Parallel image matching on a distributed system

Author(s):  
J. You ◽  
W.P. Zhu ◽  
E. Pissaloux ◽  
H.A. Cohen
1995 ◽  
Vol 1 (4) ◽  
pp. 245-259 ◽  
Author(s):  
J. You ◽  
E. Pissaloux ◽  
W.P. Zhu ◽  
H.A. Cohen

Author(s):  
Sukhbeer Singh ◽  
Sarbjeet Singh ◽  
Sukhvinder Singh ◽  
Mandeep Kour

Image processing is a task of analysing the image and produces a resultant output in linear way. Image processing tasks are widely used in many applications domains, including medical imaging, industrial manufacturing, entertainment and security systems. Often the size of the image is very large, the processing time has to be very small and usually real-time constraints have to be met. The image analysis requires a large amount of memory and cpu performance, to cope this problem image processing task is parallelized. Parallelism of image analysis task becomes a key factor for processing a huge raw image data. Parallelization allows a scalable and flexible resource management and reduces a time for developing image analysis program. This paper presenting, the automatic parallelization of image processing task in a distributed system, in which suitable subtasks for parallel processing are extracted and mapped with the components of distributed system. This paper presents different design issues of parallel image processing in distributed system. Which helps the image analysis tasks that how to post processing the image in parallel. This technique is quite interactive especially when developing parallel program, as this requires little efforts for finding a suitable distribution of program module and data.


Author(s):  
A. Olsen ◽  
J.C.H. Spence ◽  
P. Petroff

Since the point resolution of the JEOL 200CX electron microscope is up = 2.6Å it is not possible to obtain a true structure image of any of the III-V or elemental semiconductors with this machine. Since the information resolution limit set by electronic instability (1) u0 = (2/πλΔ)½ = 1.4Å for Δ = 50Å, it is however possible to obtain, by choice of focus and thickness, clear lattice images both resembling (see figure 2(b)), and not resembling, the true crystal structure (see (2) for an example of a Fourier image which is structurally incorrect). The crucial difficulty in using the information between Up and u0 is the fractional accuracy with which Af and Cs must be determined, and these accuracies Δff/4Δf = (2λu2Δf)-1 and ΔCS/CS = (λ3u4Cs)-1 (for a π/4 phase change, Δff the Fourier image period) are strongly dependent on spatial frequency u. Note that ΔCs(up)/Cs ≈ 10%, independent of CS and λ. Note also that the number n of identical high contrast spurious Fourier images within the depth of field Δz = (αu)-1 (α beam divergence) decreases with increasing high voltage, since n = 2Δz/Δff = θ/α = λu/α (θ the scattering angle). Thus image matching becomes easier in semiconductors at higher voltage because there are fewer high contrast identical images in any focal series.


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