| Records |
| Author |
Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa |
| Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2703-2712 |
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| Abstract |
Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($i$) to adaptively extract informative features and learn more expressive spatial context information; ($ii$) to better leverage multi-level representations before up-sampling stage; and ($iii$) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches. |
| Address |
Virtual; January 2021 |
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WACV |
| Notes |
MSIAU; 600.130; 600.122 |
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no |
Call Number  |
Admin @ si @ MAS2021b |
Serial |
3582 |
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| Author |
Judit Martinez; F. Thomas |
| Title |
Efficient Computation of Local Geometric Moments |
Type |
Journal Article |
| Year |
2002 |
Publication |
IEEE Transactions on Image Porcessing, (IF: 2.553) |
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11 |
Issue |
9 |
Pages |
1102-1111 |
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no |
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Admin @ si @ MaT2002 |
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271 |
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| Author |
Jose Marone; Simone Balocco; Marc Bolaños; Jose Massa; Petia Radeva |
| Title |
Learning the Lumen Border using a Convolutional Neural Networks classifier |
Type |
Conference Article |
| Year |
2016 |
Publication |
19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop |
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IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To
solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows. |
| Address |
Athens; Greece; October 2016 |
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MICCAIW |
| Notes |
MILAB; |
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no |
| Call Number |
Admin @ si @ MBB2016 |
Serial |
2822 |
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| Author |
Meysam Madadi; Hugo Bertiche; Wafa Bouzouita; Isabelle Guyon; Sergio Escalera |
| Title |
Learning Cloth Dynamics: 3D+Texture Garment Reconstruction Benchmark |
Type |
Conference Article |
| Year |
2021 |
Publication |
Proceedings of Machine Learning Research |
Abbreviated Journal |
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| Volume |
133 |
Issue |
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Pages |
57-76 |
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| Abstract |
Human avatars are important targets in many computer applications. Accurately tracking, capturing, reconstructing and animating the human body, face and garments in 3D are critical for human-computer interaction, gaming, special effects and virtual reality. In the past, this has required extensive manual animation. Regardless of the advances in human body and face reconstruction, still modeling, learning and analyzing human dynamics need further attention. In this paper we plan to push the research in this direction, e.g. understanding human dynamics in 2D and 3D, with special attention to garments. We provide a large-scale dataset (more than 2M frames) of animated garments with variable topology and type, calledCLOTH3D++. The dataset contains RGBA video sequences paired with its corresponding 3D data. We pay special care to garment dynamics and realistic rendering of RGB data, including lighting, fabric type and texture. With this dataset, we hold a competition at NeurIPS2020. We design three tracks so participants can compete to develop the best method to perform 3D garment reconstruction in a sequence from (1) 3D-to-3D garments, (2) RGB-to-3D garments, and (3) RGB-to-3D garments plus texture. We also provide a baseline method, based on graph convolutional networks, for each track. Baseline results show that there is a lot of room for improvements. However, due to the challenging nature of the problem, no participant could outperform the baselines. |
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| Notes |
HUPBA; no proj |
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no |
| Call Number |
Admin @ si @ MBB2021 |
Serial |
3655 |
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| Author |
Armin Mehri; Parichehr Behjati; Dario Carpio; Angel Sappa |
| Title |
SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
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| Abstract |
Recent breakthroughs in single image super resolution have investigated the potential of deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs based models suffer from their limited fields and their inability to adapt to the input content. Recently, Transformer based models were presented, which demonstrated major performance gains in Natural Language Processing and Vision tasks while mitigating the drawbacks of CNNs. Nevertheless, Transformer computational complexity can increase quadratically for high-resolution images, and the fact that it ignores the original structures of the image by converting them to the 1D structure can make it problematic to capture the local context information and adapt it for real-time applications. In this paper, we present, SRFormer, an efficient yet powerful Transformer-based architecture, by making several key designs in the building of Transformer blocks and Transformer layers that allow us to consider the original structure of the image (i.e., 2D structure) while capturing both local and global dependencies without raising computational demands or memory consumption. We also present a Gated Multi-Layer Perceptron (MLP) Feature Fusion module to aggregate the features of different stages of Transformer blocks by focusing on inter-spatial relationships while adding minor computational costs to the network. We have conducted extensive experiments on several super-resolution benchmark datasets to evaluate our approach. SRFormer demonstrates superior performance compared to state-of-the-art methods from both Transformer and Convolutional networks, with an improvement margin of 0.1∼0.53dB . Furthermore, while SRFormer has almost the same model size, it outperforms SwinIR by 0.47% and inference time by half the time of SwinIR. The code will be available on GitHub. |
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MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ MBC2023 |
Serial |
3887 |
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| Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
| Title |
SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
106 |
Issue |
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Pages |
107472 |
| Keywords |
Deep learning; 3D Human pose; Body shape; SMPL; Denoising autoencoder; Volumetric stack hourglass |
| Abstract |
In this paper we propose to embed SMPL within a deep-based model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 12 mm, respectively. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MBE2020 |
Serial |
3439 |
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| Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
| 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 |
IJCV |
| Volume |
129 |
Issue |
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Pages |
2499–2512 |
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| 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. |
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HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MBE2021 |
Serial |
3654 |
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| Author |
Andreas Møgelmose; Chris Bahnsen; Thomas B. Moeslund; Albert Clapes; Sergio Escalera |
| Title |
Tri-modal Person Re-identification with RGB, Depth and Thermal Features |
Type |
Conference Article |
| Year |
2013 |
Publication |
9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition |
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Issue |
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Pages |
301-307 |
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| Abstract |
Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios. |
| Address |
Portland; oregon; June 2013 |
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978-0-7695-4990-3 |
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Conference |
CVPRW |
| Notes |
HUPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ MBM2013 |
Serial |
2253 |
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| Author |
Subhajit Maity; Sanket Biswas; Siladittya Manna; Ayan Banerjee; Josep Llados; Saumik Bhattacharya; Umapada Pal |
| Title |
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Doccument Analysis and Recognition |
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| Volume |
14187 |
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Pages |
342–360 |
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| Abstract |
Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: this https URL |
| Address |
Document Layout Analysis; Document |
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ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ MBM2023 |
Serial |
3990 |
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| Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
| Title |
Lost in Transcription of Graphic Signs in Ciphers |
Type |
Conference Article |
| Year |
2022 |
Publication |
International Conference on Historical Cryptology (HistoCrypt 2022) |
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Pages |
153-158 |
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transcription of ciphers; hand-written text recognition of symbols; graphic signs |
| Abstract |
Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
| Address |
Amsterdam, Netherlands, June 20-22, 2022 |
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HystoCrypt |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
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Admin @ si @ MBS2022 |
Serial |
3731 |
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| Author |
Armin Mehri; Parichehr Behjati; Angel Sappa |
| Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue |
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Pages |
11529-11540 |
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| Abstract |
Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient. |
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MSIAU |
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no |
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Admin @ si @ MBS2023 |
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3876 |
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| Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
| Title |
Context Aware Keypoint Extraction for Robust Image Representation |
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Conference Article |
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2012 |
Publication |
23rd British Machine Vision Conference |
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100.1 - 100.12 |
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BMVC |
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MILAB |
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no |
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Admin @ si @ MCG2012a |
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2140 |
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| Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
| Title |
Stable Salient Shapes |
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Conference Article |
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2012 |
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International Conference on Digital Image Computing: Techniques and Applications |
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DICTA |
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MILAB |
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no |
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Admin @ si @ MCG2012b |
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2166 |
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| Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
| Title |
Context-aware features and robust image representations |
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Journal Article |
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2014 |
Publication |
Journal of Visual Communication and Image Representation |
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JVCIR |
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25 |
Issue |
2 |
Pages |
339-348 |
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Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation. |
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LAMP; 600.079;MILAB |
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no |
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Admin @ si @ MCG2014 |
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2467 |
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| Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
| Title |
On the completeness of feature-driven maximally stable extremal regions |
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Journal Article |
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2016 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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74 |
Issue |
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9-16 |
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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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LAMP;MILAB; |
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Admin @ si @ MCG2016 |
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2748 |
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