Overt visual attention for a humanoid robot

Vijayakumar, Sethu; Conradt, Jörg; Shibata, Tomohiro; Schaal, Stefan

doi:10.1109/iros.2001.976418

Cited by 68 publications

(48 citation statements)

References 10 publications

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“…Recently the models have been used to boost some computer vision and pattern recognition techniques such as object detection [113], [124], [125], object recognition [126]- [131], action recognition [132], [133], segmentation [37], [114], [115], [134], [135] and background subtraction [136]. Besides, specific applications include video summarization [137] and compression [138], scene understanding [139]- [141], computer-human interaction [98], [142]- [147], robotics [132], [148]- [150], and driver assistance [151], [152]. The potential of the visual attention models that are capable of extracting important regions promises their contributions to many other domains.…”

Section: Discussionmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation. key words : human visual attention, computational model, saliency, bottom-up, top-down MotivationDeveloping sophisticated algorithms for detecting and recognizing something like objects from a given image and video has been a long distance challenge in pattern recognition and computer vision research fields. In fact, a huge number of studies, techniques and theories related to object detection and recognition have already been developed. In particular, several methods for detecting certain specific categories of objects such as human bodies and human faces have already been put to practical use in for example surveillance, authentication and the human-centric enhancement of image quality, with the best possible use of the prior knowledge of target objects (human bodies and faces) [1], [2]. However, generic object detection and recognition without any constraints as regards the target objects has remained major challenge, because (1) various kinds of objects might constitute the targets and (2) target objects in the same category might have different appearances due to variations of instances in a specific category, illumination changes and so on. † † The author is with the Graduate School of Informatics, Kyoto University, Kyoto-shi, 606-8501 Japan.† † † The author is with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464-8603 Japan.a) E-mail: akisato@ieee.org b) E-mail: yonetani@vision.kuee.kyoto-u.ac.jp c) E-mail: hirayama@is.nagoya-u.ac.jp DOI: 10.1587/transinf.E96.D.562 On the other hand, human beings seem to be able to detect various kinds of objects without any thought or effort. For example, from Fig. 1 left, we can easily and instantly detect a red car, a blue traffic sign and a broad white line. Visual attention [3] is considered to play an important role in achieving this function. Visual attention is one of the built-in mechanisms of the human visual system that quickly selects regions in a visual scene, which are most likely to contain items of interest. Such a pre-selection mechanism focusing only on relevant data would be essential in enabling computers to undertake subsequent processing such as generic o...

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Section: Discussionmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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“…the act of directing the sensors towards salient stimuli, and scene exploration for robotic applications has been addressed by several authors in recent years (see, e.g., [3], [5], [7], [9], [27]- [29]). The main difference between (covert) attention mechanisms that operate on still images, i.e.…”

Section: Related Workmentioning

confidence: 99%

Multimodal saliency-based attention for object-based scene analysis

Schauerte¹,

Kuhn²,

Kroschel³

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Multimodal attention is a key requirement for humanoid robots in order to navigate in complex environments and act as social, cognitive human partners. To this end, robots have to incorporate attention mechanisms that focus the processing on the potentially most relevant stimuli while controlling the sensor orientation to improve the perception of these stimuli. In this paper, we present our implementation of audio-visual saliency-based attention that we integrated in a system for knowledge-driven audio-visual scene analysis and object-based world modeling. For this purpose, we introduce a novel isophote-based method for proto-object segmentation of saliency maps, a surprise-based auditory saliency definition, and a parametric 3-D model for multimodal saliency fusion. The applicability of the proposed system is demonstrated in a series of experiments.Index Terms-audio-visual saliency, auditory surprise, isophote-based visual proto-objects, parametric 3-D saliency model, object-based inhibition of return, multimodal attention, scene exploration, hierarchical object analysis, overt attention, active perception

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“…Studies on oculomotor control in humanoid robots include vestibulo-ocular and optokinetic reflex, smooth pursuit, saccades, and vergence control (Manzotti et al, 2001;Panerai et al, 2000;Rougeaux & Kuniyoshi, 1998;Shibata et al, 2001). On the image processing side, researchers studied humanoid vision for visual attention (Breazeal et al, 2001;Vijayakumar et al, 2001), segmentation (Fitzpatrick, 2003), and tracking (Metta et al, 2004;Rougeaux & Kuniyoshi, 1998). The utilization of foveation for object recognition was not of major concern in these papers.…”

Section: Introductionmentioning

confidence: 99%

Interactive Object Learning and Recognition with Multiclass Support Vector Machines

Ude¹

2010

Robot Vision

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Overt visual attention for a humanoid robot

Cited by 68 publications

References 10 publications

Computational Models of Human Visual Attention and Their Implementations: A Survey

Computational Models of Human Visual Attention and Their Implementations: A Survey

Multimodal saliency-based attention for object-based scene analysis

Interactive Object Learning and Recognition with Multiclass Support Vector Machines

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