| Records |
| 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 |
| Keywords |
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| 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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Elsevier |
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ISE |
Approved |
no |
| Call Number |
Admin @ si @ FBR2011b |
Serial |
1723 |
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| Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol |
| Title |
Minimal Design of Error-Correcting Output Codes |
Type |
Journal Article |
| Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
33 |
Issue |
6 |
Pages |
693-702 |
| Keywords |
Multi-class classification; Error-correcting output codes; Ensemble of classifiers |
| Abstract |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Elsevier |
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| ISSN |
0167-8655 |
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| Notes |
MILAB; OR;HuPBA;MV |
Approved |
no |
| Call Number |
Admin @ si @ BEB2011a |
Serial |
1800 |
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| Author |
Albert Clapes; Miguel Reyes; Sergio Escalera |
| Title |
Multi-modal User Identification and Object Recognition Surveillance System |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
34 |
Issue |
7 |
Pages |
799-808 |
| Keywords |
Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning |
| Abstract |
We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. |
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Elsevier |
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HUPBA; 600.046; 605.203;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ CRE2013 |
Serial |
2248 |
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| Author |
Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi |
| Title |
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
160 |
Issue |
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Pages |
43-49 |
| Keywords |
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| Abstract |
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching |
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Elsevier |
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| Notes |
DAG; 600.121; 600.162; 602.230 |
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no |
| Call Number |
Admin @ si @ SFK2022 |
Serial |
3736 |
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| Author |
David Sanchez-Mendoza; David Masip; Agata Lapedriza |
| Title |
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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Elsevier B.V. |
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0167-8655 |
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| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ SML2015 |
Serial |
2746 |
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| Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
| Title |
On the completeness of feature-driven maximally stable extremal regions |
Type |
Journal Article |
| Year |
2016 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
74 |
Issue |
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Pages |
9-16 |
| Keywords |
Local features; Completeness; Maximally Stable Extremal Regions |
| Abstract |
By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. |
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Elsevier B.V. |
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0167-8655 |
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| Notes |
LAMP;MILAB; |
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no |
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Admin @ si @ MCG2016 |
Serial |
2748 |
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| Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
| Title |
Median graph: A new exact algorithm using a distance based on the maximum common subgraph |
Type |
Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
30 |
Issue |
5 |
Pages |
579–588 |
| Keywords |
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| Abstract |
Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems. |
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Elsevier Science Inc. |
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0167-8655 |
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DAG |
Approved |
no |
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DAG @ dag @ FVS2009a |
Serial |
1114 |
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| Author |
Fadi Dornaika; Angel Sappa |
| Title |
Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression |
Type |
Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
30 |
Issue |
5 |
Pages |
535–543 |
| Keywords |
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| Abstract |
This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes. |
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Elsevier Science Inc. |
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0167-8655 |
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ADAS |
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no |
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ADAS @ adas @ DoS2009a |
Serial |
1115 |
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| Author |
Debora Gil; Petia Radeva |
| Title |
Inhibition of false landmarks |
Type |
Journal Article |
| Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
27 |
Issue |
9 |
Pages |
1022-1030 |
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| Abstract |
Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator. |
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Elsevier Science Inc. |
Place of Publication |
New York, NY, USA |
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0167-8655 |
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IAM;MILAB |
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no |
| Call Number |
IAM @ iam @ GiR2006 |
Serial |
1529 |
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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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| 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 |
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0167-8655 |
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| Notes |
DAG; IAM |
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no |
| Call Number |
IAM @ iam @ VAM2003 |
Serial |
1653 |
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