We present a multi-scale representation of grey-level shape, called the scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. Th...
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We present a multi-scale representation of grey-level shape, called the scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. The representation gives a qualitative description of the image structure that allows for extraction of significant image structure - stable scales and regions of interest - in a solely bottom-up data-driven manner. Hence, it can be seen as preceding further processing, which can then be properly tuned. Experiments on real imagery demonstrate that the proposed theory gives intuitively reasonable results.
Some versions of the maximum common subgraph problem are studied and approximation algorithms are given. The maximum bounded common induced subgraph problem is shown to be Max SNP-hard and the maximum unbounded common...
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Elementary techniques from real analysis, and singularity theory are applied to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treatment com...
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Elementary techniques from real analysis, and singularity theory are applied to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treatment comprise
We present an algorithm to compute the Hilbert series of a homogeneous ideal in a polynomial ring. From an ideal I, generated by the principal monomials of a Gröbner basis, we compute the Hilbert series \(Hilbk[x...
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We consider how junction detection and classification can be performed in an active visual system. This is to exemplify that feature detection and classification in general can be done by both simple and robust method...
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Projective distortion of surface texture observed in a perspective image can provide direct information about the shape of the underlying surface. Previous theories have generally concerned planar surfaces;this paper ...
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Projective distortion of surface texture observed in a perspective image can provide direct information about the shape of the underlying surface. Previous theories have generally concerned planar surfaces;in this pap...
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In this paper we study small depth circuits that contain threshold gates (with or without weights) and parity gates. All circuits we consider are of polynomial size. We prove several results which complete the work on...
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A highly simplified network model of cortical associative memory, based on Hebb's theory of cell assemblies, has been developed and simulated. The network comprises realistically modelled pyramidal-type cells and ...
A highly simplified network model of cortical associative memory, based on Hebb's theory of cell assemblies, has been developed and simulated. The network comprises realistically modelled pyramidal-type cells and inhibitory fast-spiking interneurons and its connectivity is adopted from a trained recurrent artificial neural network. After-activity, pattern completion and competition between cell assemblies is readily produced. If, instead of pyramidal cells, motor neuron type cells are used, network behaviour changes drastically. For instance, spike synchronization can be observed but after-activity is hard to produce. The authors results support the biological feasibility of Hebb's cell assembly theory. The analogy between this theory and recurrent artificial neural network models is discussed.
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