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Section 4 is dedicated to calculate the watermark capacity based on image complexity.
In this paper, we introduced a new method for calculating watermark capacity based on image complexity.
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Object tracking based on image has the difficulties and complexities.
Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasoning based on image recognition.
Additionally, an image palette reduction algorithm is introduced to eliminate redundant colors in the image; this reduction is based on local image complexity estimation which employed the median algorithm and the standard deviation [13].
Yet this reputation is based on image, not reality.
TACs were produced based on image statistics.
Based on image formation methodology, fNIRS provides two types of brain images: topographic and tomographic images.
Contours were drawn on the T2-weighted images, based on image pixel intensities.
Additionally, an image palette reduction algorithm based on local image complexity estimation is introduced to eliminate redundant colors, thus decreasing the number of pixels that are bad classified in the segmentation process and minimizing the resource consumption of the mobile device.
The proposed quantization technique is based on local image complexity estimation employing the median algorithm and the standard deviation [13].
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