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| Author | German Barquero; Sergio Escalera; Cristina Palmero | ||||
| Title | Seamless Human Motion Composition with Blended Positional Encodings | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BEP2024 | Serial | 4022 | ||
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| Author | Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal | ||||
| Title | GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BBL2024b | Serial | 4023 | ||
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| Author | Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang | ||||
| Title | Diffusion-based network for unsupervised landmark detection | Type | Journal Article | ||
| Year | 2024 | Publication | Knowledge-Based Systems | Abbreviated Journal | |
| Volume | 292 | Issue | Pages | 111627 | |
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| Abstract | Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ WWT2024 | Serial | 4024 | ||
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| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva | ||||
| Title | Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions | Type | Book Chapter | ||
| Year | 2006 | Publication | 11th Iberoamerican Congress on Pattern Recognition | Abbreviated Journal | |
| Volume | 4225 | Issue | Pages | 178–187 | |
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| Abstract | This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns. | ||||
| Address | Cancun (Mexico) | ||||
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| Publisher | Springer Verlag | Place of Publication | Berlin Heidelberg | Editor |
.F. Mart ́ınez-Trinidad et al |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | 800 | Expedition | Conference | ||
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f | Serial | 728 | ||
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| Author | A.S. Coquel; Jean-Pascal Jacob; M. Primet; A. Demarez; Mariella Dimiccoli; T. Julou; L. Moisan; A. Lindner; H. Berry | ||||
| Title | Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect | Type | Journal Article | ||
| Year | 2013 | Publication | Plos Computational Biology | Abbreviated Journal | PCB |
| Volume | 9 | Issue | 4 | Pages | |
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| Abstract | Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of “soft” intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli. | ||||
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| Publisher | Place of Publication | Editor |
: Stanislav Shvartsman, Princeton University, United States of America | ||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @CJP2013 | Serial | 2786 | ||
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| Author | David Roche; Debora Gil; Jesus Giraldo | ||||
| Title | Assessing agonist efficacy in an uncertain Em world | Type | Conference Article | ||
| Year | 2012 | Publication | 40th Keystone Symposia on mollecular and celular biology | Abbreviated Journal | |
| Volume | Issue | Pages | 79 | ||
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| Abstract | The operational model of agonism has been widely used for the analysis of agonist action since its formulation in 1983. The model includes the Em parameter, which is defined as the maximum response of the system. The methods for Em estimation provide Em values not significantly higher than the maximum responses achieved by full agonists. However, it has been found that that some classes of compounds as, for instance, superagonists and positive allosteric modulators can increase the full agonist maximum response, implying upper limits for Em and thereby posing doubts on the validity of Em estimates. Because of the correlation between Em and operational efficacy, τ, wrong Em estimates will yield wrong τ estimates.
In this presentation, the operational model of agonism and various methods for the simulation of allosteric modulation will be analyzed. Alternatives for curve fitting will be presented and discussed. |
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| Address | Fairmont Banff Springs, Banff, Alberta, Canada | ||||
| Corporate Author | Keystone Symposia | Thesis | |||
| Publisher | Keystone Symposia | Place of Publication | Editor |
A. Christopoulus and M. Bouvier | |
| Language | english | Summary Language | english | Original Title | |
| Series Editor | Keystone Symposia | Series Title | Abbreviated Series Title | ||
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| Area | Expedition | Conference | KSMCB | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ RGG2012 | Serial | 1855 | ||
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| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | Optical Music Recognition by Long Short-Term Memory Networks | Type | Book Chapter | ||
| Year | 2018 | Publication | Graphics Recognition. Current Trends and Evolutions | Abbreviated Journal | |
| Volume | 11009 | Issue | Pages | 81-95 | |
| Keywords | Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory | ||||
| Abstract | Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. | ||||
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| Publisher | Springer | Place of Publication | Editor |
A. Fornes, B. Lamiroy | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRC2018 | Serial | 3227 | ||
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| Author | Santiago Segui; Laura Igual; Fernando Vilariño; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
| Title | Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy | Type | Book Chapter | ||
| Year | 2008 | Publication | Computer Vision Systems. 6th International | Abbreviated Journal | |
| Volume | 5008 | Issue | Pages | 251–260 | |
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| Abstract | Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists. | ||||
| Address | Santorini (Greece) | ||||
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| Publisher | Springer-Verlag | Place of Publication | Berlin Heidelberg | Editor |
A. Gasteratos, M. Vincze, and J.K. Tsotsos |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-540-79546-9 | Medium | ||
| Area | 800 | Expedition | Conference | ICVS | |
| Notes | OR; MV; MILAB; SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SIV2008; IAM @ iam @ SIV2008 | Serial | 962 | ||
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| Author | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu | ||||
| Title | 3D Texton Spaces for color-texture retrieval | Type | Conference Article | ||
| Year | 2010 | Publication | 7th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 6111 | Issue | Pages | 354–363 | |
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| Abstract | Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor |
A.C. Campilho and M.S. Kamel | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-13771-6 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ ASV2010a | Serial | 1325 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Deep learning-based vegetation index estimation | Type | Book Chapter | ||
| Year | 2021 | Publication | Generative Adversarial Networks for Image-to-Image Translation | Abbreviated Journal | |
| Volume | Issue | Pages | 205-234 | ||
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| Abstract | Chapter 9 | ||||
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| Publisher | Elsevier | Place of Publication | Editor |
A.Solanki; A.Nayyar; M.Naved | |
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| Notes | MSIAU; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2021a | Serial | 3578 | ||
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| Author | Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel | ||||
| Title | Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures | Type | Book Chapter | ||
| Year | 2010 | Publication | Bayesian Network | Abbreviated Journal | |
| Volume | Issue | Pages | 13-37 | ||
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| Publisher | Sciyo | Place of Publication | Editor |
Ahmed Rebai | |
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| ISSN | ISBN | 978-953-307-124-4 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ BDL2010 | Serial | 1461 | ||
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| Author | Debora Gil; Petia Radeva | ||||
| Title | Inhibition of False Landmarks | Type | Book Chapter | ||
| Year | 2004 | Publication | Recent Advances in Artificial Intelligence Research and Development | Abbreviated Journal | |
| Volume | Issue | Pages | 233-244 | ||
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| Abstract | We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Its high sensitivity to changes in vector directions makes it suitable for landmark location in real images prone to need smoothing to reduce the impact of noise. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our Inhibition Orientation Energy (IOE) landmark locator. | ||||
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| Publisher | IOS Press | Place of Publication | Barcelona (Spain) | Editor |
al, J.V. et |
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| Notes | IAM;MILAB | Approved | no | ||
| Call Number | IAM @ iam @ GiR2004a | Serial | 1533 | ||
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| Author | Jaume Garcia; Debora Gil; A.Bajo; M.J.Ledesma-Carbayo; C.SantaMarta | ||||
| Title | Influence of the temporal resolution on the quantification of displacement fields in cardiac magnetic resonance tagged images | Type | Conference Article | ||
| Year | 2008 | Publication | Proc. Computers in Cardiology | Abbreviated Journal | |
| Volume | 35 | Issue | Pages | 785-788 | |
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| Abstract | It is difficult to acquire tagged cardiac MR images with a high temporal and spatial resolution using clinical MR scanners. However, if such images are used for quantifying scores based on motion, it is essential a resolution as high as possible. This paper explores the influence of the temporal resolution of a tagged series on the quantification of myocardial dynamic parameters. To such purpose we have designed a SPAMM (Spatial Modulation of Magnetization) sequence allowing acquisition of sequences at simple and double temporal resolution. Sequences are processed to compute myocardial motion by an automatic technique based on the tracking of the harmonic phase of tagged images (the Harmonic Phase Flow, HPF). The results have been compared to manual tracking of myocardial tags. The error in displacement fields for double resolution sequences reduces 17%. | ||||
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| Publisher | Place of Publication | Editor |
Alan Murray | ||
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GGB2008 | Serial | 1508 | ||
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| Author | Nataliya Shapovalova; Carles Fernandez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Semantics of Human Behavior in Image Sequences | Type | Book Chapter | ||
| Year | 2011 | Publication | Computer Analysis of Human Behavior | Abbreviated Journal | |
| Volume | Issue | 7 | Pages | 151-182 | |
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| Abstract | Human behavior is contextualized and understanding the scene of an action is crucial for giving proper semantics to behavior. In this chapter we present a novel approach for scene understanding. The emphasis of this work is on the particular case of Human Event Understanding. We introduce a new taxonomy to organize the different semantic levels of the Human Event Understanding framework proposed. Such a framework particularly contributes to the scene understanding domain by (i) extracting behavioral patterns from the integrative analysis of spatial, temporal, and contextual evidence and (ii) integrative analysis of bottom-up and top-down approaches in Human Event Understanding. We will explore how the information about interactions between humans and their environment influences the performance of activity recognition, and how this can be extrapolated to the temporal domain in order to extract higher inferences from human events observed in sequences of images. | ||||
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| Publisher | Springer London | Place of Publication | Editor |
Albert Ali Salah; | |
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| ISSN | ISBN | 978-0-85729-993-2 | Medium | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ SFR2011 | Serial | 1810 | ||
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| Author | Arnau Baro | ||||
| Title | Reading Music Systems: From Deep Optical Music Recognition to Contextual Methods | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The transcription of sheet music into some machine-readable format can be carried out manually. However, the complexity of music notation inevitably leads to burdensome software for music score editing, which makes the whole process
very time-consuming and prone to errors. Consequently, automatic transcription systems for musical documents represent interesting tools. Document analysis is the subject that deals with the extraction and processing of documents through image and pattern recognition. It is a branch of computer vision. Taking music scores as source, the field devoted to address this task is known as Optical Music Recognition (OMR). Typically, an OMR system takes an image of a music score and automatically extracts its content into some symbolic structure such as MEI or MusicXML. In this dissertation, we have investigated different methods for recognizing a single staff section (e.g. scores for violin, flute, etc.), much in the same way as most text recognition research focuses on recognizing words appearing in a given line image. These methods are based in two different methodologies. On the one hand, we present two methods based on Recurrent Neural Networks, in particular, the Long Short-Term Memory Neural Network. On the other hand, a method based on Sequence to Sequence models is detailed. Music context is needed to improve the OMR results, just like language models and dictionaries help in handwriting recognition. For example, syntactical rules and grammars could be easily defined to cope with the ambiguities in the rhythm. In music theory, for example, the time signature defines the amount of beats per bar unit. Thus, in the second part of this dissertation, different methodologies have been investigated to improve the OMR recognition. We have explored three different methods: (a) a graphic tree-structure representation, Dendrograms, that joins, at each level, its primitives following a set of rules, (b) the incorporation of Language Models to model the probability of a sequence of tokens, and (c) graph neural networks to analyze the music scores to avoid meaningless relationships between music primitives. Finally, to train all these methodologies, and given the method-specificity of the datasets in the literature, we have created four different music datasets. Two of them are synthetic with a modern or old handwritten appearance, whereas the other two are real handwritten scores, being one of them modern and the other old. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor |
Alicia Fornes | |
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| ISSN | ISBN | 978-84-124793-8-6 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; | Approved | no | ||
| Call Number | Admin @ si @ Bar2022 | Serial | 3754 | ||
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