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| Author | Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti | ||||
| Title | Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control | Abbreviated Journal | T-UFFC |
| Volume | 58 | Issue | 1 | Pages  |
60-72 |
| Keywords | 3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging | ||||
| Abstract | Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals. | ||||
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| ISSN | 0885-3010 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM;ADAS | Approved | no | ||
| Call Number | IAM @ iam @ HGG2011 | Serial | 1546 | ||
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| Author | Koen E.A. van de Sande; Theo Gevers; Cees G.M. Snoek | ||||
| Title | Empowering Visual Categorization with the GPU | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
| Volume | 13 | Issue | 1 | Pages |
60-70 |
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| Abstract | Visual categorization is important to manage large collections of digital images and video, where textual meta-data is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a severe drawback of this model is its high computational cost. As the trend to increase computational power in newer CPU and GPU architectures is to increase their level of parallelism, exploiting this parallelism becomes an important direction to handle the computational cost of the bag-of-words approach. When optimizing a system based on the bag-of-words approach, the goal is to minimize the time it takes to process batches of images. Additionally, we also consider power usage as an evaluation metric. In this paper, we analyze the bag-of-words model for visual categorization in terms of computational cost and identify two major bottlenecks: the quantization step and the classification step. We address these two bottlenecks by proposing two efficient algorithms for quantization and classification by exploiting the GPU hardware and the CUDA parallel programming model. The algorithms are designed to (1) keep categorization accuracy intact, (2) decompose the problem and (3) give the same numerical results. In the experiments on large scale datasets it is shown that, by using a parallel implementation on the Geforce GTX260 GPU, classifying unseen images is 4.8 times faster than a quad-core CPU version on the Core i7 920, while giving the exact same numerical results. In addition, we show how the algorithms can be generalized to other applications, such as text retrieval and video retrieval. Moreover, when the obtained speedup is used to process extra video frames in a video retrieval benchmark, the accuracy of visual categorization is improved by 29%. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ SGS2011b | Serial | 1729 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 70 | Issue | Pages |
60-74 | |
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| Abstract | Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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| Notes | DAG; 600.084; 601.197; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GoK2017 | Serial | 2886 | ||
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| Author | Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Audio-Visual Emotion Recognition in Video Clips | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume | 10 | Issue | 1 | Pages |
60-75 |
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| Abstract | This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. | ||||
| Address | 1 Jan.-March 2019 | ||||
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| Notes | HUPBA; 602.143; 602.133 | Approved | no | ||
| Call Number | Admin @ si @ NMN2017 | Serial | 3011 | ||
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| Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; V. Leboran; Xose M. Pardo | ||||
| Title | Psychophysical evaluation of individual low-level feature influences on visual attention | Type | Journal Article | ||
| Year | 2019 | Publication | Vision Research | Abbreviated Journal | VR |
| Volume | 154 | Issue | Pages |
60-79 | |
| Keywords | Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset | ||||
| Abstract | In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images. | ||||
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| Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BFO2019a | Serial | 3274 | ||
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| Author | Joan Mas; J.A. Jorge; Gemma Sanchez; Josep Llados | ||||
| Title | Describing and Parising Hand-Drawn Sketches using a Syntactic Approach | Type | Conference Article | ||
| Year | 2007 | Publication | Seventh IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
61–62 | ||
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| Address | Curitiba (Brasil) | ||||
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| Publisher | Place of Publication | Editor | J. Llados, W. Liu, J.M. Ogier | ||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ MJS2007 | Serial | 845 | ||
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| Author | Agata Lapedriza; David Masip; Jordi Vitria | ||||
| Title | Subject Recognition Using a New Approach for Feature Extraction | Type | Conference Article | ||
| Year | 2008 | Publication | 3rd International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 2 | Issue | Pages |
61–66 | |
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| Address | Madeira (Portugal) | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | OR; MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ LMV2008a | Serial | 980 | ||
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| Author | Marta Nuñez-Garcia; Sonja Simpraga; M.Angeles Jurado; Maite Garolera; Roser Pueyo; Laura Igual | ||||
| Title | FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization | Type | Conference Article | ||
| Year | 2015 | Publication | Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015 | Abbreviated Journal | |
| Volume | Issue | Pages |
61-68 | ||
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| Address | Munich; Germany; October 2015 | ||||
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| Area | Expedition | Conference | MLMI | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ NSJ2015 | Serial | 2674 | ||
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| Author | Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou | ||||
| Title | The Robust Reading Competition Annotation and Evaluation Platform | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages |
61-66 | ||
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| Abstract | The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services. |
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| Address | Viena; Austria; April 2018 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | KGR2018 | Serial | 3103 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach | Type | Conference Article | ||
| Year | 2011 | Publication | 2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems | Abbreviated Journal | |
| Volume | Issue | Pages |
62-71 | ||
| Keywords | Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time. | ||||
| Abstract | In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction. | ||||
| Address | Rome, Italy | ||||
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| Publisher | SciTePress | Place of Publication | Editor | Djemal, Khalifa | |
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| Area | 800 | Expedition | Conference | MIAD | |
| Notes | MV;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ BSV2011a | Serial | 1695 | ||
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| Author | Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell | ||||
| Title | Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops | Abbreviated Journal | |
| Volume | 9401 | Issue | Pages |
62-70 | |
| Keywords | Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy | ||||
| Abstract | Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface. | ||||
| Address | Quebec; Canada; September 2016 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | IAM; MV; 600.060; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ SGB2016 | Serial | 2885 | ||
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| Author | Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Saliency for fine-grained object recognition in domains with scarce training data | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 94 | Issue | Pages |
62-73 | |
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| Abstract | This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline. | ||||
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| Notes | LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ FGW2019 | Serial | 3264 | ||
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| Author | Jose Antonio Rodriguez; Gemma Sanchez; Josep Llados | ||||
| Title | Categorization of Digital Ink Elements using Spectral Features | Type | Conference Article | ||
| Year | 2007 | Publication | Seventh IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
63–64 | ||
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| Address | Curitiba (Brazil) | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RSL2007c | Serial | 888 | ||
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| Author | Agata Lapedriza; Jaume Garcia; Ernest Valveny; Robert Benavente; Miquel Ferrer; Gemma Sanchez | ||||
| Title | Una experiencia de aprenentatge basada en projectes en el ambit de la informatica | Type | Miscellaneous | ||
| Year | 2008 | Publication | V Jornades d’Innovacio Docent (UAB) | Abbreviated Journal | |
| Volume | Issue | Pages |
63 | ||
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| Address | Bellaterra (Spain) | ||||
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| Notes | OR; IAM; DAG; CIC; MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ LGV2008 | Serial | 1030 | ||
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| Author | Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados | ||||
| Title | Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method | Type | Conference Article | ||
| Year | 2011 | Publication | 11th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
63-67 | ||
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| Abstract | In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts. | ||||
| Address | Beijing, China | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG;ADAS | Approved | no | ||
| Call Number | Admin @ si @ RAT2011 | Serial | 1788 | ||
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