Acceso usuarios
Hamid Sadeghi, Morteza Zanjireh, Wu Chung Hsien
The image processing has widely delved into mining activities for years. The image processing is actually a simulation of the human eye which is able to distinguish differences. This agent empowers any neural network or intelligent system to detect valuable minerals of any given gangue. Similarly, morphology is defined as an extensive set of image processing algorithms that processes the images based on geometric shapes. Morphological operations refer to insertion of a structure element to an input image in order to create an equal output image. In the morphological operations, the value of each pixel in the output image is taken into account in relation to a corresponding pixel in the input image along with its neighbors. Having selected a local size and shape, you may run morphological operations which are sensitive to certain shapes in the structure of input images. Since it has been argued that dilation and erosion are the most basic morphological operations, they are discussed in this article. The texture features of images have been used in image processing. These features, which are called Haralick texture features, are characterized with specific definitions and matrix formula. In this paper, it has been attempted to examine the minerals’ ore grade using the emotional network and image processing techniques. This method is fundamentally based on emotional learning and temporal difference learning. Besides, it is characterized with a fuzzy-neural structure.
