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| Author | Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez | ||||
| Title | Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project | Type | Journal Article | ||
| Year | 2023 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCARS |
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| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ TGM2023 | Serial | 3830 | ||
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| Author | Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown | ||||
| Title | Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring | Type | Journal Article | ||
| Year | 2023 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
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| Abstract | Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework. | ||||
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| Notes | CIC; MACO | Approved | no | ||
| Call Number | Admin @ si @ XVH2023 | Serial | 3883 | ||
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| Author | Qingshan Chen; Zhenzhen Quan; Yifan Hu; Yujun Li; Zhi Liu; Mikhail Mozerov | ||||
| Title | MSIF: multi-spectrum image fusion method for cross-modality person re-identification | Type | Journal Article | ||
| Year | 2023 | Publication | International Journal of Machine Learning and Cybernetics | Abbreviated Journal | IJMLC |
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| Abstract | Sketch-RGB cross-modality person re-identification (ReID) is a challenging task that aims to match a sketch portrait drawn by a professional artist with a full-body photo taken by surveillance equipment to deal with situations where the monitoring equipment is damaged at the accident scene. However, sketch portraits only provide highly abstract frontal body contour information and lack other important features such as color, pose, behavior, etc. The difference in saliency between the two modalities brings new challenges to cross-modality person ReID. To overcome this problem, this paper proposes a novel dual-stream model for cross-modality person ReID, which is able to mine modality-invariant features to reduce the discrepancy between sketch and camera images end-to-end. More specifically, we propose a multi-spectrum image fusion (MSIF) method, which aims to exploit the image appearance changes brought by multiple spectrums and guide the network to mine modality-invariant commonalities during training. It only processes the spectrum of the input images without adding additional calculations and model complexity, which can be easily integrated into other models. Moreover, we introduce a joint structure via a generalized mean pooling (GMP) layer and a self-attention (SA) mechanism to balance background and texture information and obtain the regional features with a large amount of information in the image. To further shrink the intra-class distance, a weighted regularized triplet (WRT) loss is developed without introducing additional hyperparameters. The model was first evaluated on the PKU Sketch ReID dataset, and extensive experimental results show that the Rank-1/mAP accuracy of our method is 87.00%/91.12%, reaching the current state-of-the-art performance. To further validate the effectiveness of our approach in handling cross-modality person ReID, we conducted experiments on two commonly used IR-RGB datasets (SYSU-MM01 and RegDB). The obtained results show that our method achieves competitive performance. These results confirm the ability of our method to effectively process images from different modalities. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ CQH2023 | Serial | 3885 | ||
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| Author | Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal | ||||
| Title | SemiDocSeg: Harnessing Semi-Supervised Learning for Document Layout Analysis | Type | Journal Article | ||
| Year | 2024 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
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| Keywords | Document layout analysis; Semi-supervised learning; Co-Occurrence matrix; Instance segmentation; Swin transformer | ||||
| Abstract | Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach. | ||||
| Address | June 2024 | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BBL2024a | Serial | 4001 | ||
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| Author | Zahra Raisi-Estabragh; Carlos Martin-Isla; Louise Nissen; Liliana Szabo; Victor M. Campello; Sergio Escalera; Simon Winther; Morten Bottcher; Karim Lekadir; and Steffen E. Petersen | ||||
| Title | Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset | Type | Journal Article | ||
| Year | 2023 | Publication | Frontiers in Cardiovascular Medicine | Abbreviated Journal | FCM |
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RMN2023 | Serial | 3937 | ||
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| Author | Adrien Pavao; Isabelle Guyon; Anne-Catherine Letournel; Dinh-Tuan Tran; Xavier Baro; Hugo Jair Escalante; Sergio Escalera; Tyler Thomas; Zhen Xu | ||||
| Title | CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges | Type | Journal Article | ||
| Year | 2023 | Publication | Journal of Machine Learning Research | Abbreviated Journal | JMLR |
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| Abstract | CodaLab Competitions is an open source web platform designed to help data scientists and research teams to crowd-source the resolution of machine learning problems through the organization of competitions, also called challenges or contests. CodaLab Competitions provides useful features such as multiple phases, results and code submissions, multi-score leaderboards, and jobs running
inside Docker containers. The platform is very flexible and can handle large scale experiments, by allowing organizers to upload large datasets and provide their own CPU or GPU compute workers. |
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ PGL2023 | Serial | 3973 | ||
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| Author | Henry Velesaca; Gisel Bastidas-Guacho; Mohammad Rouhani; Angel Sappa | ||||
| Title | Multimodal image registration techniques: a comprehensive survey | Type | Journal Article | ||
| Year | 2024 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
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| Abstract | This manuscript presents a review of state-of-the-art techniques proposed in the literature for multimodal image registration, addressing instances where images from different modalities need to be precisely aligned in the same reference system. This scenario arises when the images to be registered come from different modalities, among the visible and thermal spectral bands, 3D-RGB, or flash-no flash, or NIR-visible. The review spans different techniques from classical approaches to more modern ones based on deep learning, aiming to highlight the particularities required at each step in the registration pipeline when dealing with multimodal images. It is noteworthy that medical images are excluded from this review due to their specific characteristics, including the use of both active and passive sensors or the non-rigid nature of the body contained in the image. | ||||
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| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ VBR2024 | Serial | 3997 | ||
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| Author | Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown | ||||
| Title | Noise Prism: A Novel Multispectral Visualization Technique | Type | Journal Article | ||
| Year | 2021 | Publication | 31st Color and Imaging Conference | Abbreviated Journal | |
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| Abstract | A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique. |
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| Area | Expedition | Conference | CIC | ||
| Notes | MACO; CIC | Approved | no | ||
| Call Number | Admin @ si @ CVL2021 | Serial | 4000 | ||
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| Author | Mingyi Yang; Fei Yang; Luka Murn; Marc Gorriz Blanch; Juil Sock; Shuai Wan; Fuzheng Yang; Luis Herranz | ||||
| Title | Task-Switchable Pre-Processor for Image Compression for Multiple Machine Vision Tasks | Type | Journal Article | ||
| Year | 2024 | Publication | IEEE Transactions on Circuits and Systems for Video Technology | Abbreviated Journal | |
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| Keywords | M Yang, F Yang, L Murn, MG Blanch, J Sock, S Wan, F Yang, L Herranz | ||||
| Abstract | Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications. | ||||
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| Notes | xxx | Approved | no | ||
| Call Number | Admin @ si @ YYM2024 | Serial | 4007 | ||
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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | A transformer model for boundary detection in continuous sign language | Type | Journal Article | ||
| Year | 2024 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
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| Abstract | Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundaries of isolated signs within a continuous video stream. Additionally, the reliance on handcrafted features in existing models poses a challenge to achieving optimal accuracy. To surmount these challenges, we propose a novel approach utilizing a Transformer-based model. Unlike traditional models, our approach focuses on enhancing accuracy while eliminating the need for handcrafted features. The Transformer model is employed for both ISLR and CSLR. The training process involves using isolated sign videos, where hand keypoint features extracted from the input video are enriched using the Transformer model. Subsequently, these enriched features are forwarded to the final classification layer. The trained model, coupled with a post-processing method, is then applied to detect isolated sign boundaries within continuous sign videos. The evaluation of our model is conducted on two distinct datasets, including both continuous signs and their corresponding isolated signs, demonstrates promising results. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RKE2024 | Serial | 4016 | ||
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| Author | Jordi Vitria; X. Binefa; Juan J. Villanueva | ||||
| Title | Morphological Algorithms for Visual Analysis of Integrated Circuits. | Type | Journal Article | ||
| Year | 1992 | Publication | Journal of Visual Communications and image Representation | Abbreviated Journal | |
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3 | Issue | 2 | Pages | 194-202 |
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VBV1992 | Serial | 248 | ||
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| Author | Jaume Garcia; Debora Gil; Sandra Pujades; Francesc Carreras | ||||
| Title | A Variational Framework for Assessment of the Left Ventricle Motion | Type | Journal Article | ||
| Year | 2008 | Publication | International Journal Mathematical Modelling of Natural Phenomena | Abbreviated Journal | |
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3 | Issue | 6 | Pages | 76-100 |
| Keywords | Key words: Left Ventricle Dynamics, Ventricular Torsion, Tagged Magnetic Resonance, Motion Tracking, Variational Framework, Gabor Transform. | ||||
| Abstract | Impairment of left ventricular contractility due to cardiovascular diseases is reflected in left ventricle (LV) motion patterns. An abnormal change of torsion or long axis shortening LV values can help with the diagnosis and follow-up of LV dysfunction. Tagged Magnetic Resonance (TMR) is a widely spread medical imaging modality that allows estimation of the myocardial tissue local deformation. In this work, we introduce a novel variational framework for extracting the left ventricle dynamics from TMR sequences. A bi-dimensional representation space of TMR images given by Gabor filter banks is defined. Tracking of the phases of the Gabor response is combined using a variational framework which regularizes the deformation field just at areas where the Gabor amplitude drops, while restoring the underlying motion otherwise. The clinical applicability of the proposed method is illustrated by extracting normality models of the ventricular torsion from 19 healthy subjects. | ||||
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GGC2008a | Serial | 1058 | ||
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| Author | Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera | ||||
| Title | Deteccion automatica de la dominancia en conversaciones diadicas | Type | Journal Article | ||
| Year | 2010 | Publication | Escritos de Psicologia | Abbreviated Journal | EP |
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3 | Issue | 2 | Pages | 41–45 |
| Keywords | Dominance detection; Non-verbal communication; Visual features | ||||
| Abstract | Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers' opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences. | ||||
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| ISSN | 1989-3809 | ISBN | Medium | ||
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| Notes | HUPBA; OR; MILAB;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EMV2010 | Serial | 1315 | ||
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| Author | Albert Ali Salah; Theo Gevers; Nicu Sebe; Alessandro Vinciarelli | ||||
| Title | Computer Vision for Ambient Intelligence | Type | Journal Article | ||
| Year | 2011 | Publication | Journal of Ambient Intelligence and Smart Environments | Abbreviated Journal | JAISE |
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3 | Issue | 3 | Pages | 187-191 |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ SGS2011a | Serial | 1725 | ||
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| Author | Alberto Hidalgo; Ferran Poveda; Enric Marti;Debora Gil;Albert Andaluz; Francesc Carreras; Manuel Ballester | ||||
| Title | Evidence of continuous helical structure of the cardiac ventricular anatomy assessed by diffusion tensor imaging magnetic resonance multiresolution tractography | Type | Journal Article | ||
| Year | 2012 | Publication | European Radiology | Abbreviated Journal | ECR |
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3 | Issue | 1 | Pages | 361-362 |
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| Abstract | Deep understanding of myocardial structure linking morphology and func- tion of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Diffusion tensor MRI provides a discrete measurement of the 3D arrangement of myocardial fibres by the observation of local anisotropic
diffusion of water molecules in biological tissues. In this work, we present a multi- scale visualisation technique based on DT-MRI streamlining capable of uncovering additional properties of the architectural organisation of the heart. Methods and Materials: We selected the John Hopkins University (JHU) Canine Heart Dataset, where the long axis cardiac plane is aligned with the scanner’s Z- axis. Their equipment included a 4-element passed array coil emitting a 1.5 T. For DTI acquisition, a 3D-FSE sequence is apply. We used 200 seeds for full-scale tractography, while we applied a MIP mapping technique for simplified tractographic reconstruction. In this case, we reduced each DTI 3D volume dimensions by order- two magnitude before streamlining. Our simplified tractographic reconstruction method keeps the main geometric features of fibres, allowing for an easier identification of their global morphological disposition, including the ventricular basal ring. Moreover, we noticed a clearly visible helical disposition of the myocardial fibres, in line with the helical myocardial band ventricular structure described by Torrent-Guasp. Finally, our simplified visualisation with single tracts identifies the main segments of the helical ventricular architecture. DT-MRI makes possible the identification of a continuous helical architecture of the myocardial fibres, which validates Torrent-Guasp’s helical myocardial band ventricular anatomical model. |
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| Address | Viena, Austria | ||||
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| Publisher | Springer Link | Place of Publication | Editor | ||
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| ISSN | 1869-4101 | ISBN | Medium | ||
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ HPM2012 | Serial | 1858 | ||
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