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Local cost between two neighboring pixels depends on the Laplacian zero-crossings, gradient magnitude and direction.
Recently, Wu and Tsai [11] proposed a "pixel value differencing" (PVD) method that computes the difference value between two neighboring pixels to determine how many secret bits should be embedded into a cover pixel.
where ρ is a local dissimilarity measure between two neighboring pixels and l is the path length.
The spatial distance between two neighboring pixels is one, i.e., h = 1 for image and τ is the time interval between the consecutive iterations.
Next, for the DE-based schemes, it utilizes the difference value between two neighboring pixels for embedding one secret bit, leading to the capacity of 0.5 bit/pixel (bpp).
where γ is a local propagation cost between two neighboring pixels, p0 is a pixel that lies at the seed boundary, p l =x, and l is the total path length.
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Path-based methods for vessel extraction define the cost of traversing the arc that connects any two neighboring pixels in the image in such a way that arcs between vessel pixels are more likely to have lower cost.
In detail, the modified Sobel-Feldman operation lies in expanding the conventional kernel size of the operation from 3×× 3 to 5 × 5, and up to 13 × 13 using the average gradient vectors of two neighboring pixels (2-pixel) instead of one single pixel (1-pixel) to calculate the angularity index.
We interpolate between four neighboring pixels to deduce the intensity for an arbitrary position (x,y) begin{array}rcl@ I x,y) & = & (1-Delta x cdot 1-Delta y)cdot I(x cdot 1-Delta&+, (1-Delta x)cdotDelta y cdotI(x_{i},y_{i}1}) &&+, Delta xcdot(1-Delta y)cdot I(x cdotDeltai}) &&+,Delta xcdotDelta ycdot I(x_{i}1},y_{i+1}), end{array} (2).
Neighborhood N s : Eight neighboring pixels of pixel s.
In DEM dataset, every pixel is potentially surrounded by eight neighboring pixels.
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