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36
A computational model of stereoscopic 3D visual saliency
- SUBMITTED TO IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2011
"... Many computational models of visual attention performing well in predicting salient areas of 2D images have been proposed in the literature. The emerging applications of stereoscopic 3D display bring additional depth information affecting the human viewing behavior, and require extensions of the eff ..."
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Many computational models of visual attention performing well in predicting salient areas of 2D images have been proposed in the literature. The emerging applications of stereoscopic 3D display bring additional depth information affecting the human viewing behavior, and require extensions of the efforts made in 2D visual modeling. In this paper, we propose a new computational model of visual attention for stereoscopic 3D still image. Apart from detecting salient areas based on 2D visual features, the proposed model takes depth as an additional visual dimension. The measure of depth saliency is derived from the eye movement data obtained from an eye-tracking experiment using synthetic stimuli. Two different ways of integrating depth information in the modeling of 3D visual attention are then proposed and examined. For the performance evaluation of 3D visual attention models, we have created an eye-tracking database which contains stereoscopic images of natural content and is publicly available along with this paper. The proposed model gives a good performance, compared to that of state-of-the-art 2D models on 2D images. The results also suggest that a better performance is obtained when depth information is taken into account through the creation of a depth saliency map rather than when it is integrated by a weighting method.
A hierarchical generative model of recurrent ObjectBased attention in the visual cortex
- Proceedings of the International Conference on Artificial Neural Networks (ICANN-11
, 2011
"... Abstract. In line with recent work exploring Deep Boltzmann Machines (DBMs) as models of cortical processing, we demonstrate the potential of DBMs as models of object-based attention, combining generative principles with attentional ones. We show: (1) How inference in DBMs can be related qualitative ..."
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Abstract. In line with recent work exploring Deep Boltzmann Machines (DBMs) as models of cortical processing, we demonstrate the potential of DBMs as models of object-based attention, combining generative principles with attentional ones. We show: (1) How inference in DBMs can be related qualitatively to theories of attentional recurrent processing in the visual cortex; (2) that deepness and topographic receptive fields are important for realizing the attentional state; (3) how more explicit attentional suppressive mechanisms can be implemented, depending crucially on sparse representations being formed during learning. 1
Learning Generative Models with Visual Attention
"... Attention has long been proposed by psychologists to be important for efficiently dealing with the massive amounts of sensory stimulus in the neocortex. Inspired by the attention models in visual neuroscience and the need for object-centered data for generative models, we propose a deep-learning bas ..."
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Attention has long been proposed by psychologists to be important for efficiently dealing with the massive amounts of sensory stimulus in the neocortex. Inspired by the attention models in visual neuroscience and the need for object-centered data for generative models, we propose a deep-learning based generative frame-work using attention. The attentional mechanism propagates signals from the region of interest in a scene to an aligned canonical representation for genera-tive modeling. By ignoring scene background clutter, the generative model can concentrate its resources on the object of interest. A convolutional neural net is employed to provide good initializations during posterior inference which uses Hamiltonian Monte Carlo. Upon learning images of faces, our model can robustly attend to the face region of novel test subjects. More importantly, our model can learn generative models of new faces from a novel dataset of large images where the face locations are not known. 1
Learning hierarchical sparse representations using iterative dictionary learning and dimension reduction
- In Proc. of BICA
, 2011
"... This paper introduces an elemental building block which combines Dictionary Learning and Dimension Reduction (DRDL). We show how this foundational element can be used to iteratively construct a Hierar-chical Sparse Representation (HSR) of a sensory stream. We compare our approach to existing models ..."
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This paper introduces an elemental building block which combines Dictionary Learning and Dimension Reduction (DRDL). We show how this foundational element can be used to iteratively construct a Hierar-chical Sparse Representation (HSR) of a sensory stream. We compare our approach to existing models showing the generality of our simple prescription. We then perform preliminary experiments using this frame-work, illustrating with the example of an object recognition task using standard datasets. This work introduces the very first steps towards an integrated framework for designing and analyzing various computational tasks from learning to attention to action. The ultimate goal is building a mathematically rigorous, integrated theory of intelligence.
Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks. Cereb. Cortex 24, 1152–1164. doi: 10.1093/ cercor/bhs396
- Perception
, 2014
"... Perception is an active inferential process in which prior knowledge is combined with sensory input, the result of which determines the contents of awareness. Accordingly, previous experience is known to help the brain “decide ” what to perceive. However, a critical aspect that has not been addresse ..."
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Perception is an active inferential process in which prior knowledge is combined with sensory input, the result of which determines the contents of awareness. Accordingly, previous experience is known to help the brain “decide ” what to perceive. However, a critical aspect that has not been addressed is that previous experience can exert 2 opposing effects on perception: An attractive effect, sensi-tizing the brain to perceive the same again (hysteresis), or a repul-sive effect, making it more likely to perceive something else (adaptation). We used functional magnetic resonance imaging and modeling to elucidate how the brain entertains these 2 opposing processes, and what determines the direction of such experience-dependent perceptual effects. We found that although affecting our perception concurrently, hysteresis and adaptation map into distinct cortical networks: a widespread network of higher-order visual and fronto-parietal areas was involved in perceptual stabilization, while
M.: Augmented spatial pooling
- In: Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence
, 2011
"... Abstract. It is a widely held view in contemporary computational neuroscience that the brain responds to sensory input by producing sparse distributed representations. In this paper we investigate a brain-inspired spatial pooling algorithm that produces such sparse distributed representations by mod ..."
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Abstract. It is a widely held view in contemporary computational neuroscience that the brain responds to sensory input by producing sparse distributed representations. In this paper we investigate a brain-inspired spatial pooling algorithm that produces such sparse distributed representations by modelling the formation of proximal dendrites associated with neocortical minicolumns. In this approach, distributed representations are formed out of a competitive process of inter-column inhibition and subsequent learning. Specifically, we evaluate the performance of a recently proposed binary spatial pooling algorithm on a well-known benchmark of greyscale natural images. Our main contribution is to augment the algorithm to handle greyscale images, and to produce better quality encodings of binary images. We also show that the augmented algorithm produces superior population and lifetime kurtosis measures in comparison to a number of other well-known coding schemes. 1
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, 2010
"... We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that ne ..."
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We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and nonattended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.
Saliency in crowd
- In ECCV
, 2014
"... Abstract. Theories and models on saliency that predict where people look at focus on regular-density scenes. A crowded scene is characterized by the co-occurrence of a relatively large number of regions/objects that would have stood out if in a regular scene, and what drives attention in crowd can b ..."
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Abstract. Theories and models on saliency that predict where people look at focus on regular-density scenes. A crowded scene is characterized by the co-occurrence of a relatively large number of regions/objects that would have stood out if in a regular scene, and what drives attention in crowd can be significantly different from the conclusions in the regular setting. This work presents a first fo-cused study on saliency in crowd. To facilitate saliency in crowd study, a new dataset of 500 images is constructed with eye tracking data from 16 viewers and annotation data on faces (the dataset will be publicly available with the pa-per). Statistical analyses point to key observations on features and mechanisms of saliency in scenes with different crowd levels and provide insights as of whether conventional saliency models hold in crowding scenes. Finally a new model for saliency prediction that takes into account the crowding information is proposed, and multiple kernel learning (MKL) is used as a core computational module to integrate various features at both low- and high-levels. Extensive experiments demonstrate the superior performance of the proposed model compared with the state-of-the-art in saliency computation.
Modelling object perception in cortex: hierarchical Bayesian
"... networks and belief propagation ..."
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"... contributed articles IF PHYSICS WAS the science of the first half of the 20th century, biology was certainly the science of the second half. Neuroscience is now often cited as one of the key scientific focuses of the 21st century and has indeed grown rapidly in recent years, spanning a range of appr ..."
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contributed articles IF PHYSICS WAS the science of the first half of the 20th century, biology was certainly the science of the second half. Neuroscience is now often cited as one of the key scientific focuses of the 21st century and has indeed grown rapidly in recent years, spanning a range of approaches, from molecular neurobiology to neuro-informatics and computational neuroscience. Computer science gave biology powerful new data-analysis tools that yielded bioinformatics and genomics, making possible the sequencing of the human genome. Similarly, computer science techniques are at the heart of brain imaging and other branches of neuroscience. Computers are critical for the neurosciences,
