In this paper, an improved image fusion algorithm based on PCNN
and Contourlet transform is proposed to detect obstacles in forests.
Ma, "A self-adapting method for RBC count from different blood smears based on PCNN
and image quality," in Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, pp.
(5) When the PCNN
algorithm performs the segmentation task, it searches pixels with similar gray levels only within the neighborhood of the internal connection matrix; in the absence of constraints, some small spurious information will enter into the segmentation results.
Then, 3D PCNN
is used to compute the spatial-temporal activity levels of the denoised high-pass coefficients to obtain the spatial-temporal activity maps, which are employed to merge the fused high-pass subband by the selecting maximum.
 using PCNN
and SVM and using wavelet and fuzzy sets for enhancement EINawasany et Classifying MR MRI al.
Guo, "Multifocus image fusion scheme based on features of multiscale products and PCNN
in lifting stationary wavelet domain," Optics Communications, vol.
One is that many parameters need to be set manually in PCNN
, that is, lack of adaptability.
In our previous work , we structured a PCNN
according to the input types for generating outputs.
In Table 1, we compared the results of original Hashing feature and feature fusion with the PCNN
feature under different normalization methods.
and its numerous variations are found to be useful in a wide variety of applications including smoothing, feature binding, edge and peak curvature extraction, image fusion, image decomposition, path optimisation, invariant feature generation, logical rule sets and impulse movement detection .
On the basis, the Pulse Coupling Neural Network (PCNN
) presented in [20, 21] is introduced to denoise, because it has the advantage that the industrial data process does not depend on precise mathematics model.
Compared with other artificial neural networks, PCNN
has an incomparable advantage over other traditional artificial neural networks .