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Proceedings ArticleDOI

A Combined Corner and Edge Detector

01 Jan 1988-pp 147-151

TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Abstract: The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.
Citations
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Journal ArticleDOI
David G. Lowe1Institutions (1)
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

42,225 citations


01 Jan 2011-
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Abstract: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.

14,701 citations


01 Jan 2001-
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Abstract: Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

13,284 citations


Book ChapterDOI
Herbert Bay1, Tinne Tuytelaars2, Luc Van Gool1Institutions (2)
07 May 2006-
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.

12,404 citations


Journal ArticleDOI
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision.

11,276 citations


Cites methods from "A Combined Corner and Edge Detector..."

  • ...The most widely used detector is probably the Harris corner detector [15], proposed back in 1988....

    [...]


References
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Book
01 Jan 1980-
Abstract: : The Stanford AI Lab cart is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter A computer has been programmed to drive the cart through cluttered indoor and outdoor spaces, gaining its knowledge of the world entirely from images broadcast by the onboard TV system The cart uses several kinds of stereo to locate objects around it in 3D and to deduce its own motion It plans an obstacle avoiding path to a desired destination on the basis of a model built with this information The plan changes as the cart perceives new obstacles on its journey The system is reliable for short runs, but slow The cart moves one meter every ten to fifteen minutes, in lurches After rolling a meter it stops, takes some pictures and thinks about them for a long time Then it plans a new path, executes a little of it, and pauses again The program has successfully driven the cart through several 20 meter indoor courses (each taking about five hours) complex enough to necessitate three or four avoiding swerves A less successful outdoor run, in which the cart skirted two obstacles but collided with a third, was also done Harsh lighting (very bright surfaces next to very dark shadows) giving poor pictures and movement of shadows during the cart's creeping progress were major reasons for the poorer outdoor performance The action portions of these runs were filmed by computer controlled cameras (Author)

1,032 citations


01 Jun 1983-
TL;DR: This thesis is an attempt to formulate a set of edge detection criteria that capture as directly as possible the desirable properties of an edge operator.
Abstract: : The problem of detecting intensity changes in images is canonical in vision. Edge detection operators are typically designed to optimally estimate first or second derivative over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been been argued for. This thesis is an attempt to formulate a set of edge detection criteria that capture as directly as possible the desirable properties of an edge operator. Variational techniques are used to find a solution over the space of all linear shift invariant operators. The first criterion is that the detector have low probability of error i.e. failing to mark edges or falsely marking non-edges. The second is that the marked points should b The third criterion is that there should be low probability of more than one response to a single edge. The technique is used to find optimal operators for step edges and for extended impulse profiles (ridges or valleys in two dimensions). The extension of the one dimensional operators to two dimensions is then discussed. The result is a set of operators of varying width, length and orientation. The problem of combining these outputs into a single description is discussed, and a set of heuristics for the integration are given. (Author)

972 citations


Journal ArticleDOI
Les Kitchen1, Azriel Rosenfeld1Institutions (1)
TL;DR: Several techniques are presented for measuring 'cornerity' values in gray-level images, without prior segmentation, so that corners can be detected by thresholding these values.
Abstract: The usual approach to detecting corners in shapes involves first segmenting the shape, then locating the corners in its boundary. We present several techniques for measuring 'cornerity' values in gray-level images, without prior segmentation, so that corners can be detected by thresholding these values.

656 citations


01 Jan 1978-

374 citations


Journal ArticleDOI
TL;DR: An explicit three-dimensional representation is constructed from feature points extracted from a sequence of images taken by a moving camera, and their 3D locations are accurately determined by use of Kalman filters.
Abstract: An explicit three-dimensional (3D) representation is constructed from feature points extracted from a sequence of images taken by a moving camera. The points are tracked through the sequence, and their 3D locations are accurately determined by use of Kalman filters. The egomotion of the camera is also determined.

174 citations


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No. of citations received by the Paper in previous years
YearCitations
202212
2021396
2020536
2019641
2018689
2017822