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Embodied Object Recognition using Adaptive Target Observations
Linköpings universitet, Institutionen för systemteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-5698-5983
2010 (engelsk)Inngår i: Cognitive Computation, ISSN 1866-9956, E-ISSN 1866-9964, Vol. 2, nr 4, s. 316-325Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this paper, we study object recognition in the embodied setting. More specifically, we study the problem of whether the recognition system will benefit from acquiring another observation of the object under study, or whether it is time to give up, and report the observed object as unknown. We describe the hardware and software of a system that implements recognition and object permanence as two nested perception-action cycles. We have collected three data sets of observation sequences that allow us to perform controlled evaluation of the system behavior. Our recognition system uses a KNN classifier with bag-of-features prototypes. For this classifier, we have designed and compared three different uncertainty measures for target observation. These measures allow the system to (a) decide whether to continue to observe an object or to move on, and to (b) decide whether the observed object is previously seen or novel. The system is able to successfully reject all novel objects as “unknown”, while still recognizing most of the previously seen objects.

sted, utgiver, år, opplag, sider
Springer, 2010. Vol. 2, nr 4, s. 316-325
Emneord [en]
Object recognition - Attention - Visual search - Fixation - Object permanence
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-63344DOI: 10.1007/s12559-010-9079-7ISI: 000292777400011OAI: oai:DiVA.org:liu-63344DiVA, id: diva2:378735
Merknad

The original publication is available at www.springerlink.com: Marcus Wallenberg and Per-Erik Forssén, Embodied Object Recognition using Adaptive Target Observations, 2010, Cognitive Computation, (2), 4, 316-325. http://dx.doi.org/10.1007/s12559-010-9079-7 Copyright: Springer Science Business Media http://www.springerlink.com/

Tilgjengelig fra: 2010-12-16 Laget: 2010-12-16 Sist oppdatert: 2017-12-11bibliografisk kontrollert
Inngår i avhandling
1. Components of Embodied Visual Object Recognition: Object Perception and Learning on a Robotic Platform
Åpne denne publikasjonen i ny fane eller vindu >>Components of Embodied Visual Object Recognition: Object Perception and Learning on a Robotic Platform
2013 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Object recognition is a skill we as humans often take for granted. Due to our formidable object learning, recognition and generalisation skills, it is sometimes hard to see the multitude of obstacles that need to be overcome in order to replicate this skill in an artificial system. Object recognition is also one of the classical areas of computer vision, and many ways of approaching the problem have been proposed. Recently, visually capable robots and autonomous vehicles have increased the focus on embodied recognition systems and active visual search. These applications demand that systems can learn and adapt to their surroundings, and arrive at decisions in a reasonable amount of time, while maintaining high object recognition performance. Active visual search also means that mechanisms for attention and gaze control are integral to the object recognition procedure. This thesis describes work done on the components necessary for creating an embodied recognition system, specifically in the areas of decision uncertainty estimation, object segmentation from multiple cues, adaptation of stereo vision to a specific platform and setting, and the implementation of the system itself. Contributions include the evaluation of methods and measures for predicting the potential uncertainty reduction that can be obtained from additional views of an object, allowing for adaptive target observations. Also, in order to separate a specific object from other parts of a scene, it is often necessary to combine multiple cues such as colour and depth in order to obtain satisfactory results. Therefore, a method for combining these using channel coding has been evaluated. Finally, in order to make use of three-dimensional spatial structure in recognition, a novel stereo vision algorithm extension along with a framework for automatic stereo tuning have also been investigated. All of these components have been tested and evaluated on a purpose-built embodied recognition platform known as Eddie the Embodied.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2013. s. 64
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1607
Emneord
computer vision, object recognition, stereo vision, classification
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-93812 (URN)978-91-7519-564-3 (ISBN)
Presentation
2013-08-16, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (engelsk)
Opponent
Veileder
Prosjekter
Embodied Visual Object Recognition
Forskningsfinansiär
Swedish Research Council
Tilgjengelig fra: 2013-07-09 Laget: 2013-06-10 Sist oppdatert: 2019-12-08bibliografisk kontrollert
2. Embodied Visual Object Recognition
Åpne denne publikasjonen i ny fane eller vindu >>Embodied Visual Object Recognition
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[sv]
Förkroppsligad objektigenkänning
Abstract [en]

Object recognition is a skill we as humans often take for granted. Due to our formidable object learning, recognition and generalisation skills, it is sometimes hard to see the multitude of obstacles that need to be overcome in order to replicate this skill in an artificial system. Object recognition is also one of the classical areas of computer vision, and many ways of approaching the problem have been proposed. Recently, visually capable robots and autonomous vehicles have increased the focus on embodied recognition systems and active visual search. These applications demand that systems can learn and adapt to their surroundings, and arrive at decisions in a reasonable amount of time, while maintaining high object recognition performance. This is especially challenging due to the high dimensionality of image data. In cases where end-to-end learning from pixels to output is needed, mechanisms designed to make inputs tractable are often necessary for less computationally capable embodied systems.Active visual search also means that mechanisms for attention and gaze control are integral to the object recognition procedure. Therefore, the way in which attention mechanisms should be introduced into feature extraction and estimation algorithms must be carefully considered when constructing a recognition system.This thesis describes work done on the components necessary for creating an embodied recognition system, specifically in the areas of decision uncertainty estimation, object segmentation from multiple cues, adaptation of stereo vision to a specific platform and setting, problem-specific feature selection, efficient estimator training and attentional modulation in convolutional neural networks. Contributions include the evaluation of methods and measures for predicting the potential uncertainty reduction that can be obtained from additional views of an object, allowing for adaptive target observations. Also, in order to separate a specific object from other parts of a scene, it is often necessary to combine multiple cues such as colour and depth in order to obtain satisfactory results. Therefore, a method for combining these using channel coding has been evaluated. In order to make use of three-dimensional spatial structure in recognition, a novel stereo vision algorithm extension along with a framework for automatic stereo tuning have also been investigated. Feature selection and efficient discriminant sampling for decision tree-based estimators have also been implemented. Finally, attentional multi-layer modulation of convolutional neural networks for recognition in cluttered scenes has been evaluated. Several of these components have been tested and evaluated on a purpose-built embodied recognition platform known as Eddie the Embodied.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2017. s. 89
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1811
Emneord
object recognition, machine learning, computer vision
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-132762 (URN)10.3384/diss.diva-132762 (DOI)9789176856260 (ISBN)
Disputas
2017-01-20, Visionen, B-huset, Campus VAlla, Linköping, 13:00 (engelsk)
Opponent
Veileder
Prosjekter
Embodied Visual Object RecognitionFaceTrack
Forskningsfinansiär
Swedish Research Council, 2008-4509Vinnova, 2013-00439EU, FP7, Seventh Framework Programme, 247947Linköpings universitet, LiU-foass
Tilgjengelig fra: 2016-12-06 Laget: 2016-11-23 Sist oppdatert: 2019-10-12bibliografisk kontrollert

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