip; Agata Lapedriza
Title (up) Emotion recognition from mid-level features Type Journal Article
Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 67 Issue Part 1 Pages 66–74
Keywords Facial expression; Emotion recognition; Action units; Computer vision
Abstract In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception.
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Publisher Elsevier B.V. Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 0167-8655 ISBN Medium
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Notes OR;MV Approved no
Call Number Admin @ si @ SML2015 Serial 2746
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Author David Guillamet; Jordi Vitria
Title (up) Evaluation of distance metrics for recognition based on non-negative matrix factorization Type Journal Article
Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 24 Issue 9-10 Pages 1599 –1605
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Abstract IF: 0.809
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ GuV2003b Serial 380
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Author Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov
Title (up) Fast: Facilitated and accurate scene text proposals through fcn guided pruning Type Journal Article
Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 119 Issue Pages 112-120
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Abstract Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition.
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Notes DAG; 600.084; 600.121; 600.129 Approved no
Call Number Admin @ si @ BGN2019 Serial 3342
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