1,004 research outputs found
Visual Attention Mechanism for a Social Robot
This paper describes a visual perception system for a social robot. The central part of this system is an artificial attention mechanism that discriminates the most relevant information from all the visual information perceived by the robot. It is composed by three stages. At the preattentive stage, the concept of saliency is implemented based on ‘proto-objects’ [37]. From these objects, different saliency maps are generated. Then, the semiattentive stage identifies and tracks significant items according to the tasks to accomplish. This tracking process allows to implement the ‘inhibition of return’. Finally, the attentive stage fixes the field of attention to the most relevant object depending on the behaviours to carry out. Three behaviours have been implemented and tested which allow the robot to detect visual landmarks in an initially unknown environment, and to recognize and capture the upper-body motion of people interested in interact with it
Multi-feature Bottom-up Processing and Top-down Selection for an Object-based Visual Attention Model
Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. This paper presents an attention model which draws attention over perceptual units of visual information, called proto-objects, and which uses a linear combination of multiple low-level features (such as colour, symmetry or shape) in order to calculate the saliency of each of them. But not only bottom-up processing is addressed, the proposed model also deals with the top-down component of attention. It is shown how a high-level task can modulate the global saliency computation, modifying the weights involved in the basic features linear combination.Ministerio de EconomÃa y Competitividad (MINECO), proyectos: TIN2008-06196 y TIN2012-38079-C03-03. Campus de Excelencia Internacional AndalucÃa Tech
From perception to action and vice versa: a new architecture showing how perception and action can modulate each other simultaneously
Presentado en: 6th European Conference on Mobile Robots (ECMR) Sep 25-27, 2013 Barcelona, SpainArtificial vision systems can not process all the
information that they receive from the world in real time
because it is highly expensive and inefficient in terms of
computational cost. However, inspired by biological perception
systems, it is possible to develop an artificial attention model
able to select only the relevant part of the scene, as human
vision does. From the Automated Planning point of view, a
relevant area can be seen as an area where the objects involved
in the execution of a plan are located. Thus, the planning system
should guide the attention model to track relevant objects. But,
at the same time, the perceived objects may constrain or provide
new information that could suggest the modification of a current
plan. Therefore, a plan that is being executed should be adapted
or recomputed taking into account actual information perceived
from the world. In this work, we introduce an architecture that
creates a symbiosis between the planning and the attention
modules of a robotic system, linking visual features with high
level behaviours. The architecture is based on the interaction of
an oversubscription planner, that produces plans constrained
by the information perceived from the vision system, and an
object-based attention system, able to focus on the relevant
objects of the plan being executed.Spanish MINECO projects TIN2008-06196, TIN2012-38079-C03-03 and TIN2012-38079-C03-02. Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tec
Combining segmentation and attention: a new foveal attention model
Artificial vision systems cannot process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. Inspired by biological perception systems, artificial attention models pursuit to select only the relevant part of the scene. On human vision, it is also well established that these units of attention are not merely spatial but closely related to perceptual objects (proto-objects). This implies a strong bidirectional relationship between segmentation and attention processes. While the segmentation process is the responsible to extract the proto-objects from the scene, attention can guide segmentation, arising the concept of foveal attention. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. In this paper, a bottom-up foveal attention model is presented. In this model the input image is a foveal image represented using a Cartesian Foveal Geometry (CFG), which encodes the field of view of the sensor as a fovea (placed in the focus of attention) surrounded by a set of concentric rings with decreasing resolution. Then multi-resolution perceptual segmentation is performed by building a foveal polygon using the Bounded Irregular Pyramid (BIP). Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image proto-object. Saliency is computed as a linear combination of multiple low level features such as color and intensity contrast, symmetry, orientation and roundness. Obtained results from natural images show that the performance of the combination of hierarchical foveal segmentation and saliency estimation is good in terms of accuracy and speed
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