Attending to visual motion

Tsotsos, John K.; Liu, Yueju; Martinez‐Trujillo, Julio; Pomplun, Marc; Simine, Evgueni; Zhou, Kunhao

doi:10.1016/j.cviu.2004.10.011

Cited by 75 publications

(65 citation statements)

References 82 publications

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“…In the UNI framework, this is equivalent to selectively tuning the spatial modulation functions s xx for particular maps. The ST model has been demonstrated successfully for motiondefined shapes (Tsotsos et al, 2005). Rybak et al (1998) proposed a model for learning and recognizing objects as combinations of their parts, with the relations between the parts encoded in saccade vectors.…”

Section: Other Models Of Spatial Attentionmentioning

confidence: 99%

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Attention in hierarchical models of object recognition

Walther

Koch

2007

Progress in Brain Research

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Object recognition and visual attention are tightly linked processes in human perception. Over the last three decades, many models have been suggested to explain these two processes and their interactions, and in some cases these models appear to contradict each other. We suggest a unifying framework for object recognition and attention and review the existing modeling literature in this context. Furthermore, we demonstrate a proof-of-concept implementation for sharing complex features between recognition and attention as a mode of top-down attention to particular objects or object categories."At first he'd most easily make out the shadows; and after that the phantoms of the human beings and the other things in water; and, later, the things themselves." -Socrates describing the visual experience of a man exposed to the richness of the visual world outside his cave for the first time (Plato, The Republic).

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Section: Other Models Of Spatial Attentionmentioning

confidence: 99%

“…A particular property chosen at the top of the hierarchy is selectively enhanced throughout the hierarchy to find an object with this property (Tsotsos et al, 1995;Tsotsos et al, 2005;Rothenstein and Tsotsos, 2006).…”

Section: Feature-based Attentionmentioning

confidence: 99%

Attention in hierarchical models of object recognition

Walther

Koch

2007

Progress in Brain Research

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“…We should note that, despite our focus on the Itti model, there exist many alternative computational models of visual attention both with and without motion including the work of Tsotsos et al (1995), Wolfe and Gancarz (1996), Breazeal and Scassellati (1999), Balkenius et al (2004), andTsotsos (2005). The analysis of these models is not addressed in this paper due to space considerations.…”

Section: Previous Workmentioning

confidence: 99%

A Behavioral Analysis of Computational Models of Visual Attention

Shic

Scassellati

2006

Int J Comput Vision

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Abstract. Robots often incorporate computational models of visual attention to streamline processing. Even though the number of visual attention systems employed on robots has increased dramatically in recent years, the evaluation of these systems has remained primarily qualitative and subjective. We introduce quantitative methods for evaluating computational models of visual attention by direct comparison with gaze trajectories acquired from humans. In particular, we focus on the need for metrics based not on distances within the image plane, but that instead operate at the level of underlying features. We present a framework, based on dimensionality-reduction over the features of human gaze trajectories, that can simultaneously be used for both optimizing a particular computational model of visual attention and for evaluating its performance in terms of similarity to human behavior. We use this framework to evaluate the Itti et al. (1998) model of visual attention, a computational model that serves as the basis for many robotic visual attention systems.

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“…Within the context of this paper, we distinguish four levels of tasks in the vision problem, which we label as follows [2]:…”

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confidence: 99%

Active 3D Object Localization Using a Humanoid Robot

et al. 2011

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Abstract-We study the problem of actively searching for an object in a 3D environment under the constraint of a maximum search time, using a visually guided humanoid robot with twentysix degrees of freedom. The inherent intractability of the problem is discussed and a greedy strategy for selecting the best next viewpoint is employed. We describe a target probability updating scheme approximating the optimal solution to the problem, providing an efficient solution to the selection of the best next viewpoint. We employ a hierarchical recognition architecture, inspired by human vision, that uses contextual cues for attending to the view-tuned units at the proper intrinsic scales and for active control of the robotic platform sensor's coordinate frame, also giving us control of the extrinsic image scale and achieving the proper sequence of pathognomonic views of the scene. The recognition model makes no particular assumptions on shape properties like texture and is trained by showing the object by hand to the robot. Our results demonstrate the feasibility of using state of the art vision-based systems for efficient and reliable object localization in an indoor 3D environment.

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Attending to visual motion

Cited by 75 publications

References 82 publications

Attention in hierarchical models of object recognition

Attention in hierarchical models of object recognition

A Behavioral Analysis of Computational Models of Visual Attention

Active 3D Object Localization Using a Humanoid Robot

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