| Home | [21–30] << 31 32 33 34 35 36 37 38 39 40 >> [41–50] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Carlo Gatta; Francesco Ciompi | ||||
Title  |
Stacked Sequential Scale-Space Taylor Context | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 36 | Issue | 8 | Pages | 1694-1700 |
| Keywords | |||||
| Abstract | We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets. | ||||
| 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 | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; MILAB; 601.160; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ GaC2014 | Serial | 2466 | ||
| Permanent link to this record | |||||
| Author | Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir | ||||
| Title |
Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge | Type | Conference Article | ||
| Year | 2020 | Publication | MYOPS challenge and workshop | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | Virtual; October 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 | MICCAIW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ MAG2020 | Serial | 3518 | ||
| Permanent link to this record | |||||
| Author | Pedro Martins; Paulo Carvalho; Carlo Gatta | ||||
| Title |
Stable Salient Shapes | Type | Conference Article | ||
| Year | 2012 | Publication | International Conference on Digital Image Computing: Techniques and Applications | 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 | DICTA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ MCG2012b | Serial | 2166 | ||
| Permanent link to this record | |||||
| Author | Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title |
Stable Anatomical Structure Tracking for video-bronchoscopy Navigation | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking | ||||
| Abstract | Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at di erent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations. | ||||
| Address | Athens; Greece; October 2016 | ||||
| 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 | MICCAIW | ||
| Notes | IAM; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ LSB2016b | Serial | 2857 | ||
| Permanent link to this record | |||||
| Author | Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title |
Stable Airway Center Tracking for Bronchoscopic Navigation | Type | Conference Article | ||
| Year | 2016 | Publication | 28th Conference of the international Society for Medical Innovation and Technology | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be
matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers from intra‐operative videos based on tracking of maximal stable regions of energy maps. |
||||
| Address | Delft; Rotterdam; Leiden; The Netherlands; October 2016 | ||||
| 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 | SMIT | ||
| Notes | IAM; | Approved | no | ||
| Call Number | Admin @ si @ LSB2016a | Serial | 2856 | ||
| Permanent link to this record | |||||
| Author | Jose Luis Alba; A. Pujol; Juan J. Villanueva | ||||
| Title |
ST-SOM: A Shape+Texture Self Organizing Map. | Type | Miscellaneous | ||
| Year | 2001 | Publication | Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:55–60 | 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 | ISE @ ise @ APV2001b | Serial | 70 | ||
| Permanent link to this record | |||||
| Author | Javier Marin; Sergio Escalera | ||||
| Title |
SSSGAN: Satellite Style and Structure Generative Adversarial Networks | Type | Journal Article | ||
| Year | 2021 | Publication | Remote Sensing | Abbreviated Journal | |
| Volume | 13 | Issue | 19 | Pages | 3984 |
| Keywords | |||||
| Abstract | This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area. |
||||
| 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 @ MaE2021 | Serial | 3651 | ||
| Permanent link to this record | |||||
| Author | Joan Mas; Gemma Sanchez; Josep Llados | ||||
| Title |
SSP: Sketching slide Presentations, a Syntactic Approach | Type | Conference Article | ||
| Year | 2009 | Publication | 8th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | The design of a slide presentation is a creative process. In this process first, humans visualize in their minds what they want to explain. Then, they have to be able to represent this knowledge in an understandable way. There exists a lot of commercial software that allows to create our own slide presentations but the creativity of the user is rather limited. In this article we present an application that allows the user to create and visualize a slide presentation from a sketch. A slide may be seen as a graphical document or a diagram where its elements are placed in a particular spatial arrangement. To describe and recognize slides a syntactic approach is proposed. This approach is based on an Adjacency Grammar and a parsing methodology to cope with this kind of grammars. The experimental evaluation shows the performance of our methodology from a qualitative and a quantitative point of view. Six different slides containing different number of symbols, from 4 to 7, have been given to the users and they have drawn them without restrictions in the order of the elements. The quantitative results give an idea on how suitable is our methodology to describe and recognize the different elements in a slide. | ||||
| Address | La Rochelle; France; July 2009 | ||||
| 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 | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ MSL2009a | Serial | 1441 | ||
| Permanent link to this record | |||||
| Author | Joan Mas; Gemma Sanchez; Josep Llados | ||||
| Title |
SSP: Sketching slide Presentations, a Syntactic Approach | Type | Book Chapter | ||
| Year | 2010 | Publication | Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers | Abbreviated Journal | |
| Volume | 6020 | Issue | Pages | 118-129 | |
| Keywords | |||||
| Abstract | The design of a slide presentation is a creative process. In this process first, humans visualize in their minds what they want to explain. Then, they have to be able to represent this knowledge in an understandable way. There exists a lot of commercial software that allows to create our own slide presentations but the creativity of the user is rather limited. In this article we present an application that allows the user to create and visualize a slide presentation from a sketch. A slide may be seen as a graphical document or a diagram where its elements are placed in a particular spatial arrangement. To describe and recognize slides a syntactic approach is proposed. This approach is based on an Adjacency Grammar and a parsing methodology to cope with this kind of grammars. The experimental evaluation shows the performance of our methodology from a qualitative and a quantitative point of view. Six different slides containing different number of symbols, from 4 to 7, have been given to the users and they have drawn them without restrictions in the order of the elements. The quantitative results give an idea on how suitable is our methodology to describe and recognize the different elements in a slide. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-13727-3 | Medium | |
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | MSL2010 | Serial | 2405 | ||
| Permanent link to this record | |||||
| Author | Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez | ||||
| Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities | Type | Journal Article | ||
| Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 16 | Pages | 4587 |
| Keywords | |||||
| Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. | ||||
| 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 | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSM2020 | Serial | 3452 | ||
| Permanent link to this record | |||||
| 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 | Pages | ||
| Keywords | |||||
| 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. | ||||
| 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 | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ MBC2023 | Serial | 3887 | ||
| Permanent link to this record | |||||
| Author | Simone Balocco; Carlo Gatta; Oriol Pujol; J. Mauri; Petia Radeva | ||||
| Title |
SRBF: Speckle Reducing Bilateral Filtering | Type | Journal Article | ||
| Year | 2010 | Publication | Ultrasound in Medicine and Biology | Abbreviated Journal | UMB |
| Volume | 36 | Issue | 8 | Pages | 1353-1363 |
| Keywords | |||||
| Abstract | Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US). | ||||
| 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;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ BGP2010 | Serial | 1314 | ||
| Permanent link to this record | |||||
| Author | Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva | ||||
| Title |
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation | Type | Journal Article | ||
| Year | 2017 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 155 | Issue | Pages | 55-69 | |
| Keywords | |||||
| Abstract | While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. | ||||
| 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; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ DBT2017 | Serial | 2714 | ||
| Permanent link to this record | |||||
| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title |
Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 156-160 | ||
| Keywords | |||||
| Abstract | This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. | ||||
| 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 | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2014 | Serial | 2543 | ||
| Permanent link to this record | |||||
| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title |
Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary | Type | Book Chapter | ||
| Year | 2016 | Publication | Recent Trends in Image Processing and Pattern Recognition | Abbreviated Journal | |
| Volume | 709 | 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 | RTIP2R | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ HTR2016 | Serial | 2956 | ||
| Permanent link to this record | |||||