Edge detection in correlated noise using latin square masks

Stern, David E.; Kurz, L.

doi:10.1016/0031-3203(88)90018-0

Cited by 26 publications

(16 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that is a correlation matrix and so it is symmetric and positive-definite. Following [14], [15], there exists a nonsingular matrix such that where the first equality is due to (3.4). Therefore, are unique and they may be used interchangeably.…”

Section: Least Squares Estimates Of the Parametersmentioning

confidence: 99%

Contrast techniques for line detection in a correlated noise environment

Béhar¹,

Cheung

Kurz

1997

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

A new class of line detectors based on the theory of linear contrasts is developed. By incorporating the F-statistic and the shape test, the new algorithm detects and locates line features simultaneously, and thereby increases the detection power substantially. Many classes of detectors, including the step edge detector, naturally becomes subclasses of the proposed detector. Mathematical analysis indicates that the new algorithm that accounts for noise dependence performs better than its counterpart that neglects the dependence of noise. A symmetrical balanced incomplete-blocks (SBIB) design is introduced to model real-world images in terms of a treatment group that is associated with preexistent, conspicuous, features such as lines, edges, etc., and an incomplete treatment group that is randomized to accommodate image textures. The new algorithm in conjunction with the SBIB design is applied in the four main directions, perpendicularly to the line under investigation. It is robust, simple, efficient, and amenable to real-time applications. All pertinent statistics for the implementation of the procedure are estimated from data. Extensive computer simulations demonstrate the performance of the proposed detector.

show abstract

Section: Least Squares Estimates Of the Parametersmentioning

confidence: 99%

Contrast techniques for line detection in a correlated noise environment

Béhar¹,

Cheung

Kurz

1997

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…However, all the edges do not contain a step variation in intensity, because the properties such as refraction or poor focus can result in objects with borders defined by a gradual variation of intensity (Argyle & Rosenfeld, 1971). Many algorithms have been suggested for analysing image intensity variation, including statistical methods (Nahi & Assefi, 1972;Huang & Tseng, 1988;Stern & Kurz, 1988), difference methods (Prewitt, 1970;Marr & Hildreth, 1984) and curve fitting methods (Haralick, 1984;Nalwa & Binford, 1986). Edge detection in a noisy environment can be viewed as an optimal linear filter design problem (Torre & Poggio, 1986;Manjunath & Chellappa, 1993).…”

Section: Introductionmentioning

confidence: 99%

Multi-level threshold based edge detector using logical operations

Singh¹,

Kapoor²,

Sharma³

et al. 2016

J. Natn. Sci. Foundation Sri Lanka

View full text Add to dashboard Cite

Edge detection is a very significant feature of image processing and refers to the algorithms, which are intended for identifying points in a digital image where the image intensity changes significantly. The need of edge detection is to find the discontinuities in intensity, surface direction, change in texture properties, and variation in scene illumination. This paper proposes a rapid and superior edge detection technique, which uses the logical operations on circularly shifted images to efficiently detect the edges with lesser computational complexity. The results are compared with other edge detectors using 2-dimensional Fourier transform. Experimental results show that the proposed algorithm improves detection accuracy and possesses encouraging robustness when applied to different images, in comparison to several other edge detection algorithms. Moreover, the algorithm is also applied on a bio-medical application to segment blood cells to show the diversified nature of the proposed algorithm.

show abstract

“…The Laplacian method searches for zero crossings in the second derivative of the image to find edges. A variety of algorithms have been proposed for analyzing image intensity variation, including statistical methods [1][2][3][4][5], difference methods [6][7][8] and curve fitting methods [9][10][11][12][13]. The early days of works on edge detection were done by Sobel and Roberts [14].…”

Section: Introductionmentioning

confidence: 99%