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| Author |
J.L. Pech-Pacheco; J. Alvarez-Borrego; Gabriel Cristobal; Matthias S. Keil |
| Title |
Automatic object identification irrespective to geometric changes |
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Journal |
| Year |
2003 |
Publication  |
Optical Engineering, 42(2): 551–559 (IF: 0.877) |
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Admin @ si @ PAC2003 |
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632 |
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| Author |
Mikhail Mozerov; V. Kober |
| Title |
Impulse Noise Removal with Gradient Adaptive Neighborhoods |
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| Year |
2006 |
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Optical Engineering, 45: 67003 |
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ISE |
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ISE @ ise @ MoK2006 |
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676 |
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| Author |
Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez |
| Title |
Trajectory Occlusion Handling with Multiple View Distance Minimisation Clustering |
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Journal |
| Year |
2008 |
Publication |
Optical Engineering, vol. 47(04)04702, DOI:10.11781.2909665 |
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ISE |
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ISE @ ise @ MAR2008c |
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970 |
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| Author |
Javier Varona; Jordi Gonzalez; Ignasi Rius; Juan J. Villanueva |
| Title |
Importance of Detection for Video Surveillance Applications |
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Journal |
| Year |
2008 |
Publication |
Optical Engineering, vol. 47(8), 087201/1–9 |
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ISE @ ise @ VGR2008 |
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998 |
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| Author |
Xavier Roca; X. Binefa; Jordi Vitria |
| Title |
A New Autofocus Algorithm for Cytological Tissue in a Microscopy Environment. |
Type |
Miscellaneous |
| Year |
1998 |
Publication |
Optical Engineering. |
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OR;ISE;MV |
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no |
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BCNPCL @ bcnpcl @ RBV1998 |
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16 |
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| Author |
Javier Vazquez; Graham D. Finlayson; Luis Herranz |
| Title |
Improving the perception of low-light enhanced images |
Type |
Journal Article |
| Year |
2024 |
Publication |
Optics Express |
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| Volume |
32 |
Issue |
4 |
Pages |
5174-5190 |
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| Abstract |
Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.e. perceptual quality). As an example, the perception-distortion trade-off states that, close to the optimal results, improving distortion results in worsening perception. This means that current low-light image enhancement methods —that focus on distortion minimization— cannot be optimal in the sense of obtaining a good image in terms of perception errors. In this paper, we propose a post-processing approach in which, given the original low-light image and the result of a specific method, we are able to obtain a result that resembles as much as possible that of the original method, but, at the same time, giving an improvement in the perception of the final image. More in detail, our method follows the hypothesis that in order to minimally modify the perception of an input image, any modification should be a combination of a local change in the shading across a scene and a global change in illumination color. We demonstrate the ability of our method quantitatively using perceptual blind image metrics such as BRISQUE, NIQE, or UNIQUE, and through user preference tests. |
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MACO |
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no |
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Admin @ si @ VFH2024 |
Serial |
4018 |
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| Author |
M.J. Yzuel; J. Pladellorens; Joan Serrat; A. Dupuy |
| Title |
Application restauration and edge detection techniques in the calculation of left ventricular volumes. |
Type |
Conference Article |
| Year |
1993 |
Publication |
Optics in Medicine, Biology and Environmental Research : Selected contributions to the first International Conference on Optics within Life Sciences (OWLS I) |
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374-375 |
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Elsevier |
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ADAS |
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no |
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ADAS @ adas @ YPS1993 |
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244 |
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| Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
| Title |
Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy |
Type |
Conference Article |
| Year |
2018 |
Publication |
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
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11041 |
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Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification |
| Abstract |
Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems. |
| Address |
Granada; September 2018 |
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MICCAIW |
| Notes |
IAM; 600.096; 600.075; 601.323; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ RSB2018b |
Serial |
3137 |
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| Author |
S.Grau; Ana Puig; Sergio Escalera; Maria Salamo |
| Title |
Intelligent Interactive Volume Classification |
Type |
Conference Article |
| Year |
2013 |
Publication |
Pacific Graphics |
Abbreviated Journal |
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| Volume |
32 |
Issue |
7 |
Pages |
23-28 |
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| Abstract |
This paper defines an intelligent and interactive framework to classify multiple regions of interest from the original data on demand, without requiring any preprocessing or previous segmentation. The proposed intelligent and interactive approach is divided in three stages: visualize, training and testing. First, users visualize and label some samples directly on slices of the volume. Training and testing are based on a framework of Error Correcting Output Codes and Adaboost classifiers that learn to classify each region the user has painted. Later, at the testing stage, each classifier is directly applied on the rest of samples and combined to perform multi-class labeling, being used in the final rendering. We also parallelized the training stage using a GPU-based implementation for
obtaining a rapid interaction and classification. |
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ISBN |
978-3-905674-50-7 |
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PG |
| Notes |
HuPBA; 600.046;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ GPE2013b |
Serial |
2355 |
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| Author |
Bogdan Raducanu; Jordi Vitria |
| Title |
Learning to Learn: From Smarts Machines to Intelligent Machines |
Type |
Journal |
| Year |
2008 |
Publication |
Patter Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
29 |
Issue |
8 |
Pages |
1024–1032 |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ RaV2008a |
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950 |
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| Author |
H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena |
| Title |
Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior |
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Journal Article |
| Year |
2016 |
Publication |
Patter Recognition Letters |
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PRL |
| Volume |
76 |
Issue |
1 |
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76-82 |
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IAM; 600.060 |
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no |
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Admin @ si @ MFV2017b |
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2941 |
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| Author |
Marçal Rusiñol; Josep Llados; Gemma Sanchez |
| Title |
Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings |
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Journal Article |
| Year |
2010 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
| Volume |
13 |
Issue |
3 |
Pages |
321-331 |
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| Abstract |
In this paper, we address the problem of symbol spotting in technical document images applied to scanned and vectorized line drawings. Like any information spotting architecture, our approach has two components. First, symbols are decomposed in primitives which are compactly represented and second a primitive indexing structure aims to efficiently retrieve similar primitives. Primitives are encoded in terms of attributed strings representing closed regions. Similar strings are clustered in a lookup table so that the set median strings act as indexing keys. A voting scheme formulates hypothesis in certain locations of the line drawing image where there is a high presence of regions similar to the queried ones, and therefore, a high probability to find the queried graphical symbol. The proposed approach is illustrated in a framework consisting in spotting furniture symbols in architectural drawings. It has been proved to work even in the presence of noise and distortion introduced by the scanning and raster-to-vector processes. |
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Springer-Verlag |
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1433-7541 |
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DAG |
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no |
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DAG @ dag @ RLS2010 |
Serial |
1165 |
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| Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
| Title |
Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification |
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Journal Article |
| Year |
2015 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
| Volume |
18 |
Issue |
2 |
Pages |
247-261 |
| Keywords |
Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification |
| Abstract |
In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. |
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Springer-Verlag |
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1433-7541 |
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HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ PEP2013 |
Serial |
2251 |
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| Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
| Title |
Combining Local and Global Learners in the Pairwise Multiclass Classification |
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Journal Article |
| Year |
2015 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
| Volume |
18 |
Issue |
4 |
Pages |
845-860 |
| Keywords |
Multiclass classification; Pairwise approach; One-versus-one |
| Abstract |
Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. |
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Springer London |
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1433-7541 |
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HuPBA;MILAB |
Approved |
no |
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Admin @ si @ BGE2014 |
Serial |
2441 |
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| Author |
Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli |
| Title |
Batch-based activity recognition from egocentric photo-streams revisited |
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Journal Article |
| Year |
2018 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
| Volume |
21 |
Issue |
4 |
Pages |
953–965 |
| Keywords |
Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks |
| Abstract |
Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CMR2018 |
Serial |
3186 |
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