Wavelet based corner detection

Lee, Jiann-Shu; Sun, Yung-Nien; Chen, Chin-Hsing; Tsai, Chih‐Heng

doi:10.1016/0031-3203(93)90051-w

Cited by 37 publications

(16 citation statements)

References 18 publications

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“…Wavelet transformation was also investigated in the context of feature point extraction with successful results based on multi-resolution analysis in [35,111,218].…”

Section: Intensity Variationsmentioning

confidence: 99%

Local Invariant Feature Detectors: A Survey

Tuytelaars

Mikolajczyk

2007

FNT in Computer Graphics and Vision

1,146

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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“…Wavelet transformation was also investigated in the context of feature point extraction with successful results based on multi-resolution analysis in [35,111,218].…”

Section: Intensity Variationsmentioning

confidence: 99%

Local Invariant Feature Detectors: A Survey

Tuytelaars

Mikolajczyk

2007

FNT in Computer Graphics and Vision

1,146

573

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“…In the past decade, one-dimensional wavelet transforms (1D WT) [22,23,24,25] became popular for localised frequency analysis, feature extraction, and corner detection [26,27,28,29,30] because they are able to multi-decompose an input signal f(t) and capture the local abnormalities at lower detail levels. As Fig.…”

Section: Introductionmentioning

confidence: 99%

A case study: passive component inspection using a 1D wavelet transform

Yeh

Shen

2003

The International Journal of Advanced Manufacturing Technology

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This paper exploits a wavelet-based scheme to inspect the surface defects and basic dimensions of 0805 Multi-layer Ceramic Chip capacitors (MLCC) using machine vision. The image of a passive component is initially processed to show only two solder plates (terminations). Then, the covariance matrix eigenvector for each boundary point generates the 1D h-p representation to describe the angle variations at the boundaries of each termination. The 1D h-p representation is further decomposed directly by a one-dimensional wavelet transform (1D WT). Since a single corner (an intersection of two boundary edges) and the jag corners (the surface defects) are local deviations on termination boundaries, their locations are represented as intensive and have a highly fluctuated wavelet energy at the 1st detail scale. Concerning a 0805 MLCC type of passive component, the surface defects and single corners on termination boundaries can be captured by appropriate thresholds (e.g., in wavelet energy). The basic dimensions of a passive component are simply the direct distance between located single corners. Real 0805 MLCCs are testing samples to evaluate the performance of the proposed approach. Experimental results show that the proposed method achieves a precise identification of the surface defects and measurements for basic dimensions. The proposed approach is invariant with respect to the orientation, easy to implement and free from a primitive-matching process. Therefore, it is especially suitable for various types of passive components that are similar to the 0805 MLCC in small-batch production.

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“…The reconstruction of the edges is then carried out by simple addition of all the scales (10) where is the set of edges at the "finest" scale and is the set of edges at the "coarsest" scale.…”

Section: Starck-murtagh-bijaoui Wavelet Transformmentioning

confidence: 99%

“…Wavelets and multiresolution appear in the literature on image processing for a variety of applications, such as singularity detection [7], image coding using multiscale edges [8], [9], and feature detection [10]- [12], [15].…”

Section: A Motivation For Using Waveletsmentioning

confidence: 99%