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| Author | Marc Bolaños; Petia Radeva | ||||
| Title | Simultaneous Food Localization and Recognition | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition | Abbreviated Journal | |
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| Abstract | CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images. |
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| Address | Cancun; Mexico; December 2016 | ||||
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| Area | Expedition | Conference | ICPR | ||
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BoR2016 | Serial | 2834 | ||
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| Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva | ||||
| Title | VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering | Type | Conference Article | ||
| Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks | ||||
| Abstract | In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. | ||||
| Address | Faro; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BPC2017 | Serial | 2939 | ||
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| Author | Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva | ||||
| Title | Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study | Type | Conference Article | ||
| Year | 2017 | Publication | International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) | Abbreviated Journal | |
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| Abstract | An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback. The pipeline of the method requires the identi cation of the stable frames of the sequence and the reliable detection of stent struts. Using this data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores. |
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| Address | Quebec; Canada; September 2017 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BCR2017 | Serial | 2968 | ||
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| Author | Maedeh Aghaei; Mariella Dimiccoli; C. Canton-Ferrer; Petia Radeva | ||||
| Title | Towards social pattern characterization from egocentric photo-streams | Type | Journal Article | ||
| Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 171 | Issue | Pages | 104-117 | |
| Keywords | Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks | ||||
| Abstract | Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams. | ||||
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ ADC2018 | Serial | 3022 | ||
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| Author | Aniol Lidon; Marc Bolaños; Mariella Dimiccoli; Petia Radeva; Maite Garolera; Xavier Giro | ||||
| Title | Semantic Summarization of Egocentric Photo-Stream Events | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd Workshop on Lifelogging Tools and Applications | Abbreviated Journal | |
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| Address | San Francisco; USA; October 2017 | ||||
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| ISSN | ISBN | 978-1-4503-5503-2 | Medium | ||
| Area | Expedition | Conference | ACMW (LTA) | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ LBD2017 | Serial | 3024 | ||
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| Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva | ||||
| Title | Egocentric video description based on temporally-linked sequences | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
| Volume | 50 | Issue | Pages | 205-216 | |
| Keywords | egocentric vision; video description; deep learning; multi-modal learning | ||||
| Abstract | Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BPC2018 | Serial | 3109 | ||
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| Author | Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel | ||||
| Title | A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 465-473 | |
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| Abstract | Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. | ||||
| Address | Povoa de Varzim; Portugal; June 2018 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICIAR | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SRR2018a | Serial | 3110 | ||
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| Author | Stefan Lonn; Petia Radeva; Mariella Dimiccoli | ||||
| Title | A picture is worth a thousand words but how to organize thousands of pictures? | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. | ||||
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ LRD2018 | Serial | 3111 | ||
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| Author | Md.Mostafa Kamal Sarker; , Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig | ||||
| Title | SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. | Type | Conference Article | ||
| Year | 2018 | Publication | 21st International Conference on Medical Image Computing & Computer Assisted Intervention | Abbreviated Journal | |
| Volume | 2 | Issue | Pages | 21-29 | |
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| Abstract | Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU. | ||||
| Address | Granada; Espanya; September 2018 | ||||
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| Area | Expedition | Conference | MICCAI | ||
| Notes |
MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SRA2018 | Serial | 3112 | ||
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| Author | Alejandro Cartas; Estefania Talavera; Petia Radeva; Mariella Dimiccoli | ||||
| Title | On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users. | ||||
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ CTR2018 | Serial | 3184 | ||
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| Author | Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli | ||||
| Title | Batch-based activity recognition from egocentric photo-streams revisited | Type | Journal Article | ||
| Year | 2018 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
| Volume | 21 | Issue | 4 | Pages | 953–965 |
| Keywords | Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks | ||||
| Abstract | Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. | ||||
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ CMR2018 | Serial | 3186 | ||
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| Author | Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva | ||||
| Title | Introduction to the special issue: Egocentric Vision and Lifelogging | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
| Volume | 55 | Issue | Pages | 352-353 | |
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ DGC2018 | Serial | 3187 | ||
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| Author | Sumit K. Banchhor; Narendra D. Londhe; Tadashi Araki; Luca Saba; Petia Radeva; Narendra N. Khanna; Jasjit S. Suri | ||||
| Title | Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. | Type | Journal Article | ||
| Year | 2018 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
| Volume | 101 | Issue | Pages | 184-198 | |
| Keywords | Heart disease; Stroke; Atherosclerosis; Intravascular; Coronary; Carotid; Calcium; Morphology; Risk stratification | ||||
| Abstract | Purpose of review
Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins. Recent finding Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes. |
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BLA2018 | Serial | 3188 | ||
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| Author | Md Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Vivek Kumar Singh; Syeda Furruka Banu; Forhad U H Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig; Mohamed Abdel-Nasser | ||||
| Title | SLSNet: Skin lesion segmentation using a lightweight generative adversarial network | Type | Journal Article | ||
| Year | 2021 | Publication | Expert Systems With Applications | Abbreviated Journal | ESWA |
| Volume | 183 | Issue | Pages | 115433 | |
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| Abstract | The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications. | ||||
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SRA2021 | Serial | 3633 | ||
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| Author | Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva | ||||
| Title | Assessment of intracoronary stent location and extension in intravascular ultrasound sequences | Type | Journal Article | ||
| Year | 2019 | Publication | Medical Physics | Abbreviated Journal | MEDPHYS |
| Volume | 46 | Issue | 2 | Pages | 484-493 |
| Keywords | IVUS; malapposition; stent; ultrasound | ||||
| Abstract | PURPOSE:
An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment. METHODS: The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm. RESULTS: The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present. CONCLUSIONS: Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method. |
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BCR2019 | Serial | 3231 | ||
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