Invariant 2D object recognition using eigenvalues of covariance matrices, re-sampling and autocorrelation

Sun, Te-Hsiu; Liu, Chi-Shuan; Tien, Fang-Chih

doi:10.1016/j.eswa.2007.08.106

Cited by 15 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the obtained data are in the time domain, it is reasonable to analyse signal patterns and time shifts using an autocorrelation function. An autocorrelation function helps to examine whether the time series data are The autocorrelation coefficient used is defined as (Sun et al, 2008):…”

Section: Autocorrelation Analysismentioning

confidence: 99%

SAG mill system diagnosis using multivariate process variable analysis

Ko¹,

Shang²

2011

Can J Chem Eng

View full text Add to dashboard Cite

Semi-autogenous grinding (SAG) of ore plays a critical role in a mineral processing plant. In SAG operations, abnormal conditions, such as overload or insufficient ore holdup, often result in inefficient production and unstable operation. It is, therefore, essential to monitor the process using effective technology so that abnormal or faulty conditions can be detected and addressed in a timely manner. In this study, investigation is focused on applying multivariate analysis in the monitoring and diagnosing of an industrial SAG operation. The results show that principal component analysis provides an effective methodology for on-line monitoring and diagnosis. The detection and removal of faulty conditions will help to provide stable and cost-efficient operation.Le broyage semi-autogène (BSA) du minerai joue un rôle essentiel dans une usine de traitement des minéraux. Dans les activités de BSA, des conditions anormales, comme une surcharge ou une retenue du minerai insuffisante, mènent souventà une production inefficace et un fonctionnement instable. Il est, par conséquent, essentiel de surveiller le processusà l'aide d'une technologie efficace pour que les conditions anormales soient détectées et abordées de manière opportune. Dans ce document, l'enquête est axée sur l'utilisation d'une analyse multivariée dans le suivi et le diagnostic d'une activité de BSA industrielle. Les résultats indiquent que l'analyse des principaux composants fournit une méthodologie efficace pour le suivi et le diagnostic en ligne. La détection et le retrait de ses conditions anormales aiderontà procurer un fonctionnement stable et economique.

show abstract

Section: Autocorrelation Analysismentioning

confidence: 99%

SAG mill system diagnosis using multivariate process variable analysis

Ko¹,

Shang²

2011

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

“…Let ϕ(k, p i ) denote the included angle of the region of support S k (p i ). It can be estimated by the arccosine of the two arm vectors of S k (p i ) [23], [28], i.e.…”

Section: Estimation Of Included Anglesmentioning

confidence: 99%

“…Since then, many studies have applied the small eigenvalues to detect corners directly [15], [16], [18], [21]. In addition, some others studies were also inspired by Tsai et al's small eigenvalues approach [6], [7], [13], [14], [17], [19], [22], [23]. Although Sossa Azuela et al [20] commented that Tsai et al's method was the best one among the methods they had tested, Guru et al [8] later discovered that Tsai et al's method may also detect unwanted spurious corners.…”

Section: Introductionmentioning

confidence: 99%

“…Others use morphological operators to extract corners [5]. Boundary-based corner detectors, segmenting objects from an image first and then locating the discontinuities on the object boundaries [6]- [13], have been widely applied to spline curve fitting [14], [15], automated visual inspection [16]- [19], image segmentation [20]- [22], object recognition [23], etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revision of Using Eigenvalues of Covariance Matrices in Boundary-Based Corner Detection

Horng

Chen

2009

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYIn this paper, we present a revision of using eigenvalues of covariance matrices proposed by Tsai et al. as a measure of significance (i.e., curvature) for boundary-based corner detection. We first show the pitfall of Tsai et al.'s approach. We then further investigate the properties of eigenvalues of covariance matrices of three different types of curves and point out a mistake made by Tsai et al.'s method. Finally, we propose a modification of using eigenvalues as a measure of significance for corner detection to remedy their defect. The experiment results show that under the same conditions of the test patterns, in addition to correctly detecting all true corners, the spurious corners detected by Tsai et al.'s method disappear in our modified measure of significance.

show abstract

“…The eigenvalues of such rows and columns matrix shows the main feature of the images. In image processing techniques such as image enhancement, image compression [10], pattern recognition [11] and face identification [12], calculation of eigenvalues and eigenvectors is required. The largest eigenvalue represent the important (dominant) features of the image whereas the smallest eigenvalue represent the less important features or noise which can be neglected.…”

Section: Proposed Approach For Estimation Of Eigenvalues Of Imagesmentioning

confidence: 99%

A New Approach for Estimation of Eigenvalues of Images

Gaidhane¹,

Hote²,

Singh³

2011

IJCA

View full text Add to dashboard Cite

In this paper, a new approach for estimation of eigenvalues of images is presented. The proposed approach is based on the Gerschgorin's circles theorem. This approach is more efficient as there is no need of calculation of all real eigenvalues. It is also helpful for all type of images where the calculation of eigenvalues may be impractical. More importantly, anyone can come to the conclusion by visual inspection as it is a graphical method. The estimation of eigenvalues can be used to extract the important information of images for various applications.

show abstract

Invariant 2D object recognition using eigenvalues of covariance matrices, re-sampling and autocorrelation

Cited by 15 publications

References 25 publications

SAG mill system diagnosis using multivariate process variable analysis

SAG mill system diagnosis using multivariate process variable analysis

Revision of Using Eigenvalues of Covariance Matrices in Boundary-Based Corner Detection

A New Approach for Estimation of Eigenvalues of Images

Contact Info

Product

Resources

About