Robot vision using a feature search strategy generated from a 3D object model

Kuno, Yoshinori; Okamoto, Y.; Okada, Shigeru

doi:10.1109/34.99241

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…Among the literature of recognition, many solutions are available (Kuno et al 1991 we may refer to the fast recognition by learning (Grewe and Kak 1995) and function-based reasoning (Sutton and Stark 2008), as well as multi-view recognition of time-varying geometry objects (Mackay and Benhabib 2008b). A review of sensor planning for active recognition can be found in Roy et al (2004).…”

Section: Recognitionmentioning

confidence: 99%

“…On the other hand, object recognition does also obviously need to analyze local surface features. The appearance of an object from various viewpoints is described in terms of visible 2D features, which are used for feature search and viewpoint decisions (Kuno et al 1991).…”

Section: Expectationmentioning

confidence: 99%

“…In another method of object recognition by Kuno et al (1991), features are ranked according to the number of viewpoints from which they are visible. The rank and feature extraction cost of each feature are used to generate a tree-like strategy graph.…”

Section: Viewpointmentioning

confidence: 99%

See 2 more Smart Citations

Active vision in robotic systems: A survey of recent developments

Chen

Kwok

2011

The International Journal of Robotics Research

366

204

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In this paper we provide a broad survey of developments in active vision in robotic applications over the last 15 years. With increasing demand for robotic automation, research in this area has received much attention. Among the many factors that can be attributed to a high-performance robotic system, the planned sensing or acquisition of perceptions on the operating environment is a crucial component. The aim of sensor planning is to determine the pose and settings of vision sensors for undertaking a vision-based task that usually requires obtaining multiple views of the object to be manipulated. Planning for robot vision is a complex problem for an active system due to its sensing uncertainty and environmental uncertainty. This paper describes such problems arising from many applications, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration. A bundle of solutions and methods have been proposed to solve these problems in the past. They are summarized in this review while enabling readers to easily refer solution methods for practical applications. Representative contributions, their evaluations, analyses, and future research trends are also addressed in an abstract level.

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Section: Recognitionmentioning

confidence: 99%

Section: Expectationmentioning

confidence: 99%

See 1 more Smart Citation

Active vision in robotic systems: A survey of recent developments

Chen

Kwok

2011

The International Journal of Robotics Research

366

204

View full text Add to dashboard Cite

show abstract

“…Specifically, it is important that the structures that are elliptical are indeed detected and the structures that are non-elliptical are not detected as (Antonaros and Petrou, 2001;Belaroussi et al, 2005;Bell et al, 2006;Burrill et al, 1996;Chia et al, 2009;Dijkers et al, 2005;Fernandes, 2009;Feyaerts et al, 2001;Foresti, 2002;Foresti et al, 2005;Fu and Huang, 1995;He et al, 2009;Hua et al, 2007;Hwang et al, 2006;Iles et al, 2007;Ji et al, 1999;Kayikcioglu et al, 2000;Kumar et al, 2009;Kuno et al, 1991;Liu et al, 2007;Lu et al, 2005;Matson et al, 1970;O'Leary et al, 2005;Prasad 2011c;Rosin and West, 1992;Salas et al, 2006;Shen et al, 2009;Shih et al, 2008;Smereka and Glab, 2006;Soetedjo and Yamada, 2005;Sood et al, 2005;Takimoto et al, 2004;Tang et al, 2000;Wang et al, 2006;Wu and Wang, 1993;Yuasa et al, 2004;Zaim et al, 2006;Zhang et al, 2003;Zhou and Shen, 2003;Zhou et al, 2009) ellipses. However, the problem of detecting ellipses in real images is very challenging due to many reasons.…”

Section: Introductionmentioning

confidence: 99%

Methods for Ellipse Detection from Edge Maps of Real Images

Prasad¹,

Leung²

2012

Machine Vision - Applications and Systems

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“…As a key quantity for sequence optimization, an estimate of features' posterior probability enters the new criterion. The method is an alternative to other, often more heuristic strategies aimed at producing a short search sequence, such as checking for model features in the order of the features' prior probability [7,12], in a coarse-to-fine hierarchy [6], or as predicted from the current scene interpretation [4,8].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Search for Object Segmentation and Recognition

Hillenbrand¹,

Hirzinger²

2002

Computer Vision — ECCV 2002

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Abstract. The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape.

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Robot vision using a feature search strategy generated from a 3D object model

Cited by 17 publications

References 18 publications

Active vision in robotic systems: A survey of recent developments

Active vision in robotic systems: A survey of recent developments

Methods for Ellipse Detection from Edge Maps of Real Images

Probabilistic Search for Object Segmentation and Recognition

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