Optimal digital color image correlation

The efficiency of the proposed method is demonstrated by the comparison with the previous authors work and the Spatial Color Rich Model and CFA-Aware features for

For the particular case of a color image, we asso- ciate the Clifford Fourier transform with the action of group morphisms Φ e u,v,0,0,D , specified by only two real numbers

Table 5: Signal to noise ratio of the Y color information con- tained in G and G n channels, θ y/g denotes the geometric angle between the y color axis and the raw G axes of the

Table 5: Signal to noise ratio of the Y color information con- tained in G and G n channels, θ y/g denotes the geometric angle between the y color axis and the raw G axes of the

In this paper, a new method to reach multiresolution VQ is pre- sented. The main idea is to reorder color vectors from the code- book to obtain an index image very close to the

The proposed CFA has a natural and simple demosaicking algorithm associated to it, inspired by its characteristics in the Fourier domain. This demosaicking process amounts to

Abstract. When the amount of color data is reduced in a lossy com- pression scheme, the question of the use of a color distance is crucial, since no total order exists in IR n , n

Advantages and drawback The main advantage of this new segmentation framework is that it makes use of optimal transport to compare histograms of fea- tures, without