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Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez |
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Title |
Single image super-resolution based on directional variance attention network |
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Journal Article |
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Year |
2023 |
Publication  |
Pattern Recognition |
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PR |
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133 |
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108997 |
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Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. |
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Admin @ si @ BPF2023 |
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3861 |
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Author |
V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich |
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Title |
Adaptive Correlation Filters for Pattern Recognition |
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2006 |
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Pattern Recognition and Image Analysis |
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16 |
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3 |
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425-431 |
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Pattern recognition, Correlation filters, A adaptive filters |
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Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. |
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ISE @ ise @ KMA2006a |
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673 |
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Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez |
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Title |
Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System |
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2009 |
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Pattern Recognition and Image Analysis |
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19 |
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1 |
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165-171 |
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An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path. |
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1054-6618 |
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ISE @ ise @ MAR2009a |
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1160 |
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Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca |
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Title |
Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications |
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2011 |
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Pattern Recognition Letters |
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32 |
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13 |
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1581-1587 |
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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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Admin @ si @ PGB2011a |
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1707 |
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Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Augmenting Video Surveillance Footage with Virtual Agents for Incremental Event Evaluation |
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2011 |
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Pattern Recognition Letters |
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PRL |
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32 |
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6 |
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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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Admin @ si @ FBR2011b |
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1723 |
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