Zernike moment-based feature detectors

Ghosal, S.; Mehrotra, R.

doi:10.1109/icip.1994.413246

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…If a corner is a bilevel wedge, then a response function based on local Zernike moments can be used to detect corners [70]. A more direct method for finding wedges is to find points where concentric contiguous arcs of pixels are significantly different from the center pixel [71].…”

Section: Direct Graylevel Detectorsmentioning

confidence: 99%

Faster and Better: A Machine Learning Approach to Corner Detection

Rosten

Porter

Drummond

2010

IEEE Trans. Pattern Anal. Mach. Intell.

1,581

812

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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.

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Section: Direct Graylevel Detectorsmentioning

confidence: 99%

Faster and Better: A Machine Learning Approach to Corner Detection

Rosten

Porter

Drummond

2010

IEEE Trans. Pattern Anal. Mach. Intell.

1,581

812

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“…A different model-based approach is proposed in [77]. For each type of feature, a parametric model is developed to characterize the local intensity in an image.…”

Section: Model-based Methodsmentioning

confidence: 99%

Local Invariant Feature Detectors: A Survey

Tuytelaars

Mikolajczyk

2007

FNT in Computer Graphics and Vision

1,107

573

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In this survey, we give an overview of invariant interest point detectors, how they evolved over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions.

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“…With these properties, Zernike moments have been widely used in a variety of fields: invariant pattern or object recognition [5,25,13,40,24,29], image reconstruction [36,31], image segmentation [12], edge detection [11], context-based image retrieval [27], face recognition [15], gait recognition [47] and biomedical imaging [41,22]. Additionally, higher-order Zernike moments have been used in several applications such as in image watermarking [26,6], moving object reconstruction [37], and peg-free hand shape verification [3].…”

Section: Introductionmentioning

confidence: 99%