The Experts below are selected from a list of 108117 Experts worldwide ranked by ideXlab platform
Xiaoyu Chen - One of the best experts on this subject based on the ideXlab platform.
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Retrieving Geometric Information from images: the case of hand-drawn diagrams
Data Mining and Knowledge Discovery, 2017Co-Authors: Dan Song, Dongming Wang, Xiaoyu ChenAbstract:This paper addresses the problem of retrieving meaningful Geometric Information implied in image data. We outline a general algorithmic scheme to solve the problem in any Geometric domain. The scheme, which depends on the domain, may lead to concrete algorithms when the domain is properly and formally specified. Taking plane Euclidean geometry E as an example of the domain, we show how to formally specify E and how to concretize the scheme to yield algorithms for the retrieval of meaningful Geometric Information in E. For images of hand-drawn diagrams in E, we present concrete algorithms to retrieve typical Geometric objects and Geometric relations, as well as their labels, and demonstrate the feasibility of our algorithms with experiments. An example is presented to illustrate how nontrivial Geometric theorems can be generated from retrieved Geometric objects and relations and thus how implied Geometric knowledge may be discovered automatically from images.
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Retrieving Geometric Information from images: the case of hand-drawn diagrams
Data Mining and Knowledge Discovery, 2017Co-Authors: Dan Song, Dongming Wang, Xiaoyu ChenAbstract:This paper addresses the problem of retrieving meaningful Geometric Information implied in image data. We outline a general algorithmic scheme to solve the problem in any Geometric domain. The scheme, which depends on the domain, may lead to concrete algorithms when the domain is properly and formally specified. Taking plane Euclidean geometry $${\mathbb {E}}$$E as an example of the domain, we show how to formally specify $${\mathbb {E}}$$E and how to concretize the scheme to yield algorithms for the retrieval of meaningful Geometric Information in $${\mathbb {E}}$$E. For images of hand-drawn diagrams in $${\mathbb {E}}$$E, we present concrete algorithms to retrieve typical Geometric objects and Geometric relations, as well as their labels, and demonstrate the feasibility of our algorithms with experiments. An example is presented to illustrate how nontrivial Geometric theorems can be generated from retrieved Geometric objects and relations and thus how implied Geometric knowledge may be discovered automatically from images.
Giorgio Vallortigara - One of the best experts on this subject based on the ideXlab platform.
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doing socrates experiment right controlled rearing studies of Geometrical knowledge in animals
Current Opinion in Neurobiology, 2009Co-Authors: Giorgio Vallortigara, Valeria Anna Sovrano, Cinzia ChiandettiAbstract:The issue of whether encoding of Geometric Information for navigational purposes crucially depends on environmental experience or whether it is innately predisposed in the brain has been recently addressed in controlled rearing studies. Non-human animals can make use of the Geometric shape of an environment for spatial reorientation and in some circumstances reliance on purely Geometric Information (metric properties and sense) can overcome use of local featural Information. Animals reared in home cages of different Geometric shapes proved to be equally capable of learning and performing navigational tasks based on Geometric Information. The findings suggest that effective use of Geometric Information for spatial reorientation does not require experience in environments with right angles and metrically distinct surfaces.
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Spatial reorientation in large and small enclosures: comparative and developmental perspectives
Cognitive Processing, 2008Co-Authors: Cinzia Chiandetti, Giorgio VallortigaraAbstract:Several vertebrate species, including humans, following passive spatial disorientation appear to be able to reorient themselves by making use of the Geometric shape of the environment (i.e., metric properties of surfaces and directional sense). In some circumstances, reliance on such purely Geometric Information can overcome the use of local featural cues (landmarks). The relative use of Geometric and non-Geometric Information seems to depend upon, among other factors, the size of the experimental space. Evidence in non-human animals and in human infants for primacy in encoding either Geometric or landmark Information depending on the size of the environment is reviewed, together with possible theoretical accounts of this phenomenon.
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reorientation by Geometric and landmark Information in environments of different size
Developmental Science, 2005Co-Authors: Giorgio Vallortigara, Marco Feruglio, Valeria Anna SovranoAbstract:It has been found that disoriented children could use Geometric Information in combination with landmark Information to reorient themselves in large but not in small experimental spaces. We tested domestic chicks in the same task and found that they were able to conjoin Geometric and nonGeometric (landmark) Information to reorient themselves in both the large and the small space used. Moreover, chicks reoriented immediately when displaced from a large to a small experimental space and vice versa, suggesting that they used the relative metrics of the environment. However, when tested with a transformation (affine transformation) that alters the Geometric relations between the target and the shape of the environment, chicks tended to make more errors based on Geometric Information when tested in the small than in the large space. These findings suggest that the reliance of the use of Geometric Information on the spatial scale of the environment is not restricted to the human species.
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modularity as a fish xenotoca eiseni views it conjoining Geometric and nonGeometric Information for spatial reorientation
Journal of Experimental Psychology: Animal Behavior Processes, 2003Co-Authors: Valeria Anna Sovrano, Angelo Bisazza, Giorgio VallortigaraAbstract:When disoriented in a closed rectangular tank, fish (Xenotoca eiseni) reoriented in accord with the large-scale shape of the environment, but they were also able to conjoin Geometric Information with nonGeometric properties such as the color of a wall or the features provided by panels located at the corners of the tank. Fish encoded Geometric Information even when featural Information sufficed to solve the spatial task. When tested after transformations that altered the original arrangement of the panels, fish were more affected by those transformations that modified the Geometric relationship between the target and the shape of the environment. Finally, fish appeared unable to use nonGeometric Information provided by distant panels. These findings show that a reorientation mechanism based on geometry is widespread among vertebrates, though the joint use of Geometric and nonGeometric cues by fish suggest that the degree of Information encapsulation of the mechanism varies considerably between species.
Dan Song - One of the best experts on this subject based on the ideXlab platform.
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Retrieving Geometric Information from images: the case of hand-drawn diagrams
Data Mining and Knowledge Discovery, 2017Co-Authors: Dan Song, Dongming Wang, Xiaoyu ChenAbstract:This paper addresses the problem of retrieving meaningful Geometric Information implied in image data. We outline a general algorithmic scheme to solve the problem in any Geometric domain. The scheme, which depends on the domain, may lead to concrete algorithms when the domain is properly and formally specified. Taking plane Euclidean geometry E as an example of the domain, we show how to formally specify E and how to concretize the scheme to yield algorithms for the retrieval of meaningful Geometric Information in E. For images of hand-drawn diagrams in E, we present concrete algorithms to retrieve typical Geometric objects and Geometric relations, as well as their labels, and demonstrate the feasibility of our algorithms with experiments. An example is presented to illustrate how nontrivial Geometric theorems can be generated from retrieved Geometric objects and relations and thus how implied Geometric knowledge may be discovered automatically from images.
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Retrieving Geometric Information from images: the case of hand-drawn diagrams
Data Mining and Knowledge Discovery, 2017Co-Authors: Dan Song, Dongming Wang, Xiaoyu ChenAbstract:This paper addresses the problem of retrieving meaningful Geometric Information implied in image data. We outline a general algorithmic scheme to solve the problem in any Geometric domain. The scheme, which depends on the domain, may lead to concrete algorithms when the domain is properly and formally specified. Taking plane Euclidean geometry $${\mathbb {E}}$$E as an example of the domain, we show how to formally specify $${\mathbb {E}}$$E and how to concretize the scheme to yield algorithms for the retrieval of meaningful Geometric Information in $${\mathbb {E}}$$E. For images of hand-drawn diagrams in $${\mathbb {E}}$$E, we present concrete algorithms to retrieve typical Geometric objects and Geometric relations, as well as their labels, and demonstrate the feasibility of our algorithms with experiments. An example is presented to illustrate how nontrivial Geometric theorems can be generated from retrieved Geometric objects and relations and thus how implied Geometric knowledge may be discovered automatically from images.
Federico Thomas - One of the best experts on this subject based on the ideXlab platform.
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ICRA - Set membership approach to the propagation of uncertain Geometric Information
Proceedings. 1991 IEEE International Conference on Robotics and Automation, 1Co-Authors: Assumpta Sabater, Federico ThomasAbstract:An alternative approach for the propagation of uncertain Geometric Information, based on the ideas presented by J.R. Deller (IEEE ASSP Magazine, vol.6, p.4-20, Oct. 1989) and extended to deal with graphs of Geometric constraints, is presented. This method avoids the independency assumption of the probabilistic approach. In this approach, when new sensory data are acquired, a set of strips is obtained, propagated, and fused to obtain the updated ellipsoids associated with each feature, Then, the hypothesis about the location of the involved Geometric features can be easily updated. Inconsistencies are easily detected, resulting in fast rejection of erroneous data. >
K. Kanatani - One of the best experts on this subject based on the ideXlab platform.
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IROS - Statistical inference by stereo vision: Geometric Information criterion
Proceedings of IEEE RSJ International Conference on Intelligent Robots and Systems. IROS '96, 1Co-Authors: Y. Kanazawa, K. KanataniAbstract:Introducing a mathematical model of noise in stereo images, we define the Geometric Information criterion (Geometric AIC) for evaluating the goodness of an assumption about the object we are viewing. We show that we can test whether or not the object is located infinitely far away or the object is a planar surface without using any knowledge about the noise magnitude or any empirically adjustable thresholds. Synthetic and real-image examples are shown to illustrate our theory.
