@article {aCumplido-Mayoral22, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration stratified by sex}, journal = {eLife}, volume = {12}, year = {2023}, month = {04/2023}, abstract = {

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer9s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.

}, issn = {2050-084X}, doi = {https://doi.org/10.7554/eLife.81067}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Gregory Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Luigi Lorenzini and Carolina Minguillon and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @conference {cCumplido-Mayoral23a, title = {Brain-age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum}, booktitle = {Alzheimer{\textquoteright}s Association International Conference (AAIC)}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serrat and Gonzalo S{\'a}nchez-Benavides and Armand Gonz{\'a}lez-Escalante and Federica Anastasi and Marta Mila-Aloma and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Carolina Minguillon and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral23, title = {Brain-age prediction and its associations with glial and synaptic CSF markers}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Karine Fauria and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2022}, month = {07/2022}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Greg Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral22, title = {Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer{\textquoteright}s disease and neurodegeneration stratified by sex}, booktitle = {15th Clinical Trials on Alzheimer{\textquoteright}s Disease Conference (CTAD)}, year = {2022}, month = {11/2022}, address = {San Francisco, USA}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Luigi Lorenzini and Alle Meije Wink and H. Mutsaerts and Sven Haller and Ga{\"e}l Chetelat and Frederik Barkhof and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cMora20, title = {Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation}, booktitle = {MICCAI 2020 - Brain Lesion Workshop (BrainLes), Multimodal Brain Tumor Segmentation Challenge (BRATS)}, year = {2020}, month = {10/2020}, abstract = {

Automation of brain tumors in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is specially critical in medical diagnosis. This work proposes a 3D encoder-decoder architecture, based on V-Net which is trained with patching techniques to reduce memory consumption and decrease the effect of unbalanced data. We also introduce voxel-wise uncertainty, both epistemic and aleatoric using test-time dropout and data-augmentation respectively. Uncertainty maps can provide extra information to expert neurologists, useful for detecting when the model is not confident on the provided segmentation.

}, author = {Laura Mora and Ver{\'o}nica Vilaplana} } @conference {cCombaliac, title = {BCN20000: Dermoscopic Lesions in the Wild}, booktitle = {International Skin Imaging Collaboration (ISIC) Challenge on Dermoscopic Skin Lesion Analysis 2019}, year = {2019}, month = {10/2019}, abstract = {

This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Cl{\'\i}nic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019 \cite{ISIC2019}, where they will be asked to train algorithms to classify dermoscopic images of skin cancer automatically.

}, author = {Marc Combalia and Noel C. F. Codella and Veronica Rotemberg and Brian Helba and Ver{\'o}nica Vilaplana and Ofer Reiter and Cristina Carrera and Alicia Barreiro and Allan C. Halpern and Susana Puig and Josep Malvehy} } @article {aWang19, title = {Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {2019/2/27}, abstract = {

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

}, doi = {10.1109/TMI.2019.2901712}, author = {Li Wang and Dong Nie and Guannan Li and Elodie Puybareau and Jose Dolz and Qian Zhang and Fan Wang and Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-HanThung and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao Xu and Pim Moeskops and Josien Pluim and Christian Desrosiers and Ismail Ben Ayed and Gerard Sanroma and Oualid Benkarim and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Weili Lin and Gang Li and Dinggang Shen} } @conference {cBellverb, title = {Budget-aware Semi-Supervised Semantic and Instance Segmentation}, booktitle = {CVPR 2019 DeepVision Workshop}, year = {2019}, month = {05/2019}, publisher = {OpenCVF}, organization = {OpenCVF}, address = {Long Beach, CA, USA}, abstract = {

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.

Winners of the best paper award at the $\#$CVPR2019 DeepVision workshop

Budget-aware Semi-Supervised Semantic and Instance Segmentation from Universitat Polit{\`e}cnica de Catalunya
}, url = {https://arxiv.org/abs/1905.05880}, author = {M{\'\i}riam Bellver and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xRoca, title = {Block-based Speech-to-Speech Translation}, year = {2018}, abstract = {

This bachelor{\textquoteright}s thesis explores different ways of building a block-based Speech Translation system with the aim of generating huge amounts of parallel speech data. The first goal is to research and manage to run suitable tools to implement each one of the three blocks that integrates the Speech Translation system: Speech Recognition, Translation and Speech Synthesis. We experiment with some open-source toolkits and we manage to train a speech recognition system and a neural machine translation system. Then, we test them to evaluate their performance. As an alternative option, we use the cloud computing solutions provided by Google Cloud to implement the three sequential blocks and we successfully build the overall system. Finally, we make a comparative study between an in-house software development versus Cloud computing implementation.

Block-based Speech to Speech Translation from Universitat Polit{\`e}cnica de Catalunya
}, author = {Roca, Sandra}, editor = {Amanda Duarte and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xRosello18, title = {Brain lesion segmentation using Convolutional Neuronal Networks}, year = {2018}, abstract = {

Convolutional neural networks (CNN) are powerful tools for learning representations from images. They are being used in a large range of applications, being the state-of-the art in many computer vision tasks. In this work, we study the brain tumor segmentation problem using CNNs and the publicly available BraTS dataset. One of the key factors for this task is which training scheme is used since it should deal with memory constraints and should alleviate the high-imbalance nature between healthy and lesion tissue in the brain.

Thus, the purpose of this project is to propose a comparison between several training schemes and extensively analyze and evaluate them in terms of the dice score. We evaluate densetraining against patch-sampling, and particularly, xed-rule against adaptive sampling scheme. Furthermore, variants and modications of the existing training schemes have been proposed in order to enhance their performance. Finally, several loss functions for each training scheme have been analyzed.

}, author = {Clara Bon{\'\i}n Rosell{\'o}}, editor = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana} } @conference {cSancheza, title = {Brain MRI Super-Resolution using Generative Adversarial Networks}, booktitle = {International Conference on Medical Imaging with Deep Learning}, year = {2018}, month = {07/2018}, address = {Amsterdam, The Netherlands}, author = {Irina S{\'a}nchez and Ver{\'o}nica Vilaplana} } @conference {cMohedanoa, title = {Bags of Local Convolutional Features for Scalable Instance Search}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)}, year = {2016}, month = {06/2016}, publisher = {ACM}, organization = {ACM}, address = {New York City, NY; USA}, abstract = {

Image representations extracted from convolutional neural networks (CNNs) have been shown to outperform hand-crafted features in multiple computer vision tasks, such as visual image retrieval. This work proposes a simple pipeline for encoding the local activations of a convolutional layer of a pre-trained CNN using the well-known bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an \textit{assignment map}, a compact representation that relates regions of an image with a visual word. We use the assignment map for fast spatial reranking, obtaining object localizations that are used for query expansion. We demonstrate the suitability of the Bag of Words representation based on local CNN features for image retrieval, achieving state-of-the-art performance on the Oxford and Paris buildings benchmarks. We show that our proposed system for CNN feature aggregation with BoW outperforms state-of-the-art techniques using sum pooling at a subset of the challenging TRECVid INS benchmark.

Best poster award at ACM ICMR 2016

Preprint on arXiv

Project page with source code

Post on GitXiv

Overall acceptance rate in ICMR 2016: 30\%\ 

2016-05-Seminar-AmaiaSalvador-DeepVision from Image Processing Group on Vimeo.

Convolutional Features for Instance Search from Xavier Giro
}, keywords = {Bag of Words, Convolutional Neural Networks, Instance Retrieval}, isbn = {978-1-4503-4359-6}, doi = {http://dx.doi.org/10.1145/2911996.2912061}, url = {http://dx.doi.org/10.1145/2911996.2912061}, author = {Mohedano, Eva and Amaia Salvador and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Marqu{\'e}s, F.} } @mastersthesis {xEscue, title = {Bundling interest points for object classification}, year = {2014}, abstract = {

Advisors: Xavier Giro-i-Nieto and Carles Ventura-Royo

Bundling interest points for object classification from Xavi Gir{\'o}

This Bachelor of Science thesis addresses the problem of image classification combining two popular visual representations: points and regions. Firstly, the study explores bundling interest points with regions. These regions are generated with an initial SLIC partition and using Binary Partition Tree (BPT), considering different scales of resolution in the segmentation. Secondly, it explores modelling visual classes as a group of points extracted from different images. Based on Naive-Bayes Nearest Neighbor (NBNN), we are using 1-Nearest Neighbor with SURF descriptor on the 17 Category Flower Dataset with 1360 images of flowers distributed into 17 classes, 80 images per class. We have verified that grouping interest points of the same class improves the F1-score a 9.2\%. However, bundling interest points into regions using segmentation worsens the F1-score between 1\% and 7\%, depending on the number of regions in the segmentation.

[Extended summary on Bitsearch blog]

Author{\textquoteright}s website: jordisanchez.info

Grade: A (9.3/10)

(This BSc thesis was written in Catalan language.)

---------------------------------

Aquest Treball Final de Grau aborda el problema de la classificaci{\'o} d{\textquoteright}imatges combinant dues representacions visuals populars: els punts i les regions. En primer lloc, l{\textquoteright}estudi explora l{\textquoteright}agrupaci{\'o} de punts d{\textquoteright}inter{\`e}s amb les regions. Aquestes regions es generen amb una partici{\'o} inicial SLIC i s{\textquoteright}utilitzen els Arbres de Partici{\'o} Bin{\`a}ria (Binary Partition Tree, BPT), considerant diferents escales de resoluci{\'o} en la segmentaci{\'o}. En segon lloc, s{\textquoteright}estudia modelar les classes com a grup de punts d{\textquoteright}inter{\`e}s extrets d{\textquoteright}imatges diferents. Basant-nos en el classificador Naive-Bayes Nearest Neighbor (NBNN), hem utilitzat el ve{\"\i} m{\'e}s proper amb un descriptor SURF sobre la base de dades 17 Category Flower Dataset, que cont{\'e} 1360 imatges de flors distribu{\"\i}des en 17 classes, amb 80 imatges per classe. Hem pogut verificar que el fet d{\textquoteright}ajuntar els punts d{\textquoteright}inter{\`e}s de les imatges d{\textquoteright}una mateixa classe millora la puntuaci{\'o} F1 en un 9,2\%. No obstant, l{\textquoteright}agrupaci{\'o} de punts d{\textquoteright}inter{\`e}s en regions utilitzant una segmentaci{\'o} de la imatge empitjora la puntuaci{\'o} F1 entre l{\textquoteright}1\% i el 7\%, depenent del nombre de regions de la segmentaci{\'o}.

Lloc web de l{\textquoteright}autor: jordisanchez.info

Qualificaci{\'o}: A (9.3/10)

Jordi Sanchez Escue

}, keywords = {Bundling interest points, Digital Images, image classification, Images, Nearest Neighbor, SURF}, url = {http://hdl.handle.net/2099.1/22672}, author = {S{\'a}nchez-Escu{\'e}, Jordi}, editor = {Ventura, C. and Xavier Gir{\'o}-i-Nieto} } @conference {cPerez-Pellitero13, title = {Bayesian region selection for adaptive dictionary-based Super-Resolution}, booktitle = {British Machine Vision Conference}, year = {2013}, month = {09/2013}, abstract = {

The performance of dictionary-based super-resolution (SR) strongly depends on the contents of the training dataset. Nevertheless, many dictionary-based SR methods randomly select patches from of a larger set of training images to build their dictionaries,\ thus relying on patches being diverse enough. This paper describes an external-dictionary SR algorithm based on adaptively selecting an optimal subset of patches out of the training images. Each training image is divided into sub-image entities, named regions, of such size that texture consistency is preserved. For each input patch to super-resolve, the best-fitting region (with enough high-frequency energy) is found through a Bayesian selection. In order to handle the high number of regions in the train- ing dataset, a local Naive Bayes Nearest Neighbor (NBNN) approach is used. Trained with this adapted subset of patches, sparse coding SR is applied to recover the high-resolution image. Experimental results demonstrate that using our adaptive algorithm produces an improvement in SR performance with respect to non-adaptive training.\ 

}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @article {aAlonso-Gonzalez13a, title = {Bilateral Distance Based Filtering for Polarimetric SAR Data}, journal = {Remote Sensing}, volume = {5}, year = {2013}, month = {10/2013}, pages = {5620-5641}, abstract = {

This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous samples of the data. A weighted average is performed over a given window favoring pixel values that are close on both domains. The filtering technique is based on a modified bilateral filtering, which is defined in terms of spatial and polarimetric distances. These distances encapsulate all the knowledge in both domains for an adaptation to the data structure. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset.

}, issn = {2072-4292}, doi = {10.3390/rs5115620}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P. and X. Deng} } @conference {cAlonso-Gonzalez11a, title = {Binary partition tree as a polarimetric SAR data representation in the space-time domain}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011}, year = {2011}, pages = {3819{\textendash}3822}, address = {Vanouver, Canada}, isbn = {978-1-4577-1005-6}, doi = {10.1109/IGARSS.2011.6050063}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6050063}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @mastersthesis {xRubiano11, title = {B{\'u}squeda Visual con Retroacci{\'o}n de Relevancia Basada en Actualizacion de Pesos}, year = {2011}, abstract = {

This project presents the design and implementation of different techniques of Relevance Feedback for image searches. These techniques use the user interaction with the search results to estimate the importance of various search criteria in the request submitted by the user. The results have been performed through the linear combination of similarity measures of different MPEG-7 visual descriptors. The results of this project have been compared with those previously obtained in the Final Degree Project by Carles Ventura. For this reason the system has been evaluated with the reference database, MPEG-7 Common Color Dataset (CCD).

B{\'u}squeda Visual con Retroacci{\'o}n de Relevancia basada en Actualizaci{\'o}n de Pesos from Xavi Gir{\'o}
}, url = {http://hdl.handle.net/2099.1/11792}, author = {Rubiano, Aida}, editor = {Xavier Gir{\'o}-i-Nieto} } @conference {cGiro-i-Nieto10a, title = {BitSearch, the blog before the thesis}, booktitle = {VI International Congress of University Teaching and Innovation}, year = {2010}, address = {Barcelona, Catalonia}, abstract = {

This paper presents BitSearch, a web blog written by a team of thesis students where they share\ the evolution of their work. The blog is aimed at improving the communication not only between\ the advisor and the students, but also at motivating the student through the public exposure of the\ research \  development. \  Basic \  wrting \  guidelines \  are \  provided \  by \  the \  professor \  in \  order \  to\ guarantee the quality of the posts and provide good metadata for their retrieval, both by author or\ by a generic text query from a search engine. After one year online, BitSearch has published 176\ posts written by 19 students, in addition to the professor{\textquoteright}s own contributions. Statistics show\ more than 7,000 \ accesses their pages from visitors from more than 100 countries all over the\ globe. The blog tool has helped in improving the guidance on the students activity, developing a\ sense \ of \ team \ work \ among \  authors \ as \ well \ as \  a more \  progressive \ preparation \  of the \ final\ dissertation.

[CIDUI 2010 Website]

}, keywords = {blog, ~online~learning, ~web-based~tools}, isbn = {978-84-8458-324-0}, url = {http://hdl.handle.net/2117/11371}, author = {Xavier Gir{\'o}-i-Nieto} } @conference {cGallego09, title = {Bayesian foreground segmentation and tracking using pixel-wise background model and region-based foreground model}, booktitle = {16th IEEE International Conference on Image Processing}, year = {2009}, pages = {3205{\textendash}3208}, doi = {10.1109/ICIP.2009.5414380}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5414380}, author = {Gallego, J. and M. Pard{\`a}s and Haro, G.} } @article {aVilaplana08, title = {Binary partition trees for object detection}, journal = {IEEE transactions on image processing}, volume = {17}, number = {11}, year = {2008}, pages = {1{\textendash}16}, abstract = {

This paper discusses the use of binary partition trees (BPTs) for object detection. BPTs are hierarchical region-based representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the search space. In this paper, several issues related to the use of BPT for object detection are studied. Concerning the tree construction, we analyze the compromise between computational complexity reduction and accuracy. This will lead us to define two parts in the BPT: one providing accuracy and one representing the search space for the object detection task. Then we analyze and objectively compare various similarity measures for the tree construction. We conclude that different similarity criteria should be used for the part providing accuracy in the BPT and for the part defining the search space and specific criteria are proposed for each case. Then we discuss the object detection strategy based on BPT. The notion of node extension is proposed and discussed. Finally, several object detection examples illustrating the generality of the approach and its efficiency are reported.

}, issn = {1057-7149}, doi = {10.1109/TIP.2008.2002841}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Salembier, P.} } @article {aCorrea07, title = {Bayesian approach for morphology based 2D human motion capture}, journal = {IEEE transactions on multimedia}, volume = {9}, number = {4}, year = {2007}, pages = {754{\textendash}765}, issn = {1520-9210}, author = {Correa, P. and Czyz, J. and Marqu{\'e}s, F. and Umeda, T. and Marichal, X. and Macq, B.} } @conference {cVilaplana07b, title = {On building a hierarchical region-based representation for generic image analysis}, booktitle = {IEEE International Conference on Image Processing}, year = {2007}, isbn = {978-1-61284-467-1}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @inbook {bFerran06, title = {BPT Enhancement based on Syntactic and Semantic criteria}, booktitle = {Semantic Multimedia}, volume = {4306}, year = {2006}, pages = {184{\textendash}198}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents two enhancements for the creation and analysis of Binary Partition Trees (BPTs). Firstly, the classic creation of BPT based on colour is expanded to include syntactic criteria derived from human perception. Secondly, a method to include semantic information in the BPT analysis is shown thanks to the definition of the BPT Semantic Neighborhood and the introduction of Semantic Trees. Both techniques aim at bridging the semantic gap between signal and semantics following a bottom-up and a top-down approach, respectively.

}, isbn = {978-3-540-49335-8}, doi = {10.1007/11930334_15}, url = {http://www.springerlink.com/content/u7201mw06545w057/}, author = {Ferran, C. and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @conference {cFerran06, title = {BPT Enhancement based on Syntactic and Semantic criteria}, booktitle = {1st International Conference on Semantic and Digital Media Technologies}, year = {2006}, pages = {184{\textendash}198}, isbn = {3-540-49335-2}, author = {Ferran, C. and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @conference {cFerran04, title = {Binary-Partition Tree creation using a quasi-inclusion criterion}, booktitle = {8th International Conference on Information Visualization (IV04)}, year = {2004}, pages = {259{\textendash}264}, isbn = {0-7695-2177-0}, author = {Ferran, C. and Casas, J.} } @article {aSalembier00, title = {Binary partition tree as an efficient representation for image processing, segmentation and information retrieval}, journal = {IEEE transactions on image processing}, volume = {9}, number = {4}, year = {2000}, pages = {561{\textendash}576}, abstract = {

This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.

}, issn = {1057-7149}, doi = {10.1109/83.841934}, author = {Salembier, P. and Garrido, L.} } @conference {cSalembier98b, title = {Binary partition tree as an efficient representation for filtering, segmentation and information retreival}, booktitle = {IEEE International Conference on Image Processing, ICIP 1998}, year = {1998}, address = {Chicago (IL), USA}, author = {Salembier, P. and Garrido, L.} }