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Author

Gareth Loy

Other affiliations: Australian National University
Bio: Gareth Loy is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Regular polygon & Symmetry (geometry). The author has an hindex of 11, co-authored 12 publications receiving 920 citations. Previous affiliations of Gareth Loy include Australian National University.

Papers
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Book ChapterDOI
07 May 2006
TL;DR: It is shown how symmetric pairs of features can be efficiently detected, how the symmetry bonding each pair is extracted and evaluated, and how these can be grouped into symmetric constellations that specify the dominant symmetries present in the image.
Abstract: A novel and efficient method is presented for grouping feature points on the basis of their underlying symmetry and characterising the symmetries present in an image. We show how symmetric pairs of features can be efficiently detected, how the symmetry bonding each pair is extracted and evaluated, and how these can be grouped into symmetric constellations that specify the dominant symmetries present in the image. Symmetries over all orientations and radii are considered simultaneously, and the method is able to detect local or global symmetries, locate symmetric figures in complex backgrounds, detect bilateral or rotational symmetry, and detect multiple incidences of symmetry.

387 citations

Journal ArticleDOI
TL;DR: This paper presents a complete system that reads speed signs in real-time, compares the driver's gaze, and provides immediate feedback if it appears the sign has been missed by the driver.

120 citations

Book ChapterDOI
28 May 2002
TL;DR: A new feature detection technique is presented that utilises local radial symmetry to identify regions of interest within a scene that is significantly faster than existing techniques using radial symmetry and offers the possibility of real-time implementation on a standard processor.
Abstract: A new feature detection technique is presented that utilises local radial symmetry to identify regions of interest within a scene. This transform is significantly faster than existing techniques using radial symmetry and offers the possibility of real-time implementation on a standard processor. The new transformis shown to perform well on a wide variety of images and its performance is tested against leading techniques from the literature. Both as a facial feature detector and as a generic region of interest detector the new transformis seen to offer equal or superior performance to contemporary techniques whilst requiring drastically less computational effort.

110 citations

Proceedings ArticleDOI
20 May 2002
TL;DR: In this article, a vision system is demonstrated that adaptively allocates computational resources over multiple visual cues to robustly track a target in 3D. The system uses a particle filter to maintain multiple hypotheses of the target location.
Abstract: A vision system is demonstrated that adaptively allocates computational resources over multiple cues to robustly track a target in 3D. The system uses a particle filter to maintain multiple hypotheses of the target location. Bayesian probability theory provides the framework for sensor fusion, and resource scheduling is used to intelligently allocate the limited computational resources available across the suite of cues. The system is shown to track a person in 3D space moving in a cluttered environment.

77 citations

Proceedings Article
01 Jan 2002
TL;DR: A vision system is demonstrated that adaptively allocates computational resources over multiple cues to robustly track a target in 3D using a particle filter to maintain multiple hypotheses of the target location.
Abstract: A vision system is demonstrated that adaptively allocates computational resources over multiple cues to robustly track a target in 3D. The system uses a particle filter to maintain multiple hypotheses of the target location. Bayesian probability theory provides the framework for sensor fusion, and resource scheduling is used to intelligently allocate the limited computational resources available across the suite of cues. The system is shown to track a person in 3D space moving in a cluttered environment.

75 citations


Cited by
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Book ChapterDOI
07 May 2006
TL;DR: It is shown that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time.
Abstract: Where feature points are used in real-time frame-rate applications, a high-speed feature detector is necessary. Feature detectors such as SIFT (DoG), Harris and SUSAN are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Here we show that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time. By comparison neither the Harris detector (120%) nor the detection stage of SIFT (300%) can operate at full frame rate. Clearly a high-speed detector is of limited use if the features produced are unsuitable for downstream processing. In particular, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations [1]. Hence the second contribution of this paper is a comparison corner detectors based on this criterion applied to 3D scenes. This comparison supports a number of claims made elsewhere concerning existing corner detectors. Further, contrary to our initial expectations, we show that despite being principally constructed for speed, our detector significantly outperforms existing feature detectors according to this criterion.

3,828 citations

Journal Article
TL;DR: In this paper, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations, and a comparison of corner detectors based on this criterion applied to 3D scenes is made.
Abstract: Where feature points are used in real-time frame-rate applications, a high-speed feature detector is necessary. Feature detectors such as SIFT (DoG), Harris and SUSAN are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Here we show that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time. By comparison neither the Harris detector (120%) nor the detection stage of SIFT (300%) can operate at full frame rate. Clearly a high-speed detector is of limited use if the features produced are unsuitable for downstream processing. In particular, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations[1]. Hence the second contribution of this paper is a comparison corner detectors based on this criterion applied to 3D scenes. This comparison supports a number of claims made elsewhere concerning existing corner detectors. Further, contrary to our initial expectations, we show that despite being principally constructed for speed, our detector significantly outperforms existing feature detectors according to this criterion. © Springer-Verlag Berlin Heidelberg 2006.

3,413 citations

Journal ArticleDOI
TL;DR: This survey reviews recent trends in video-based human capture and analysis, as well as discussing open problems for future research to achieve automatic visual analysis of human movement.

2,738 citations

Journal ArticleDOI
TL;DR: A new heuristic for feature detection is presented and, using machine learning, a feature detector is derived from this which can fully process live PAL video using less than 5 percent of the available processing time.
Abstract: The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

1,847 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the data fusion state of the art is proposed, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies.

1,684 citations