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Feature detection with automatic scale selection
 International Journal of Computer Vision
, 1998
"... The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works ..."
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Cited by 723 (34 self)
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works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a socalled scalespace representation. Traditional scalespace theory building on this work, however, does not address the problem of how to select local appropriate
The Laplacian Pyramid as a Compact Image Code
, 1983
"... We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixeltopixel correlations a ..."
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Cited by 1388 (12 self)
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We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixeltopixel correlations
Laplacian eigenmaps and spectral techniques for embedding and clustering.
 Proceeding of Neural Information Processing Systems,
, 2001
"... Abstract Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in ..."
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Cited by 668 (7 self)
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retrieval and data mining, one is often confronted with intrinsically low dimensional data lying in a very high dimensional space. For example, gray scale n x n images of a fixed object taken with a moving camera yield data points in rn: n2 . However , the intrinsic dimensionality of the space of all images
Features of similarity.
 Psychological Review
, 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
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Cited by 1455 (2 self)
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. These models represent objects as points in some coordinate space such that the observed dissimilarities between objects correspond to the metric distances between the respective points. Practically all analyses of proximity data have been metric in nature, although some (e.g., hierarchical clustering) yield
ScaleSpace Generators and Functionals
"... In this chapter we will try to relate axiomatic formulations of scalespaces with regularization theory. Most axiomatic formulations do not directly require regularity properties of scalespace. Nevertheless, an analytic filter is singled out so that scalespace appears to have strong regularity pr ..."
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Cited by 2 (0 self)
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of axioms regarding a scalespace representation of an image can narrow down the set of admissible scalespace operators. We call an operator G a scalespace generator if it is parametrised by a scale parameter t so that
Layered depth images
, 1997
"... In this paper we present an efficient image based rendering system that renders multiple frames per second on a PC. Our method performs warping from an intermediate representation called a layered depth image (LDI). An LDI is a view of the scene from a single input camera view, but with multiple pix ..."
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Cited by 456 (29 self)
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In this paper we present an efficient image based rendering system that renders multiple frames per second on a PC. Our method performs warping from an intermediate representation called a layered depth image (LDI). An LDI is a view of the scene from a single input camera view, but with multiple
ScaleSpace for Discrete Signals
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1990
"... We address the formulation of a scalespace theory for discrete signals. In one dimension it is possible to characterize the smoothing transformations completely and an exhaustive treatment is given, answering the following two main questions: 1. Which linear transformations remove structure in the ..."
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Cited by 134 (25 self)
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We address the formulation of a scalespace theory for discrete signals. In one dimension it is possible to characterize the smoothing transformations completely and an exhaustive treatment is given, answering the following two main questions: 1. Which linear transformations remove structure
Indexing based on scale invariant interest points
 In Proceedings of the 8th International Conference on Computer Vision
, 2001
"... This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: 1) Interest points can be adapted to scale and give repeatable results (geometrically stable). 2) Local extrema over scale of normalized derivatives indicate the ..."
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Cited by 409 (32 self)
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This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: 1) Interest points can be adapted to scale and give repeatable results (geometrically stable). 2) Local extrema over scale of normalized derivatives indicate
Scalespace
, 2008
"... Scalespace theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of realworld objects, which implies that objects may be perce ..."
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Cited by 1 (0 self)
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Scalespace theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of realworld objects, which implies that objects may
Scalespace theory: A basic tool for analysing structures at different scales
 Journal of Applied Statistics
, 1994
"... An inherent property of objects in the world is that they only exist as meaningful entities over certain ranges of scale. If one aims at describing the structure of unknown realworld signals, then a multiscale representation of data is of crucial importance. This chapter gives a tutorial review of ..."
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Cited by 166 (11 self)
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of a special type of multiscale representation, linear scalespace representation, which has been developed by thecomputer vision community in order to handle image structures at di erent scales in a consistent manner. The basic idea is to embed the original signal into a oneparameter family
Results 1  10
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