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Author  |
E. Provenzi; Carlo Gatta; M. Fierro; A. Rizzi |
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Title |
A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Constant |
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2008 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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30 |
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10 |
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1757–1770 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ PGF2008 |
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1001 |
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Author |
E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara |
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Title |
Benchmarking Keypoint Filtering Approaches for Document Image Matching |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy. |
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Kyoto; Japan; November 2017 |
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ICDAR |
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DAG; 600.084; 600.121 |
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no |
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Admin @ si @ RCR2017 |
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3000 |
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Author |
E. Sanchez |
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Title |
On-line recognition of handwritten symbols |
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2001 |
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CVC Technical Report #52 |
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Admin @ si @ San2001 |
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209 |
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Author |
E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer |
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Title |
Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images |
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2011 |
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13th IEEE International Conference on Computer Vision |
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850-857 |
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Barcelona |
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ICCV |
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MILAB |
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no |
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Call Number |
Admin @ si @ SRL2011 |
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1803 |
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Author |
E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
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Title |
Regularized Clustering for Egocentric Video Segmentation |
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2015 |
Publication |
Pattern Recognition and Image Analysis |
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327-336 |
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Temporal video segmentation ; Egocentric videos ; Clustering |
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In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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Springer International Publishing |
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978-3-319-19390-8 |
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MILAB |
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no |
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Admin @ si @TDB2015a |
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2781 |
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Author |
Edgar Riba |
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Title |
Geometric Computer Vision Techniques for Scene Reconstruction |
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2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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From the early stages of Computer Vision, scene reconstruction has been one of the most studied topics leading to a wide variety of new discoveries and applications. Object grasping and manipulation, localization and mapping, or even visual effect generation are different examples of applications in which scene reconstruction has taken an important role for industries such as robotics, factory automation, or audio visual production. However, scene reconstruction is an extensive topic that can be approached in many different ways with already existing solutions that effectively work in controlled environments. Formally, the problem of scene reconstruction can be formulated as a sequence of independent processes which compose a pipeline. In this thesis, we analyse some parts of the reconstruction pipeline from which we contribute with novel methods using Convolutional Neural Networks (CNN) proposing innovative solutions that consider the optimisation of the methods in an end-to-end fashion. First, we review the state of the art of classical local features detectors and descriptors and contribute with two novel methods that inherently improve pre-existing solutions in the scene reconstruction pipeline.
It is a fact that computer science and software engineering are two fields that usually go hand in hand and evolve according to mutual needs making easier the design of complex and efficient algorithms. For this reason, we contribute with Kornia, a library specifically designed to work with classical computer vision techniques along with deep neural networks. In essence, we created a framework that eases the design of complex pipelines for computer vision algorithms so that can be included within neural networks and be used to backpropagate gradients throw a common optimisation framework. Finally, in the last chapter of this thesis we develop the aforementioned concept of designing end-to-end systems with classical projective geometry. Thus, we contribute with a solution to the problem of synthetic view generation by hallucinating novel views from high deformable cloths objects using a geometry aware end-to-end system. To summarize, in this thesis we demonstrate that with a proper design that combine classical geometric computer vision methods with deep learning techniques can lead to improve pre-existing solutions for the problem of scene reconstruction. |
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February 2021 |
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Ph.D. thesis |
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Editor |
Daniel Ponsa |
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MSIAU |
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no |
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Call Number |
Admin @ si @ Rib2021 |
Serial |
3610 |
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Author |
Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski |
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Title |
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch |
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Conference Article |
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2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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Aspen; Colorado; USA; March 2020 |
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WACV |
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MSIAU; 600.122; 600.130 |
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no |
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Call Number |
Admin @ si @ RMP2020 |
Serial |
3291 |
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Author |
Eduard Vazquez |
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Title |
Distribution Characterization using Topological Features. Application to Colour Image Processing |
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Report |
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2007 |
Publication |
CVC Technical Report #107 |
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CVC (UAB) |
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no |
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Admin @ si @ Vaz2007a |
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823 |
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Author |
Eduard Vazquez |
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Title |
Distribution Characterization using Topological Features. Application to Colour Image Processing |
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2007 |
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CVC Technical Report # 107 |
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Master's thesis |
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no |
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Admin @ si @ Vaz2009 |
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1254 |
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Author |
Eduard Vazquez |
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Title |
Unsupervised image segmentation based on material reflectance description and saliency |
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2011 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show |
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Ph.D. thesis |
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Ramon Baldrich |
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CIC |
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no |
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Admin @ si @ Vaz2011b |
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1835 |
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Author |
Eduard Vazquez; Francesc Tous; Ramon Baldrich; Maria Vanrell |
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Title |
n-Dimensional Distribution Reduction Preserving its Structure |
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2006 |
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Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175 |
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IOS Press |
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CIC |
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no |
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CAT @ cat @ VTB2006a |
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681 |
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Author |
Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich |
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Title |
Image Segmentation in the Presence of Shadows and Highligts |
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Conference Article |
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Year |
2008 |
Publication |
10th European Conference on Computer Vision |
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5305 |
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1–14 |
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Marseille (France) |
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ECCV |
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CAT;CIC |
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CAT @ cat @ VVB2008b |
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1013 |
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Author |
Eduard Vazquez; Maria Vanrell |
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Title |
Eines per al desenvolupament de competencies de enginyeria en un assignatura de Intel·ligencia Artificial |
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Miscellaneous |
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2008 |
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V Jornades d’Innovacio Docent (UAB) |
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Bellaterra (Spain) |
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CIC |
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no |
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CAT @ cat @ VaV2008 |
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1011 |
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Author |
Eduard Vazquez; Ramon Baldrich |
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Title |
Colour Image Segmentation in Presence of Shadows |
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2008 |
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4th European Conference on Colour in Graphics, Imaging and Vision Proceedings |
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383–387 |
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Terrassa (Spain) |
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CGIV08 |
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CAT;CIC |
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no |
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CAT @ cat @ VaB2008 |
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966 |
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Author |
Eduard Vazquez; Ramon Baldrich |
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Non-supervised goodness measure for image segmentation |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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334–335 |
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Gjovik, Norway |
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Medium |
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|
| |
Area |
|
Expedition |
|
Conference |
CREATE |
|
| |
Notes |
CIC |
Approved |
no |
|
| |
Call Number |
CAT @ cat @ VaB2010 |
Serial |
1299 |
|
| Permanent link to this record |
