Fast vector median filter based on Euclidean norm approximation

Barni, Mauro; Cappellini, V.; Mecocci, A.

doi:10.1109/97.295343

Cited by 53 publications

(27 citation statements)

References 2 publications

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“…Afterwards, the Euclidean norm of local feature vectors is calculated [23] by the following formula, respectively: Obtained. It should be underlined that, differently from [24], norm-1 has been used. In order to distinguish the defect area from the fabric image, a threshold T e is calculated to generate a binary mask of defects, where "0" denotes a defected blocks: …”

Section: Figure 2 Perspective View Of (A) Even and (B) Odd Parts Of mentioning

confidence: 99%

Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA

Jing

Fan

2016

IJMUE

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Section: Figure 2 Perspective View Of (A) Even and (B) Odd Parts Of mentioning

confidence: 99%

Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA

Jing

Fan

2016

IJMUE

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“…In [10], the Euclidean norm is approximated as a linear combination of other norms such as 1 -and ∞ -norm. However, a number of complicated operations such as division and multiplication are used in those approximations [11], [12].…”

Section: Previous Workmentioning

confidence: 99%

“…Instead, we can find some works on the approximation of composite operations which contain square-root operations. In [11] and [12], the Euclidean norm ( 2 -norm) required in the vector median filtering is approximated as a linear combination of the components of a vector. In [10], the Euclidean norm is approximated as a linear combination of other norms such as 1 -and ∞ -norm.…”

Section: Previous Workmentioning

confidence: 99%

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Multiplier-less and Table-less Linear Approximation for Square-Related Functions

Park

Kim

2010

IEICE Trans. Inf. & Syst.

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SUMMARYSquare-related functions such as square, inverse square, square-root and inverse square-root operations are widely used in digital signal processing and digital communication algorithms, and their efficient realizations are commonly required to reduce the hardware complexity. In the implementation point of view, approximate realizations are often desired if they do not degrade performance significantly. In this paper, we propose new linear approximations for the square-related functions. The traditional linear approximations need multipliers to calculate slope offsets and tables to store initial offset values and slope values, whereas the proposed approximations exploit the inherent properties of square-related functions to linearly interpolate with only simple operations, such as shift, concatenation and addition, which are usually supported in modern VLSI systems. Regardless of the bit-width of the number system, more importantly, the maximum relative errors of the proposed approximations are bounded to 6.25% and 3.13% for square and square-root functions, respectively. For inverse square and inverse square-root functions, the maximum relative errors are bounded to 12.5% and 6.25% if the input operands are represented in 20 bits, respectively.

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“…Filter, (VMF) [9,128,13,15,36,105,107,109,130,135]. The definition of the multichannel median is a direct extension of the ordinary scalar median definition with the L 1 or L 2 norm utilized to order vectors according to their relative magnitude differences [9].…”

Section: Vector Median Filter (Vmf)mentioning

confidence: 99%