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| Author | David Berga; Xavier Otazu | ||||
| Title | Computations of inhibition of return mechanisms by modulating V1 dynamics | Type | Conference Article | ||
| Year | 2019 | Publication | 28th Annual Computational Neuroscience Meeting | Abbreviated Journal | |
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| Abstract | In this study we present a unifed model of the visual cortex for predicting visual attention using real image scenes. Feedforward mechanisms from RGC and LGN have been functionally modeled using wavelet filters at distinct orientations and scales for each chromatic pathway (Magno-, Parvo-, Konio-cellular) and polarity (ON-/OFF-center), by processing image components in the CIE Lab space. In V1, we process cortical interactions with an excitatory-inhibitory network of fring rate neurons, initially proposed by (Li, 1999), later extended by (Penacchio et al. 2013). Firing rates from model’s output have been used as predictors of neuronal activity to be projected in a map in superior colliculus (with WTA-like computations), determining locations of visual fxations. These locations will be considered as already visited areas for future saccades, therefore we integrated a spatiotemporal function of inhibition of return mechanisms (where LIP/FEF is responsible) to feed to the model with spatial memory for next saccades. Foveation mechanisms have been simulated with a cortical magnifcation function, which distort spatial viewing properties for each fxation. Results show lower prediction errors than with respect no IoR cases (Fig. 1), and it is functionally consistent with human psychophysical measurements. Our model follows a biologically-constrained architecture, previously shown to reproduce visual saliency (Berga & Otazu, 2018), visual discomfort (Penacchio et al. 2016), brightness (Penacchio et al. 2013) and chromatic induction (Cerda & Otazu, 2016). | ||||
| Address | Barcelona; July 2019 | ||||
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| Area | Expedition | Conference | CNS | ||
| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BeO2019a | Serial | 3373 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Computational modelingof visual attention: What do we know from physiology and psychophysics? | Type | Conference Article | ||
| Year | 2019 | Publication | 8th Iberian Conference on Perception | Abbreviated Journal | |
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| Abstract | Latest computer vision architectures use a chain of feedforward computations, mainly optimizing artificial neural networks for very specific tasks. Although their impressive performance (i.e. in saliency) using real image datasets, these models do not follow several biological principles of the human visual system (e.g. feedback and horizontal connections in cortex) and are unable to predict several visual tasks simultaneously. In this study we present biologically plausible computations from the early stages of the human visual system (i.e. retina and lateral geniculate nucleus) and lateral connections in V1. Despite the simplicity of these processes and without any type of training or optimization, simulations of firing-rate dynamics of V1 are able to predict bottom-up visual attention at distinct contexts (shown previously as well to predict visual discomfort, brightness and chromatic induction). We also show functional top-down selection mechanisms as feedback inhibition projections (i.e. prefrontal cortex for search/task-based attention and parietal area for inhibition of return). Distinct saliency model predictions are tested with eye tracking datasets in free-viewing and visual search tasks, using real images and synthetically-generated patterns. Results on predicting saliency and scanpaths show that artificial models do not outperform biologically-inspired ones (specifically for datasets that lack of common endogenous biases found in eye tracking experimentation), as well as, do not correctly predict contrast sensitivities in pop-out stimulus patterns. This work remarks the importance of considering biological principles of the visual system for building models that reproduce this (and any other) visual effects. | ||||
| Address | San Lorenzo El Escorial; July 2019 | ||||
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| Area | Expedition | Conference | CIP | ||
| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BeO2019b | Serial | 3374 | ||
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| Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Victor Leboran; Xose M. Pardo | ||||
| Title | Measuring bottom-up visual attention in eye tracking experimentation with synthetic images | Type | Conference Article | ||
| Year | 2019 | Publication | 8th Iberian Conference on Perception | Abbreviated Journal | |
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| Abstract | A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor pop-out type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. | ||||
| Address | San Lorenzo El Escorial; July 2019 | ||||
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| Area | Expedition | Conference | CIP | ||
| Notes | NEUROBIT; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ BFO2019c | Serial | 3375 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Computations of top-down attention by modulating V1 dynamics | Type | Conference Article | ||
| Year | 2020 | Publication | Computational and Mathematical Models in Vision | Abbreviated Journal | |
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| Address | St. Pete Beach; Florida; May 2020 | ||||
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| Area | Expedition | Conference | MODVIS | ||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ BeO2020a | Serial | 3376 | ||
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| Author | Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva | ||||
| Title | Towards Egocentric Person Re-identification and Social Pattern Analysis. | Type | Book Chapter | ||
| Year | 2019 | Publication | Frontiers in Artificial Intelligence and Applications | Abbreviated Journal | |
| Volume | 310 | Issue | Pages | 203 - 211 | |
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| Abstract | CoRR abs/1905.04073
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation. |
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TCP2019 | Serial | 3377 | ||
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| Author | Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva | ||||
| Title | Hierarchical approach to classify food scenes in egocentric photo-streams | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Journal of Biomedical and Health Informatics | Abbreviated Journal | J-BHI |
| Volume | 24 | Issue | 3 | Pages | 866 - 877 |
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| Abstract | Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TLM2020 | Serial | 3380 | ||
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| Author | Estefania Talavera; Nicolai Petkov; Petia Radeva | ||||
| Title | Towards Unsupervised Familiar Scene Recognition in Egocentric Videos | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1905.04093
Nowadays, there is an upsurge of interest in using lifelogging devices. Such devices generate huge amounts of image data; consequently, the need for automatic methods for analyzing and summarizing these data is drastically increasing. We present a new method for familiar scene recognition in egocentric videos, based on background pattern detection through automatically configurable COSFIRE filters. We present some experiments over egocentric data acquired with the Narrative Clip. |
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ TPR2019b | Serial | 3379 | ||
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| Author | Estefania Talavera; Petia Radeva; Nicolai Petkov | ||||
| Title | Towards Emotion Retrieval in Egocentric PhotoStream | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1905.04107
The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer's days, we propose a method that aims to assign sentiment to events extracted from egocentric photostreams. Such moments can be candidates to retrieve according to their possibility of representing a positive experience for the camera's wearer. The proposed approach obtained a classification accuracy of 75% on the test set, with a deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams. |
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TRP2019 | Serial | 3381 | ||
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| Author | Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli | ||||
| Title | How Much Does Audio Matter to Recognize Egocentric Object Interactions? | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1906.00634
Sounds are an important source of information on our daily interactions with objects. For instance, a significant amount of people can discern the temperature of water that it is being poured just by using the sense of hearing. However, only a few works have explored the use of audio for the classification of object interactions in conjunction with vision or as single modality. In this preliminary work, we propose an audio model for egocentric action recognition and explore its usefulness on the parts of the problem (noun, verb, and action classification). Our model achieves a competitive result in terms of verb classification (34.26% accuracy) on a standard benchmark with respect to vision-based state of the art systems, using a comparatively lighter architecture. |
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ CLR2019 | Serial | 3383 | ||
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| Author | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig | ||||
| Title | MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1907.00856
Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%. |
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ MRA2019 | Serial | 3384 | ||
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| Author | Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli | ||||
| Title | Seeing and Hearing Egocentric Actions: How Much Can We Learn? | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 4470-4480 | ||
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| Abstract | Our interaction with the world is an inherently multimodal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial, and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a 5.18% improvement over the state of the art on verb classification. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ CLR2019b | Serial | 3385 | ||
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| Author | Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
| Title | Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images | Type | Journal Article | ||
| Year | 2020 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
| Volume | 10 | Issue | 22 | Pages | 8170 |
| Keywords | sentiment analysis, personality trait analysis; weakly-supervised learning; visual classification; OCEAN model; social networks | ||||
| Abstract | Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions. | ||||
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| Notes | ISE; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RVC2020b | Serial | 3553 | ||
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| Author | Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde | ||||
| Title | Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain | Type | Journal Article | ||
| Year | 2021 | Publication | Biomedical Signal Processing and Control | Abbreviated Journal | BSPC |
| Volume | 68 | Issue | Pages | 102535 | |
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| Abstract | Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ DGR2021b | Serial | 3636 | ||
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| Author | Sergio Escalera; Stephane Ayache; Jun Wan; Meysam Madadi; Umut Guçlu; Xavier Baro | ||||
| Title | Inpainting and Denoising Challenges | Type | Book Whole | ||
| Year | 2019 | Publication | The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
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| Abstract | The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting.
Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting. |
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EAW2019 | Serial | 3398 | ||
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| Author | Hugo Jair Escalante; Sergio Escalera; Isabelle Guyon; Xavier Baro; Yagmur Gucluturk; Umut Guçlu; Marcel van Gerven | ||||
| Title | Explainable and Interpretable Models in Computer Vision and Machine Learning | Type | Book Whole | ||
| Year | 2018 | Publication | The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
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| Abstract | This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: ·Evaluation and Generalization in Interpretable Machine Learning ·Explanation Methods in Deep Learning ·Learning Functional Causal Models with Generative Neural Networks ·Learning Interpreatable Rules for Multi-Label Classification ·Structuring Neural Networks for More Explainable Predictions ·Generating Post Hoc Rationales of Deep Visual Classification Decisions ·Ensembling Visual Explanations ·Explainable Deep Driving by Visualizing Causal Attention ·Interdisciplinary Perspective on Algorithmic Job Candidate Search ·Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions ·Inherent Explainability Pattern Theory-based Video Event Interpretations |
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EEG2018 | Serial | 3399 | ||
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