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In our implementation, the image dimensions must be multiple of 8.
In the current implementation, the image processing tool requires a human operator to continuously supervise and take action to resolve ambiguities and/or correct tracking errors before processing continues.
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The performances from the perspective of time complexity are analyzed and compared with classical implementation, FPGA implementation, and other implementations of the image rotation.
Moreover, the optical implementation of the image encryption and decryption is given.
In this paper, we present an implementation of the image compression technique set partitioning in hierarchical trees (SPIHT) in programmable hardware.
Using the resulting relationship, our implementation performs the image warping using inverse pixel lookups.
Further details on the setup implementation and the image reconstruction procedures are provided in the Methods section.
The lower-level implementation of the image analysis protocol demands the exploration of specific computational tools and resources that implement the required sequence of data processing steps.
For example, the tools that were intended to be an image viewer are likely to have low priority for development and implementation of the image de-identification process.
In the current implementation, the reference image was first smoothed with a Gaussian kernel (FWHM = 3 times the voxel size)[17].
The results show that a single-GPU implementation is more than ten times faster as compared to a single thread CPU c2c implementation and about four times faster than a single thread CPU r2c implementation if the images are large enough.
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