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Author |
Eduardo Aguilar; Petia Radeva |
![goto web page url](img/www.gif)
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Title |
Uncertainty-aware integration of local and flat classifiers for food recognition |
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Journal Article |
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2020 |
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Pattern Recognition Letters |
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PRL |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
136 |
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237-243 |
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Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen. |
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MILAB; no proj |
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Admin @ si @ AgR2020 |
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3525 |
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F.X. Perez; F. Javier Sanchez; Xavier Binefa; Xavier Roca; Jordi Vitria; Juan J. Villanueva |
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A mathematical morphology-based system for IC´s inspection and analysis. |
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Conference Article |
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1993 |
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Institute of Physics Conferences Series |
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135 |
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10 |
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381–384 |
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Bristol |
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Institute of Physics |
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MV;OR;ISE |
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BCNPCL @ bcnpcl @ PSB1993; IAM @ iam @ PSB1993 |
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150 |
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X. Binefa; F. Javier Sanchez; F.X. Perez; Xavier Roca; Jordi Vitria; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Using defocus in optical inspection of integrated circuits |
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Conference Article |
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1993 |
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Institute of Physics Conferences Series |
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135 |
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10 |
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389-392 |
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Bristol |
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Institute of Physics |
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MV;OR;ISE |
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BCNPCL @ bcnpcl @ BSP1993; IAM @ iam @ BSP1993 |
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151 |
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Carolina Malagelada; Fosca De Iorio; Fernando Azpiroz; Anna Accarino; Santiago Segui; Petia Radeva; Juan R. Malagelada |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
New Insight Into Intestinal Motor Function via Noninvasive Endoluminal Image Analysis |
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2008 |
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Gastroenterology |
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135 |
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4 |
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1155–1162 |
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MILAB |
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BCNPCL @ bcnpcl @ MDA2008 |
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1040 |
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Author |
Olivier Penacchio |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Relative Density of L, M, S photoreceptors in the Human Retina |
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Report |
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2009 |
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CVC Technical Report |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
135 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Pen2009 |
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2394 |
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Zhengying Liu; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Towards automated computer vision: analysis of the AutoCV challenges 2019 |
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Journal Article |
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2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
135 |
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196-203 |
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Computer vision; AutoML; Deep learning |
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We present the results of recent challenges in Automated Computer Vision (AutoCV, renamed here for clarity AutoCV1 and AutoCV2, 2019), which are part of a series of challenge on Automated Deep Learning (AutoDL). These two competitions aim at searching for fully automated solutions for classification tasks in computer vision, with an emphasis on any-time performance. The first competition was limited to image classification while the second one included both images and videos. Our design imposed to the participants to submit their code on a challenge platform for blind testing on five datasets, both for training and testing, without any human intervention whatsoever. Winning solutions adopted deep learning techniques based on already published architectures, such as AutoAugment, MobileNet and ResNet, to reach state-of-the-art performance in the time budget of the challenge (only 20 minutes of GPU time). The novel contributions include strategies to deliver good preliminary results at any time during the learning process, such that a method can be stopped early and still deliver good performance. This feature is key for the adoption of such techniques by data analysts desiring to obtain rapidly preliminary results on large datasets and to speed up the development process. The soundness of our design was verified in several aspects: (1) Little overfitting of the on-line leaderboard providing feedback on 5 development datasets was observed, compared to the final blind testing on the 5 (separate) final test datasets, suggesting that winning solutions might generalize to other computer vision classification tasks; (2) Error bars on the winners’ performance allow us to say with confident that they performed significantly better than the baseline solutions we provided; (3) The ranking of participants according to the any-time metric we designed, namely the Area under the Learning Curve, was different from that of the fixed-time metric, i.e. AUC at the end of the fixed time budget. We released all winning solutions under open-source licenses. At the end of the AutoDL challenge series, all data of the challenge will be made publicly available, thus providing a collection of uniformly formatted datasets, which can serve to conduct further research, particularly on meta-learning. |
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HuPBA; no proj |
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no |
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Call Number |
Admin @ si @ LXE2020 |
Serial |
3427 |
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Permanent link to this record |
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Author |
Juan Diego Gomez |
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Title |
Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography |
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Report |
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2009 |
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CVC Technical Report |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
134 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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MILAB |
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no |
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Admin @ si @ Gom2009 |
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2393 |
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Author |
Sumit K. Banchhor; Tadashi Araki; Narendra D. Londhe; Nobutaka Ikeda; Petia Radeva; Ayman El-Baz; Luca Saba; Andrew Nicolaides; Shoaib Shafique; John R. Laird; Jasjit S. Suri |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach |
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Journal Article |
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2016 |
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Computer Methods and Programs in Biomedicine |
Abbreviated Journal |
CMPB |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
134 |
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237-258 |
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BACKGROUND AND OBJECTIVE:
Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames.
METHODS:
This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio.
RESULTS:
Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings.
CONCLUSIONS:
We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance. |
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MILAB; |
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Admin @ si @ BAL2016 |
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2830 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
![goto web page url](img/www.gif)
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Title |
Hierarchical graphs for coarse-to-fine error tolerant matching |
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Journal Article |
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2020 |
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Pattern Recognition Letters |
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PRL |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
134 |
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116-124 |
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Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval |
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During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). |
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DAG; 600.097; 601.302; 603.057; 600.140; 600.121 |
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Admin @ si @ RLF2020 |
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3349 |
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Author |
David Augusto Rojas |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Colouring Local Feature Detection for Matching |
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2009 |
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CVC Technical Report |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Roj2009 |
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2392 |
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Meysam Madadi; Hugo Bertiche; Wafa Bouzouita; Isabelle Guyon; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Learning Cloth Dynamics: 3D+Texture Garment Reconstruction Benchmark |
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Conference Article |
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2021 |
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Proceedings of Machine Learning Research |
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133 |
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57-76 |
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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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HUPBA; no proj |
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Admin @ si @ MBB2021 |
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3655 |
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Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez |
![goto web page url](img/www.gif)
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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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2023 |
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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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Josep M. Gonfaus |
![find record details (via OpenURL) openurl](img/xref.gif)
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Semantic Segmentation of Images Using Random Ferns |
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2009 |
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CVC Technical Report |
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132 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Gon2009 |
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2391 |
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Author |
Yaxing Wang; Abel Gonzalez-Garcia; Chenshen Wu; Luis Herranz; Fahad Shahbaz Khan; Shangling Jui; Jian Yang; Joost Van de Weijer |
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Title |
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains |
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Journal Article |
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2024 |
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International Journal of Computer Vision |
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IJCV |
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132 |
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490–514 |
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Given the often enormous effort required to train GANs, both computationally as well as in dataset collection, the re-use of pretrained GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods, such as mode collapse and lack of flexibility. Furthermore, to prevent overfitting on small target domains, we introduce sparse subnetwork selection, that restricts the set of trainable neurons to those that are relevant for the target dataset. We perform comprehensive experiments on several challenging datasets using various GAN architectures (BigGAN, Progressive GAN, and StyleGAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. MineGAN. |
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LAMP; MACO |
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Admin @ si @ WGW2024 |
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3888 |
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Author |
Naila Murray |
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Title |
Perceptual Feature Detection |
Type |
Report |
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2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
131 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC |
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Admin @ si @ Mur2009 |
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2390 |
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