Results 1 - 10 of 2,771

Object Recognition from Local Scale-Invariant Features

by David G. Lowe
"... An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in ..."
Abstract - Cited by 2739 (13 self) - Add to MetaCart
An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons

Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks

by Stephen Se, David Lowe, Jim Little , 2002
"... A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robo ..."
Abstract - Cited by 279 (12 self) - Add to MetaCart
robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops

Indexing based on scale invariant interest points

by Krystian Mikolajczyk, Cordelia Schmid - 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 ..."
Abstract - Cited by 409 (32 self) - Add to MetaCart
descriptor. Our descriptors are, in addition, invariant to image rotation, to affine illumination changes and robust to small perspective deformations. Experimental results for indexing show an excellent performance up to a scale factor of 4 for a database with more than 5000 images. 1

Selection of scale-invariant parts for object class recognition

by Gy. Dorkó, C. Schmid - In ICCV , 2003
"... This paper introduces a novel method for constructing and selecting scale-invariant object parts. Scale-invariant local descriptors are first grouped into basic parts. A classifier is then learned for each of these parts, and feature selection is used to determine the most discriminative ones. This ..."
Abstract - Cited by 158 (16 self) - Add to MetaCart
This paper introduces a novel method for constructing and selecting scale-invariant object parts. Scale-invariant local descriptors are first grouped into basic parts. A classifier is then learned for each of these parts, and feature selection is used to determine the most discriminative ones

An affine invariant interest point detector

by Krystian Mikolajczyk, Cordelia Schmid - In Proceedings of the 7th European Conference on Computer Vision , 2002
"... Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of ..."
Abstract - Cited by 1467 (55 self) - Add to MetaCart
Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape

SURF: Speeded Up Robust Features

by Herbert Bay, Tinne Tuytelaars, Luc Van Gool - ECCV
"... Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be comp ..."
Abstract - Cited by 897 (12 self) - Add to MetaCart
Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can

SCALE-INVARIANT GROUPS

by Volodymyr Nekrashevych, Gábor Pete , 2009
"... Motivated by the renormalization method in statistical physics, Itai Benjamini defined a finitely generated infinite group G to be scale-invariant if there is a nested sequence of finite index subgroups Gn that are all isomorphic to G and whose intersection is a finite group. He conjectured that eve ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
that every scale-invariant group has polynomial growth, hence is virtually nilpotent. We disprove his conjecture by showing that the following groups (mostly finite-state self-similar groups) are scale-invariant: the lamplighter groups F ≀ Z, where F is any finite Abelian group; the solvable Baumslag

Scale-Invariant Features on the Sphere

by Peter Hansen, Peter Corke, Wageeh Boles, Kostas Daniilidis
"... This paper considers an application of scale-invariant feature detection using scale-space analysis suitable for use with wide field of view cameras. Rather than obtain scalespace images via convolution with the Gaussian function on the image plane, we map the image to the sphere and obtain scale-sp ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
This paper considers an application of scale-invariant feature detection using scale-space analysis suitable for use with wide field of view cameras. Rather than obtain scalespace images via convolution with the Gaussian function on the image plane, we map the image to the sphere and obtain scale

Gool, L.: An efficient dense and scale-invariant spatiotemporal interest point detector

by Geert Willems, Tinne Tuytelaars, Luc Van Gool , 2008
"... Abstract. Over the years, several spatio-temporal interest point detectors have been proposed. While some detectors can only extract a sparse set of scale-invariant features, others allow for the detection of a larger amount of features at user-defined scales. This paper presents for the first time ..."
Abstract - Cited by 168 (3 self) - Add to MetaCart
spatio-temporal interest points that are at the same time scale-invariant (both spatially and temporally) and densely cover the video content. Moreover, as opposed to earlier work, the fea-tures can be computed efficiently. Applying scale-space theory, we show that this can be achieved by using

Vision-based Mobile Robot Localization And Mapping using Scale-Invariant Features

by Stephen Se, David Lowe, Jim Little - In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA , 2001
"... A key component of a mobile robot system is the ability to localize itself accurately and build a map of the environment simultaneously. In this paper, a vision-based mobile robot localization and mapping algorithm is described which uses scale-invariant image features as landmarks in unmodi ed dyna ..."
Abstract - Cited by 167 (10 self) - Add to MetaCart
A key component of a mobile robot system is the ability to localize itself accurately and build a map of the environment simultaneously. In this paper, a vision-based mobile robot localization and mapping algorithm is described which uses scale-invariant image features as landmarks in unmodi ed
Results 1 - 10 of 2,771