6. Conclusions

Figure 16. Histograms for 500 images from the database BSD500 [19].

110 Advances in Memristor Neural Networks – Modeling and Applications

Figure 17. Histograms for 500 images with Gaussian noise from the database BSD500 [19].

A symbolic model for a charge-controlled memristor has been developed. The model has been incorporated to a memristive grid that has been used as a filter for image smoothing and edge detection. A simple evaluation of the memristance expression confirmed that the model fulfills the fingerprints for the i � v pinched hysteresis loop. Besides, special attention was devoted to the memristance-charge characteristic of the anti-series connection because it constitutes the key element in the memristive grid for achieving edge detection.

The methods for image edge detection usually use a smoothing filter as the first step to improve edge detection. However, in the memristive grid, the smoothing filter is naturally implemented by the same circuit, which allows to have an analog processor that implements both functions. In addition, the grid presents a good performance in edge detection in comparison with the human outcomes.

Future lines of research are mainly devoted to speed up the edge-detection procedure for highresolution images. A relevant topic is to solve the DAEs emanating from the memristive grid by performing parallel computations on multicore computers. In this case, the edge detection can be applied to images arising from data-intensive scenarios, such as medical imaging and remote-sensing imagery.
