1,721,016 research outputs found
Preface to the Second Workshop on Artificial Intelligence for Human-Machine Interaction (AIxHMI)
No full textThe human-machine interaction (HMI) field is benefiting from the latest advances in wearable devices, sensing technologies, and artificial intelligence (AI) models. This is allowing the development of new applications in real-life, virtual and augmented environments, where AI is assuming a significant role especially considering strongly human-centered, real-time, and noisy scenarios. The main motivations and relevance of the Artificial Intelligence for Human-Machine Interaction (AIxHMI) workshop regard (i) focusing on human-centered approaches and perspectives, (ii) exploiting AI approaches to provide a better interaction between humans and machines, (iii) moving AI to pervasive technologies relying on wearable devices and portable technologies, and (iv) presenting novel AI methods and proposals that exploit heterogeneous data sources, describing the human environmental interaction in real, virtual, and augmented scenarios. Seven papers have been submitted to the second edition of AIxHMI. Out of these, six have been accepted for this volume as regular papers. Diverse fields of HMI were touched by these authors as well as by the three invited speakers
Human perception of image complexity: real scenes versus texture patches
No full textThe aim of this work is to study image complexity perception of real images. We conducted psycho-physical experiments where observers judged the complexity of different datasets of images on a web-based interface [1]. At the end of the test, observers indicated the main characteristics that guided their judgements. The databases differed in the type of visual stimuli used: images representing real scenes and/or texture patches. For real scenes the most relevant criteria used were quantity of objects, details and colors, while for texture patches they were regularity and understandability. Several criteria are adopted simultaneously, confirming the multidimensional aspect of complexity found in the literature [2]. To process the subjective data we applied z-scores and outlier removal. The mean scores are then correlated with different visual features. We considered features based on spatial, color and frequency properties that can be associated to bottom-up processes. To take into account top-down effects like understandability we included a memorability index [3]. We propose an image complexity measure where the features are linearly combined. The optimal weighting coefficients are those that best fit the subjective data and depend on the type of stimuli considered. Our measure, properly tuned, can predict complexity perception of different kind of images, outperforming the single visual features. From our investigation two aspects of image complexity can be underlined: many different perceptual properties are involved and their relative influence depends on the type of stimuli. These considerations are supported by both our computational proposal and the verbal description analysis.
[1] Ciocca G, Corchs S, Gasparini F, Bricolo E, Tebano R. Does color influence image complexity perception? In: Fifth IAPR Computational Color Imaging Workshop vol. 9016 of Lecture Notes in Computer Science. Springer Berlin/Heidelberg; ((2015) ):139–148
[2] Oliva A, Mack ML, Shrestha M. Identifying the Perceptual Dimensions of Visual Complexity of Scenes. In: Proc. 26th Annual Meeting of the Cognitive Science Society ((2004) ):101–106
[3] Isola P, Xiao J, Torralba A, and Oliva A. What makes an image memorable? In IEEE Conference on Computer Vision and Pattern Recognition ((2011) ):145–15
Large-scale neural model for visual attention: Integration of experimental single cell and fMRI data
No full textA computational neuroscience framework is proposed to better understand the role and the neuronal correlate of spatial attention modulation in visual perception. The model consists of several interconnected modules that can be related to the different areas of the dorsal and ventral paths of the visual cortex. Competitive neural interactions are implemented at both microscopic and interareal levels, according to the biased competition hypothesis. This hypothesis has been experimentally confirmed in studies in humans using functional magnetic resonance imaging (fMRI) techniques and also in single-cell recording studies in monkeys. Within this neurodynamical approach, numerical simulations are carried out that describe both the fMRI and the electrophysiological data. The proposed model draws together data of different spatial and temporal resolution, as are the above-mentioned imaging and single-cell results
A neurodynamical model to simulate neural activities in visual attention experiments
No full textIn the present work we follow a computational neuroscience approach in order to study the role of attention in visual perception. According to the biased competition hypothesis, when multiple stimuli are present in the visual field, populations of neurons are activated that engage in competitive interactions. Experimental studies in humans using functional magnetic resonance imaging (fMRI) techniques confirm such hypothesis. Here, we present a model to simulate these experimental data within a biased competition neurodynamics. The model consists of several interconnected modules which can be related with the different areas of the dorsal and ventral paths of the visual cortex. © 2002 Elsevier Science B.V. All rights reserved
Electron capture from H2 targets by H+ and He2+ ions. Dependence of the cross sections on the orientation of the molecular axis
No full textSelective attention in visual search via synchronization of phase oscillators in a multimodular neural network
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