Experiments in place recognition using gist panoramas

Murillo, Ana C.; Košecká, Jana

doi:10.1109/iccvw.2009.5457552

Cited by 69 publications

(44 citation statements)

References 18 publications

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“…They also used GIST descriptors from omnidirectional images for appearance-based topological mapping and localization [23]. Murillo et al also proposed a method for estimating the position of the moving camera using the GIST descriptor by comparing multiple cylindrical panoramic images [24,25]. However, the method used four perspective views obtained from a panoramic image instead of directly using a spherical panoramic image.…”

Section: Summary Of Previous Methodsmentioning

confidence: 99%

Estimating deformation factors of planar patterns in spherical panoramic images

Kim

Park

2016

Multimedia Systems

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Section: Summary Of Previous Methodsmentioning

confidence: 99%

Estimating deformation factors of planar patterns in spherical panoramic images

Kim

Park

2016

Multimedia Systems

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“…Our initial location recognition and loop-closure detection experiments using the proposed representation were shown in [28] and [29]. In this study, we extend them and incorporate an online temporal model for loop-closure detection which computes the probability of loop closure in a Bayesian filtering framework.…”

Section: A Image Representationmentioning

confidence: 99%

Localization in Urban Environments Using a Panoramic Gist Descriptor

et al. 2013

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Abstract-Vision-based topological localization and mapping for autonomous robotic systems have received increased research interest in recent years. The need to map larger environments requires models at different levels of abstraction and additional abilities to deal with large amounts of data efficiently. Most successful approaches for appearance-based localization and mapping with large datasets typically represent locations using local image features. We study the feasibility of performing these tasks in urban environments using global descriptors instead and taking advantage of the increasingly common panoramic datasets. This paper describes how to represent a panorama using the global gist descriptor, while maintaining desirable invariance properties for location recognition and loop detection. We propose different gist similarity measures and algorithms for appearance-based localization and an online loop-closure detection method, where the probability of loop closure is determined in a Bayesian filtering framework using the proposed image representation. The extensive experimental validation in this paper shows that their performance in urban environments is comparable with local-feature-based approaches when using wide field-of-view images.

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“…They are easily computed using spectral and superficial localized information. In addition, the use of GIST in scene recognition has been widely experimented [12]- [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic scene recognition for low-resource devices using evolving classifiers

Andréu

Baruah

Angelov

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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In this paper an original approach is proposed which makes possible autonomous scenes recognition performed on-line by an evolving self-learning classifier. Existing approaches for scene recognition are off-line and used in intelligent albums for picture categorization/selection. The emergence of powerful mobile platforms with camera on board and sensor-based autonomous (robotic) systems is pushing forward the requirement for efficient self-learning and adaptive/evolving algorithms. Fast real-time and online algorithms for categorisation of the real world environment based on live video stream are essential for understanding and situation awareness as well as for localization and context awareness. In scene analysis the critical problem is feature extraction mechanism for a quick description of the scene. In this paper we apply a well known technique called spatial envelop or GIST. Visual scenes can be quite different but very often they can be grouped in similar types/categories. For example, pictures from different cities across the Globe, e.g. Tokyo, Vancouver, New York, Moscow, Dusseldorf, etc. bear the similar pattern of an urban scene -high rise buildings, despite the differences in the architectural style. Same applies for the beaches of Miami, Maldives, Varna, Costa del Sol, etc. One assumption based on which such automatic video classifiers can be build is to pre-train them using a large number of such images from different groups. Variety of possible scenes suggests the limitations of such an approach. Therefore, we use in this paper the recently propose evolving fuzzy rule-based classifier, simpl_eClass, which is selflearning and thus updates its rules and categories descriptions with each new image. In addition, it is fully recursive, computationally efficient and yet linguistically transparent.

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Experiments in place recognition using gist panoramas

Cited by 69 publications

References 18 publications

Estimating deformation factors of planar patterns in spherical panoramic images

Estimating deformation factors of planar patterns in spherical panoramic images

Localization in Urban Environments Using a Panoramic Gist Descriptor

Automatic scene recognition for low-resource devices using evolving classifiers

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