Home | [151–160] << 161 162 163 164 165 166 167 168 169 170 >> [171–180] |
Records | |||||
---|---|---|---|---|---|
Author | Eduard Vazquez | ||||
Title | Unsupervised image segmentation based on material reflectance description and saliency | Type | Book Whole | ||
Year | 2011 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | 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 | ||||
Address | |||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Ramon Baldrich | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Vaz2011b | Serial | 1835 | ||
Permanent link to this record | |||||
Author | Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski | ||||
Title | Kornia: an Open Source Differentiable Computer Vision Library for PyTorch | Type | Conference Article | ||
Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Aspen; Colorado; USA; March 2020 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | MSIAU; 600.122; 600.130 | Approved | no | ||
Call Number | Admin @ si @ RMP2020 | Serial | 3291 | ||
Permanent link to this record | |||||
Author | Edgar Riba | ||||
Title | Geometric Computer Vision Techniques for Scene Reconstruction | Type | Book Whole | ||
Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | 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. |
||||
Address | February 2021 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Daniel Ponsa | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ Rib2021 | Serial | 3610 | ||
Permanent link to this record | |||||
Author | E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva | ||||
Title | Regularized Clustering for Egocentric Video Segmentation | Type | Book Chapter | ||
Year | 2015 | Publication | Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | Issue | Pages | 327-336 | ||
Keywords | Temporal video segmentation ; Egocentric videos ; Clustering | ||||
Abstract | 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. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer International Publishing | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-319-19390-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @TDB2015a | Serial | 2781 | ||
Permanent link to this record | |||||
Author | E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer | ||||
Title | Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images | Type | Conference Article | ||
Year | 2011 | Publication | 13th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 850-857 | ||
Keywords | |||||
Abstract | |||||
Address | Barcelona | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCV | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ SRL2011 | Serial | 1803 | ||
Permanent link to this record | |||||
Author | E. Sanchez | ||||
Title | On-line recognition of handwritten symbols | Type | Report | ||
Year | 2001 | Publication | CVC Technical Report #52 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ San2001 | Serial | 209 | ||
Permanent link to this record | |||||
Author | E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara | ||||
Title | Benchmarking Keypoint Filtering Approaches for Document Image Matching | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | 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. |
||||
Address | Kyoto; Japan; November 2017 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.084; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RCR2017 | Serial | 3000 | ||
Permanent link to this record | |||||
Author | E. Provenzi; Carlo Gatta; M. Fierro; A. Rizzi | ||||
Title | A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Constant | Type | Journal | ||
Year | 2008 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 30 | Issue | 10 | Pages | 1757–1770 |
Keywords | |||||
Abstract | |||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ PGF2008 | Serial | 1001 | ||
Permanent link to this record | |||||
Author | E. Pastor; A. Agueda; Juan Andrade; M. Muñoz; Y. Perez; E. Planas | ||||
Title | Computing the rate of spread of linear flame fronts by thermal image processing | Type | Journal | ||
Year | 2006 | Publication | Fire Safety Journal, 41(8):569–579 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ PAA2006 | Serial | 679 | ||
Permanent link to this record | |||||
Author | E. Ceron | ||||
Title | Programacion visual de tareas de pick and place: modulo de vision 3D | Type | Report | ||
Year | 2000 | Publication | CVC Technical Report #47 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | CVC (UAB) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ Cer2000 | Serial | 348 | ||
Permanent link to this record | |||||
Author | E. Bondi ; L. Sidenari; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Real-time people counting from depth imagery of crowded environments | Type | Conference Article | ||
Year | 2014 | Publication | 11th IEEE International Conference on Advanced Video and Signal based Surveillance | Abbreviated Journal | |
Volume | Issue | Pages | 337 - 342 | ||
Keywords | |||||
Abstract | In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications. | ||||
Address | Seoul; Korea; August 2014 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | AVSS | ||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BSB2014 | Serial | 2540 | ||
Permanent link to this record | |||||
Author | E. Barakova; Maya Dimitrova; T. Lorents; Petia Radeva | ||||
Title | The Web as an “Autobiographical Agent” | Type | Miscellaneous | ||
Year | 2004 | Publication | Ch. Bussler and D. Fensel (Eds), Lecture Notes in Artificial Intelligence, vol. 3192, ISBN: 3–540–22959–0, pp. 510–519. | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Springer-Verlag | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ BDL2004 | Serial | 475 | ||
Permanent link to this record | |||||
Author | E Fernandez-Nofrerias; J. Mauri; A. Tovar; L. Cano; E. Martinez; C. Julia; V. Valle; David Rotger; Petia Radeva | ||||
Title | Correspondencia de las imagenes de angiografia y ecografia intracoronaria: La fusion. | Type | Miscellaneous | ||
Year | 2001 | Publication | XXXVII Congreso Nacional de la Sociedad Española de Cardiologia | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Barcelona (Spain) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ FMT2001 | Serial | 91 | ||
Permanent link to this record | |||||
Author | Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu | ||||
Title | NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results | Type | Conference Article | ||
Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
Volume | 191 | Issue | Pages | 24-37 | |
Keywords | |||||
Abstract | We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | PMLR | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ CCB2022 | Serial | 3802 | ||
Permanent link to this record | |||||
Author | Dorota Kaminska; Kadir Aktas; Davit Rizhinashvili; Danila Kuklyanov; Abdallah Hussein Sham; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
Title | Two-stage Recognition and Beyond for Compound Facial Emotion Recognition | Type | Journal Article | ||
Year | 2021 | Publication | Electronics | Abbreviated Journal | ELEC |
Volume | 10 | Issue | 22 | Pages | 2847 |
Keywords | compound emotion recognition; facial expression recognition; dominant and complementary emotion recognition; deep learning | ||||
Abstract | Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ KAR2021 | Serial | 3642 | ||
Permanent link to this record |