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Author | Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil | ||||
Title | BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation | Type | Journal Article | ||
Year | 2023 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 228 | Issue | Pages | 107241 | |
Keywords | Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation | ||||
Abstract | Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy. |
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | IAM; | Approved | no | ||
Call Number | Admin @ si @ BSC2023 | Serial | 3702 | ||
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Author | Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira | ||||
Title | Dynamic Comparison of Headlights | Type | Journal Article | ||
Year | 2008 | Publication | Journal of Automobile Engineering | Abbreviated Journal | |
Volume | 222 | Issue | 5 | Pages | 643–656 |
Keywords | video alignment | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ SDL2008a | Serial | 958 | ||
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Author | Yaxing Wang; Abel Gonzalez-Garcia; Luis Herranz; Joost Van de Weijer | ||||
Title | Controlling biases and diversity in diverse image-to-image translation | Type | Journal Article | ||
Year | 2021 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 202 | Issue | Pages | 103082 | |
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Abstract | JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes. |
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Notes | LAMP; 600.141; 600.109; 600.147 | Approved | no | ||
Call Number | Admin @ si @ WGH2021 | Serial | 3464 | ||
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Author | Jaume Amores | ||||
Title | Multiple Instance Classification: review, taxonomy and comparative study | Type | Journal Article | ||
Year | 2013 | Publication | Artificial Intelligence | Abbreviated Journal | AI |
Volume | 201 | Issue | Pages | 81-105 | |
Keywords | Multi-instance learning; Codebook; Bag-of-Words | ||||
Abstract | Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods. |
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Publisher | Elsevier Science Publishers Ltd. Essex, UK | Place of Publication | Editor | ||
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ISSN | 0004-3702 | ISBN | Medium | ||
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Notes | ADAS; 601.042; 600.057 | Approved | no | ||
Call Number | Admin @ si @ Amo2013 | Serial | 2273 | ||
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Author | T. Widemann; Xavier Otazu | ||||
Title | Titanias radius and an upper limit on its atmosphere from the September 8, 2001 stellar occultation | Type | Journal Article | ||
Year | 2009 | Publication | International Journal of Solar System Studies | Abbreviated Journal | |
Volume | 199 | Issue | 2 | Pages | 458–476 |
Keywords | Occultations; Uranus, satellites; Satellites, shapes; Satellites, dynamics; Ices; Satellites, atmospheres | ||||
Abstract | On September 8, 2001 around 2 h UT, the largest uranian moon, Titania, occulted Hipparcos star 106829 (alias SAO 164538, a V=7.2, K0 III star). This was the first-ever observed occultation by this satellite, a rare event as Titania subtends only 0.11 arcsec on the sky. The star's unusual brightness allowed many observers, both amateurs or professionals, to monitor this unique event, providing fifty-seven occultations chords over three continents, all reported here. Selecting the best 27 occultation chords, and assuming a circular limb, we derive Titania's radius: View the MathML source (1-σ error bar). This implies a density of View the MathML source using the value View the MathML source derived by Taylor [Taylor, D.B., 1998. Astron. Astrophys. 330, 362–374]. We do not detect any significant difference between equatorial and polar radii, in the limit View the MathML source, in agreement with Voyager limb image retrieval during the 1986 flyby. Titania's offset with respect to the DE405 + URA027 (based on GUST86 theory) ephemeris is derived: ΔαTcos(δT)=−108±13 mas and ΔδT=−62±7 mas (ICRF J2000.0 system). Most of this offset is attributable to a Uranus' barycentric offset with respect to DE405, that we estimate to be: View the MathML source and ΔδU=−85±25 mas at the moment of occultation. This offset is confirmed by another Titania stellar occultation observed on August 1st, 2003, which provides an offset of ΔαTcos(δT)=−127±20 mas and ΔδT=−97±13 mas for the satellite. The combined ingress and egress data do not show any significant hint for atmospheric refraction, allowing us to set surface pressure limits at the level of 10–20 nbar. More specifically, we find an upper limit of 13 nbar (1-σ level) at 70 K and 17 nbar at 80 K, for a putative isothermal CO2 atmosphere. We also provide an upper limit of 8 nbar for a possible CH4 atmosphere, and 22 nbar for pure N2, again at the 1-σ level. We finally constrain the stellar size using the time-resolved star disappearance and reappearance at ingress and egress. We find an angular diameter of 0.54±0.03 mas (corresponding to View the MathML source projected at Titania). With a distance of 170±25 parsecs, this corresponds to a radius of 9.8±0.2 solar radii for HIP 106829, typical of a K0 III giant. | ||||
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Publisher | ELSEVIER | Place of Publication | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 0019-1035 | ISBN | Medium | ||
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Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ Wid2009 | Serial | 1052 | ||
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Author | Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva | ||||
Title | CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation | Type | Journal Article | ||
Year | 2021 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 198 | Issue | Pages | 105792 | |
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Abstract | Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ PRR2021 | Serial | 3530 | ||
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Author | Stefan Lonn; Petia Radeva; Mariella Dimiccoli | ||||
Title | Smartphone picture organization: A hierarchical approach | Type | Journal Article | ||
Year | 2019 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 187 | Issue | Pages | 102789 | |
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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 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ LRD2019 | Serial | 3297 | ||
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Author | Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa | ||||
Title | Computer Vision based Food Grain Classification: a Comprehensive Survey | Type | Journal Article | ||
Year | 2021 | Publication | Computers and Electronics in Agriculture | Abbreviated Journal | CEA |
Volume | 187 | Issue | Pages | 106287 | |
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Abstract | This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented. | ||||
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Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ VSM2021 | Serial | 3576 | ||
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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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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ SRA2021 | Serial | 3633 | ||
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Author | Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas | ||||
Title | Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices | Type | Journal Article | ||
Year | 2016 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 175 | Issue | B | Pages | 866–876 |
Keywords | Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices | ||||
Abstract | During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset. | ||||
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Notes | LAMP; 600.072; 600.068; | Approved | no | ||
Call Number | Admin @ si @ TRM2016 | Serial | 2721 | ||
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Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana | ||||
Title | Context Proposals for Saliency Detection | Type | Journal Article | ||
Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 174 | Issue | Pages | 1-11 | |
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Abstract | One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions exist which potentially can all be salient. One way to prevent an exhaustive search over all object regions is by using object proposal algorithms. These return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated. In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD). |
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Notes | LAMP; 600.109; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ AWD2018 | Serial | 3241 | ||
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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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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ ADC2018 | Serial | 3022 | ||
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Author | Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera | ||||
Title | RGB-D-based Human Motion Recognition with Deep Learning: A Survey | Type | Journal Article | ||
Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 171 | Issue | Pages | 118-139 | |
Keywords | Human motion recognition; RGB-D data; Deep learning; Survey | ||||
Abstract | Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ WLO2018 | Serial | 3123 | ||
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Author | Andreea Glavan; Alina Matei; Petia Radeva; Estefania Talavera | ||||
Title | Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams | Type | Journal Article | ||
Year | 2021 | Publication | Expert Systems with Applications | Abbreviated Journal | ESWA |
Volume | 171 | Issue | Pages | 114506 | |
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Abstract | Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ GMR2021 | Serial | 3634 | ||
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Author | David Roche; Debora Gil; Jesus Giraldo | ||||
Title | Mechanistic analysis of the function of agonists and allosteric modulators: Reconciling two-state and operational models | Type | Journal Article | ||
Year | 2013 | Publication | British Journal of Pharmacology | Abbreviated Journal | BJP |
Volume | 169 | Issue | 6 | Pages | 1189-202 |
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Abstract | Two-state and operational models of both agonism and allosterism are compared to identify and characterize common pharmacological parameters. To account for the receptor-dependent basal response, constitutive receptor activity is considered in the operational models. By arranging two-state models as the fraction of active receptors and operational models as the fractional response relative to the maximum effect of the system, a one-by-one correspondence between parameters is found. The comparative analysis allows a better understanding of complex allosteric interactions. In particular, the inclusion of constitutive receptor activity in the operational model of allosterism allows the characterization of modulators able to lower the basal response of the system; that is, allosteric modulators with negative intrinsic efficacy. Theoretical simulations and overall goodness of fit of the models to simulated data suggest that it is feasible to apply the models to experimental data and constitute one step forward in receptor theory formalism. | ||||
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Notes | IAM; 600.044; 605.203 | Approved | no | ||
Call Number | IAM @ iam @ RGG2013b | Serial | 2195 | ||
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