We give a fast and practical algorithm for statistical learning hyperparameters from observable data in probabilistic image processing, which is based on Gaussian graphical model ...
In this paper a simple but effective approach for parallelization of cellular neural networks for image processing is developed. Digital gray-scale images were used to evaluate th...
Modern embedded systems for image processing involve increasingly complex levels of functionality under real-time and resourcerelated constraints. As this complexity increases, th...
Abstract. Image processing is widely used in many applications, including medical imaging, industrial manufacturing and security systems. In these applications, the size of the ima...
Thanks to a specific formalism for signal generation, it is possible to transpose an image processing problem to an array processing problem. For straight line characterization, t...