| Records |
| Author |
Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell |
| Title |
Segmentation of Distal Airways using Structural Analysis |
Type |
Journal Article |
| Year |
2019 |
Publication |
PloS one |
Abbreviated Journal |
Plos |
| Volume |
14 |
Issue |
12 |
Pages |
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| Abstract |
Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution. |
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| Notes |
IAM; 600.139; 600.145 |
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no |
| Call Number |
Admin @ si @ GSB2019 |
Serial |
3357 |
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| Author |
Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil |
| Title |
Selection of Radiomics Features based on their Reproducibility |
Type |
Conference Article |
| Year |
2019 |
Publication |
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
403-408 |
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| Abstract |
Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network. |
| Address |
Berlin; Alemanya; July 2019 |
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EMBC |
| Notes |
IAM; 600.139; 600.145 |
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no |
| Call Number |
Admin @ si @ LTS2019 |
Serial |
3358 |
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| Author |
Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil |
| Title |
Back to Front Architecture for Diagnosis as a Service |
Type |
Conference Article |
| Year |
2018 |
Publication |
20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
Abbreviated Journal |
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Issue |
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Pages |
343-346 |
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| Abstract |
Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction. |
| Address |
Timisoara; Rumania; September 2018 |
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SYNASC |
| Notes |
IAM; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ SVA2018 |
Serial |
3360 |
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| Author |
Debora Gil; Antoni Rosell |
| Title |
Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? |
Type |
Abstract |
| Year |
2019 |
Publication |
World Lung Cancer Conference |
Abbreviated Journal |
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Invited speaker |
| Address |
Barcelona; September 2019 |
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IASLC WCLC |
| Notes |
IAM; 600.139; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ GiR2019 |
Serial |
3361 |
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| Author |
Rada Deeb; Joost Van de Weijer; Damien Muselet; Mathieu Hebert; Alain Tremeau |
| Title |
Deep spectral reflectance and illuminant estimation from self-interreflections |
Type |
Journal Article |
| Year |
2019 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
JOSA A |
| Volume |
31 |
Issue |
1 |
Pages |
105-114 |
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| Abstract |
In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches. |
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| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ DWM2019 |
Serial |
3362 |
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| Author |
Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz |
| Title |
SDIT: Scalable and Diverse Cross-domain Image Translation |
Type |
Conference Article |
| Year |
2019 |
Publication |
27th ACM International Conference on Multimedia |
Abbreviated Journal |
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Issue |
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Pages |
1267–1276 |
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Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. |
| Address |
Nice; Francia; October 2019 |
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ACM-MM |
| Notes |
LAMP; 600.106; 600.109; 600.141; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ WGW2019 |
Serial |
3363 |
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| Author |
Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit |
| Title |
Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding |
Type |
Journal Article |
| Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
149 |
Issue |
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Pages |
164-171 |
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| Abstract |
Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. |
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| Notes |
DAG; 600.121 |
Approved |
no |
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Admin @ si @ DGV2021 |
Serial |
3364 |
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| Author |
Mohammed Al Rawi; Ernest Valveny |
| Title |
Compact and Efficient Multitask Learning in Vision, Language and Speech |
Type |
Conference Article |
| Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
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Pages |
2933-2942 |
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Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains. |
| Address |
Seul; Korea; October 2019 |
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ICCVW |
| Notes |
DAG; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ RaV2019 |
Serial |
3365 |
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| Author |
Md.Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig |
| Title |
Food Places Classification in Egocentric Images Using Siamese Neural Networks |
Type |
Conference Article |
| Year |
2019 |
Publication |
22nd International Conference of the Catalan Association of Artificial Intelligence |
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145-151 |
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Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2. |
| Address |
Illes Balears; October 2019 |
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CCIA |
| Notes |
MILAB; no proj |
Approved |
no |
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Admin @ si @ SBR2019 |
Serial |
3368 |
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| Author |
Eduardo Aguilar; Petia Radeva |
| Title |
Food Recognition by Integrating Local and Flat Classifiers |
Type |
Conference Article |
| Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
11867 |
Issue |
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65-74 |
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The recognition of food image is an interesting research topic, in which its applicability in the creation of nutritional diaries stands out with the aim of improving the quality of life of people with a chronic disease (e.g. diabetes, heart disease) or prone to acquire it (e.g. people with overweight or obese). For a food recognition system to be useful in real applications, it is necessary to recognize a huge number of different foods. We argue that for very large scale classification, a traditional flat classifier is not enough to acquire an acceptable result. To address this, we propose a method that performs prediction with local classifiers, based on a class hierarchy, or with flat classifier. We decide which approach to use, depending on the analysis of both the Epistemic Uncertainty obtained for the image in the children classifiers and the prediction of the parent classifier. When our criterion is met, the final prediction is obtained with the respective local classifier; otherwise, with the flat classifier. From the results, we can see that the proposed method improves the classification performance compared to the use of a single flat classifier. |
| Address |
Madrid; July 2019 |
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LNCS |
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IbPRIA |
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MILAB; no proj |
Approved |
no |
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Admin @ si @ AgR2019b |
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3369 |
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| Author |
Emanuel Sanchez Aimar; Petia Radeva; Mariella Dimiccoli |
| Title |
Social Relation Recognition in Egocentric Photostreams |
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Conference Article |
| Year |
2019 |
Publication |
26th International Conference on Image Processing |
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3227-3231 |
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This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal. |
| Address |
Taipei; Taiwan; September 2019 |
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ICIP |
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MILAB; no menciona |
Approved |
no |
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Admin @ si @ SRD2019 |
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3370 |
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| Author |
C. Alejandro Parraga; Xavier Otazu; Arash Akbarinia |
| Title |
Modelling symmetry perception with banks of quadrature convolutional Gabor kernels |
Type |
Conference Article |
| Year |
2019 |
Publication |
42nd edition of the European Conference on Visual Perception |
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224-224 |
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Mirror symmetry is a property most likely to be encountered in animals than in medium scale vegetation or inanimate objects in the natural world. This might be the reason why the human visual system has evolved to detect it quickly and robustly. Indeed, the perception of symmetry assists higher-level visual processing that are crucial for survival such as target recognition and identification irrespective of position and location. Although the task of detecting symmetrical objects seems effortless to us, it is very challenging for computers (to the extent that it has been proposed as a robust “captcha” by Funk & Liu in 2016). Indeed, the exact mechanism of symmetry detection in primates is not well understood: fMRI studies have shown that symmetrical shapes activate specific higher-level areas of the visual cortex (Sasaki et al.; 2005) and similarly, a large body of psychophysical experiments suggest that the symmetry perception is critically influenced by low-level mechanisms (Treder; 2010). In this work we attempt to find plausible low-level mechanisms that might form the basis for symmetry perception. Our simple model is made from banks of (i) odd-symmetric Gabors (resembling edge-detecting V1 neurons); and (ii) banks of larger odd- and even-symmetric Gabors (resembling higher visual cortex neurons), that pool signals from the 'edge image'. As reported previously (Akbarinia et al, ECVP2017), the convolution of the symmetrical lines with the two Gabor kernels of alternative phase produces a minimum in one and a maximum in the other (Osorio; 1996), and the rectification and combination of these signals create lines which hint of mirror symmetry in natural images. We improved the algorithm by combining these signals across several spatial scales. Our preliminary results suggest that such multiscale combination of convolutional operations might form the basis for much of the operation of the HVS in terms of symmetry detection and representation. |
| Address |
Leuven; Belgium; August 2019 |
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ECVP |
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NEUROBIT; 600.128 |
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no |
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Admin @ si @ POA2019 |
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3371 |
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| Author |
David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Xose M. Pardo |
| Title |
SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention Modeling |
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Conference Article |
| Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
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8788-8797 |
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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 popout 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-ofthe-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 |
Seul; Corea; October 2019 |
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ICCV |
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NEUROBIT; 600.128 |
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no |
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Admin @ si @ BFO2019b |
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3372 |
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| Author |
David Berga; Xavier Otazu |
| Title |
Computations of inhibition of return mechanisms by modulating V1 dynamics |
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Conference Article |
| Year |
2019 |
Publication |
28th Annual Computational Neuroscience Meeting |
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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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CNS |
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NEUROBIT; no menciona |
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no |
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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? |
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Conference Article |
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2019 |
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8th Iberian Conference on Perception |
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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. |
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San Lorenzo El Escorial; July 2019 |
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CIP |
| Notes |
NEUROBIT; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BeO2019b |
Serial |
3374 |
| Permanent link to this record |
