3D object recognition using shape similiarity-based aspect graph

Cyr, C.M.; Kimia, Benjamin B.

doi:10.1109/iccv.2001.937526

Cited by 139 publications

(107 citation statements)

References 22 publications

(12 reference statements)

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“…There is substantial cognitive evidence that recognition of 3D objects can be based on 2D projections, and planar shape similarity measure can be used in this context (Cyr and Kimia [10]). …”

Section: Relevant Workmentioning

confidence: 99%

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Optimal partial shape similarity

Latecki

Lakaemper

Wolter

2005

Image and Vision Computing

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Humans are able to recognize objects in the presence of significant amounts of occlusion and changes in the view angle. In human and robot vision, these conditions are normal situations and not exceptions. In digital images one more problem occurs due to unstable outcomes of the segmentation algorithms. Thus, a normal case is that a given shape is only partially visible, and the visible part is distorted. To our knowledge there does not exist a shape representation and similarity approach that could work under these conditions. However, such an approach is necessary to solve the object recognition problem. The main contribution of this paper is the definition of an optimal partial shape similarity measure that works under these conditions. In particular, the presented novel approach to shape-based object recognition works even if only a small part of a given object is visible and the visible part is significantly distorted, assuming the visible part is distinctive. q

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Section: Relevant Workmentioning

confidence: 99%

“…The shape of 3D objects can be recognized using their 2D projections [10,22]. The key observation is that the contour variation in the projection of a single visual part is significantly smaller than the variation in the projection of the whole 3D object.…”

Section: Introductionmentioning

confidence: 99%

Optimal partial shape similarity

Latecki

Lakaemper

Wolter

2005

Image and Vision Computing

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“…Visual object tracking is an active research area [1], with many different algorithms developed that get broadly categorized in either geometrical [2] or appearance [3] based techniques. Real-time applications in autonomous robots typically require remote computers, with few recent exceptions ( [4], [5]) that need precisely calibrated sensors and high computational resources devoted to the tracking system.…”

Section: Introductionmentioning

confidence: 99%

Self-calibrating Marker Tracking in 3D with Event-Based Vision Sensors

Muller

Conradt

2012

Artificial Neural Networks and Machine Learning – ICANN 2012

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Abstract.Following an object's position relative to oneself is a fundamental functionality required in intelligent real-world interacting robotic systems. This paper presents a computationally efficient vision based 3D tracking system, which can ultimately operate in real-time on autonomous mobile robots in cluttered environments. At the core of the system, two neural inspired eventbased dynamic vision sensors (eDVS) independently track a high frequency flickering LED in their respective 2D angular coordinate frame. A self-adjusted feed-forward neural network maps those independent 2D angular coordinates into a Cartesian 3D position in world coordinates. During an initial calibration phase, an object composed of multiple independent markers with known geometry provides relative position information between those markers for network training (without ever using absolute world coordinates for training). In a subsequent application phase tracking a single marker yields position estimates relative to sensor origin, while tracking multiple markers provides additional orientation. The neural inspired vision-based tracking system runs in real-time on ARM7 microcontrollers, without the need for an external PC.

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“…Different objects will have different prototype or aspect views [4]. However, we can envision a canonical set of optimal views for specific object classes with notably high intraclass similarity such as the human face.…”

Section: Introductionmentioning

confidence: 99%

“…Here we make use of aspect views as proposed by Cyr & Kimia [4] for general 3D object recognition. An aspect view is the silhou- ette projection of the object from a viewpoint which represents a range of similar nearby views in the space of uniformly sampled view-sphere.…”

Section: Introductionmentioning

confidence: 99%

Finding optimal views for 3D face shape modeling

Lee¹,

Moghaddam²,

Pfister³

et al.

Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.

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A fundamental problem in multi-view 3D face modeling is the determination of the set of optimal views (poses) required for accurate 3D shape estimation of a generic face. There is no analytical solution to this problem, instead (partial) solutions require (near) exhaustive combinatorial search, hence the inherent computational difficulty of this task. We build on our previous modeling framework which uses an efficient contour-based silhouette matching method and extend it by aggressive pruning of the view-sphere with view clustering and imposing various imaging constraints. A multiview optimization search is performed using both model-based (eigenhead) and datadriven (visual hull) methods, yielding comparable best views. These constitute the first reported set of optimal views for 3D face shape capture and should provide useful empirical guidelines for the design of 3D face recognition systems. Published in: IEEE Int'l Conf. on Automatic Face & Gesutre Recognition (FG'04)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

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3D object recognition using shape similiarity-based aspect graph

Cited by 139 publications

References 22 publications

Optimal partial shape similarity

Optimal partial shape similarity

Self-calibrating Marker Tracking in 3D with Event-Based Vision Sensors

Finding optimal views for 3D face shape modeling

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