A simple and robust algorithm was proposed to determine the rotational differences between two images based on orientation histogram matching accumulated from local orientation of each pixel without any feature extraction.
Figure 3 shows match value variation of object related to angle variation and distance of camera based on histogram.
The algorithm presented in [24] is based on histogram modification.
Secondly, a low complexity auto white balance hardware architecture is presented based on histogram equalization algorithm.
We study several algorithms based on histogram differences and evaluate them with respect to precision, recall, and processing latency.
Motivated by this, a novel automatic grayscale image colorization method based on histogram regression is presented in this paper.
We constructed these categories based on histogram distributions of the XRF bromine readings.
In recent years, local image descriptors based on histograms of oriented gradients (e.g., SIFT, DAISY) and intensity orders (e.g., LBP, LIOP) have been popular for the keypoint matching.
As an entire algorithm, reversible data hiding based on histogram-difference-shift is also reported.
