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| Author | Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel | ||||
| Title | A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 465-473 | |
| Keywords | |||||
| Abstract | Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. | ||||
| Address | Povoa de Varzim; Portugal; June 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference  |
ICIAR | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SRR2018a | Serial | 3110 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 353-362 | |
| Keywords | |||||
| Abstract | This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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| Address | Povoa de Varzim; Portugal; June 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference |
ICIAR | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2018c | Serial | 3196 | ||
| Permanent link to this record | |||||
| Author | Rafael E. Rivadeneira; Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Thermal Image SuperResolution Through Deep Convolutional Neural Network | Type | Conference Article | ||
| Year | 2019 | Publication | 16th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 417-426 | ||
| Keywords | |||||
| Abstract | Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a super-resolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process, a new thermal images dataset is used. Different experiments have been carried out, firstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.
cidis.espol.edu.ec/es/dataset. |
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| Address | Waterloo; Canada; August 2019 | ||||
| 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 |
ICIAR | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ RSS2019 | Serial | 3269 | ||
| Permanent link to this record | |||||
| Author | Xose M. Pardo; Petia Radeva; Juan J. Villanueva | ||||
| Title | Self-Training Statistic Snake for Image Segmentation and Tracking. | Type | Miscellaneous | ||
| Year | 1999 | Publication | Proceedings 10th International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 406-411 | ||
| Keywords | |||||
| Abstract | . | ||||
| Address | Venice | ||||
| 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 |
ICIAP | ||
| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ PRV1999 | Serial | 26 | ||
| Permanent link to this record | |||||
| Author | J.R. Serra; J.B. Subirana | ||||
| Title | Adaptive non-cartesian networks for vision. | Type | Conference Article | ||
| Year | 1997 | Publication | Image Analysis and Processing. ICIAP 1997 | Abbreviated Journal | |
| Volume | 1311 | Issue | Pages | ||
| Keywords | |||||
| Abstract | |||||
| Address | Florence | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference |
ICIAP | ||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ SeS1997 | Serial | 212 | ||
| Permanent link to this record | |||||
| Author | Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva | ||||
| Title | Multi-class Binary Symbol Classification with Circular Blurred Shape Models | Type | Conference Article | ||
| Year | 2009 | Publication | 15th International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 5716 | Issue | Pages | 1005–1014 | |
| Keywords | |||||
| Abstract | Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. | ||||
| Address | Salerno, Italy | ||||
| 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 | 978-3-642-04145-7 | Medium | |
| Area | Expedition | Conference |
ICIAP | ||
| Notes | MILAB;HuPBA;DAG | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EFP2009c | Serial | 1186 | ||
| Permanent link to this record | |||||
| Author | L.Tarazon; D. Perez; N. Serrano; V. Alabau; Oriol Ramos Terrades; A. Sanchis; A. Juan | ||||
| Title | Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text | Type | Conference Article | ||
| Year | 2009 | Publication | 15th International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 5716 | Issue | Pages | 567-574 | |
| Keywords | |||||
| Abstract | An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence. | ||||
| Address | Vietri sul Mare, Italy | ||||
| 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 | 978-3-642-04145-7 | Medium | |
| Area | Expedition | Conference |
ICIAP | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ TPS2009 | Serial | 1871 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | 19th international conference on image analysis and processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | CNN in Multispectral Imaging; Image Colorization | ||||
| Abstract | This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results. | ||||
| Address | Catania; Italy; September 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 |
ICIAP | ||
| Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017c | Serial | 3016 | ||
| Permanent link to this record | |||||
| Author | Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera | ||||
| Title | Multi-varied Cumulative Alignment for Domain Adaptation | Type | Conference Article | ||
| Year | 2022 | Publication | 6th International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 13232 | Issue | Pages | 324–334 | |
| Keywords | Domain Adaptation; Computer vision; Neural networks | ||||
| Abstract | Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca). | ||||
| Address | Indonesia; October 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference |
ICIAP | ||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ OAS2022 | Serial | 3777 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Dario Carpio; Angel Sappa | ||||
| Title | A Deep Learning Based Approach for Synthesizing Realistic Depth Maps | Type | Conference Article | ||
| Year | 2023 | Publication | 22nd International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 14234 | Issue | Pages | 369–380 | |
| Keywords | |||||
| Abstract | This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality. | ||||
| Address | Udine; Italia; Setember 2023 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference |
ICIAP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SCS2023a | Serial | 3968 | ||
| Permanent link to this record | |||||
| Author | Partha Pratim Roy; Umapada Pal; Josep Llados | ||||
| Title | Morphology Based Handwritten Line Segmentation using Foreground and Background Information | Type | Conference Article | ||
| Year | 2008 | Publication | International Conference on Frontiers in Handwriting Recognition, | Abbreviated Journal | |
| Volume | Issue | Pages | 241–246 | ||
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| Abstract | |||||
| Address | Montreal (Canada) | ||||
| 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 |
ICFHR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RPL2008a | Serial | 1050 | ||
| Permanent link to this record | |||||
| Author | Jose Antonio Rodriguez; Florent Perronnin | ||||
| Title | Local Gradient Histogram Features for Word Spotting in Unconstrained Handwritten Documents | Type | Conference Article | ||
| Year | 2008 | Publication | International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 7–12 | ||
| Keywords | |||||
| Abstract | |||||
| Address | Montreal (Canada) | ||||
| 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 |
ICFHR | ||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ RoP2008b | Serial | 1066 | ||
| Permanent link to this record | |||||
| Author | Jose Antonio Rodriguez; Florent Perronnin | ||||
| Title | Score Normalization for Hmm-based Word Spotting Using Universal Background Model | Type | Conference Article | ||
| Year | 2008 | Publication | International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 82–87 | ||
| Keywords | |||||
| Abstract | |||||
| Address | Montreal (Canada) | ||||
| 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 |
ICFHR | ||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ RoP2008c | Serial | 1067 | ||
| Permanent link to this record | |||||
| Author | Alicia Fornes; Josep Llados | ||||
| Title | A Symbol-dependent Writer Identifcation Approach in Old Handwritten Music Scores | Type | Conference Article | ||
| Year | 2010 | Publication | 12th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 634 - 639 | ||
| Keywords | |||||
| Abstract | Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we introduce a symbol-dependent approach for identifying the writer of old music scores, which is based on two symbol recognition methods. The main idea is to use the Blurred Shape Model descriptor and a DTW-based method for detecting, recognizing and describing the music clefs and notes. The proposed approach has been evaluated in a database of old music scores, achieving very high writer identification rates. | ||||
| Address | Kolkata (India) | ||||
| 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 | 978-1-4244-8353-2 | Medium | ||
| Area | Expedition | Conference |
ICFHR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ FoL2010 | Serial | 1321 | ||
| Permanent link to this record | |||||
| Author | Jon Almazan; David Fernandez; Alicia Fornes; Josep Llados; Ernest Valveny | ||||
| Title | A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection | Type | Conference Article | ||
| Year | 2012 | Publication | 13th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 453-458 | ||
| Keywords | |||||
| Abstract | In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase. | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4673-2262-1 | Medium | ||
| Area | Expedition | Conference |
ICFHR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ AFF2012 | Serial | 1983 | ||
| Permanent link to this record | |||||