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Multisensory 3D Saliency for Artificial Attention Systems

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In this paper we present proof-of-concept for a novel solution consisting of a short-term 3D memory for artificial attention systems, loosely inspired in perceptual processes believed to be implemented in the human brain. Our solution supports the implementation of multisen-sory perception and stimulus-driven processes of attention. For this purpose , it provides (1) knowledge persistence with temporal coherence tackling potential salient regions outside the field of view, via a panoramic, log-spherical inference grid; (2) prediction, by using estimates of local 3D velocity to anticipate the effect of scene dynamics; (3) spatial correspondence between volumetric cells potentially occupied by proto-objects and their corresponding multisensory saliency scores. Visual and auditory signals are processed to extract features that are then filtered by a proto-object segmentation module that employs colour and depth as discriminatory traits. We consider as features, apart from the commonly used colour and intensity contrast, colour bias, the presence of faces, scene dynamics and also loud auditory sources. Combining conspicuity maps derived from these features we obtain a 2D saliency map, which is then processed using the probability of occupancy in the scene to construct the final 3D saliency map as an additional layer of the Bayesian Volumetric Map (BVM) inference grid.
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... This requires the implementation of the representational skill, which toddlers achieve in early stages of development [12], and involves discovering the object that the interlocutor is referring to. One of the possible approaches discussed in the literature is to provide an internal representation of the environment that works as a short-term memory and stores important information for attention [4,8], and then use the gaze cue to modulate the potential attended objects. ...
... Regarding to attention, the information provided by the different sensors must be subjected to a spatial correspondence [3]. For instance, a sound source should be related with its potential origin location in order to make possible the correspondent attention action [8]. In fact, in overt attention, where the scene is partially observed and changes depending on the actions (e.g., head movements), if we do not enable the machine to have an internal representation of the environment with temporal registration, its actions will become reactive for each location and angles setup. ...
... In this work we will use the 3D log-spherical representation proposed in [4] to codify the perceived environment. The method is also valid when the saliency of the scene is modelled [8]. ...
Chapter
Human gaze is one of the most important cue for social robotics due to its embedded intention information. Discovering the location or the object that an interlocutor is staring at, gives the machine some insight to perform the correct attentional behaviour. This work presents a fast voxel traversal algorithm for estimating the potential locations that a human is gazing. Given a 3D occupancy map in log-spherical coordinates and the gaze vector, we evaluate the regions that are relevant for attention by computing the set of intersected voxels between an arbitrary gaze ray in the 3D space and a log-spherical bounded section defined by \(\rho \in (\rho _{min},\rho _{max}),\theta \in (\theta _{min},\theta _{max} ),\phi \in (\phi _{min},\phi _{max})\). The first intersected voxel is computed in closed form and the rest are obtained by binary search guaranteeing no repetitions in the intersected set. The proposed method is motivated and validated within a human-robot interaction application: gaze tracing for artificial attention systems.
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... On one hand, the image stream provided by the camera is processed by a visual attention system [21], that contributes with two main outputs: the saliency map with the protoobject relevance encoded in a 2D image and a list of attended protoobjects in the working memory. The spatial saliency is computed using several conspicuity maps that represent different features of the protoobjects [40]: color and intensity contrast, optical flow and color bias. These feature maps (2D images) are combined by weighted average using a fixed attentional set (weights), which in the case of having contextual information is used for top-down modulation. ...
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Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action. In other words, they do not consider where to sample next, given current beliefs. Here, we reclaim salience as an active inference process that relies on two basic principles: uncertainty minimisation and rhythmic scheduling. For this, we make a distinction between attention and salience. Briefly, we associate attention with precision control, i.e., the confidence with which beliefs can be updated given sampled sensory data, and salience with uncertainty minimisation that underwrites the selection of future sensory data. Using this, we propose a new account of attention based on rhythmic precision-modulation and discuss its potential in robotics, providing numerical experiments that showcase advantages of precision-modulation for state and noise estimation, system identification and action selection for informative path planning.
... Results showed that the robot was able to discern between inbody and outbody sources without using markers or simplified segmentation. Figure 1 shows the proto-object saliency system [4] used as visual input and the computed probability of the image regions belonging to the robot arm. Body perception was formalized as an inference problem while the robot was interacting with the world. ...
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... 3D Attention. Many works have been done on 3D human attention[Sugano et al., 2014;Jeni and Cohn, 2016;FunesMora and Odobez, 2016;Mansouryar et al., 2016;Chen et al., 2008;Lanillos et al., 2015]. Funes-Mora and Odobez[Funes-Mora and Odobez, 2016]estimated gaze directions based on head poses and eye images. ...
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... The robot, binding visual (saliency map with motion) and proprioceptive (accelerometers) cues, detects whether a pixel belongs to itself or not. This layer combines probabilistic inference grids with attentional maps [14]. To avoid the tracking of the robot parts 1st order dynamics (velocities) are learnt online. ...
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... Applications in education are envisioned when helping students in engineering to understand conventions in technical drawing. Other applications in computer vision would be interesting, for example when computing 3D attention saliency in proto-objects [42], a Q3D description could help to store a short memory narrative of the evolution of the proto-objects in these attention artificial systems. ...
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