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David Berga; Xavier Otazu |
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Title |
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 |
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2020 |
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Neurocomputing |
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NEUCOM |
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417 |
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270-289 |
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Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts. |
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NEUROBIT |
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Admin @ si @ BeO2020c |
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3444 |
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David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; V. Leboran; Xose M. Pardo |
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Title |
Psychophysical evaluation of individual low-level feature influences on visual attention |
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Journal Article |
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2019 |
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Vision Research |
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VR |
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154 |
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60-79 |
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Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset |
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In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images. |
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NEUROBIT; 600.128; 600.120 |
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Admin @ si @ BFO2019a |
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3274 |
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Arash Akbarinia; C. Alejandro Parraga |
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Title |
Feedback and Surround Modulated Boundary Detection |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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12 |
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1367–1380 |
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Boundary detection; Surround modulation; Biologically-inspired vision |
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Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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NEUROBIT; 600.068; 600.072 |
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Admin @ si @ AkP2018b |
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2991 |
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Author |
David Berga; Xavier Otazu; Xose R. Fernandez-Vidal; Victor Leboran; Xose M. Pardo |
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Title |
Generating Synthetic Images for Visual Attention Modeling |
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2019 |
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Perception |
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PER |
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48 |
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99 |
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NEUROBIT; no menciona |
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Admin @ si @ BOF2019 |
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3309 |
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C. Alejandro Parraga |
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Title |
Colours and Colour Vision: An Introductory Survey |
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2017 |
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Perception |
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PER |
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46 |
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5 |
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640-641 |
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NEUROBIT; no menciona |
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Par2017 |
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3101 |
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