Extraction of textural feature and recognition of coal flotation froth

TLDR: In this article, a series of textural features such as energy (E), entropy ( ENTS), inertia ( I ) of SGLDM and small number emphasis ( Fine), large number emphasis( Fine), coarse, entropy ( ENTN), second moment ( SM), number nonuniformity ( NN ) of NGLDM are proposed to describe the coal froth textural characteristics.

Abstract: By conducting a series of coal batch cell flotation experiments, a number of digital froth images are captured. Two algorithms--the spatial gray level dependence matrix (SGLDM) and the neighboring gray level dependence matrix (NGLDM) are introduced to extract the visual textural characteristics of coal froth images. Based on these two matrixes, a series of textural features such as energy ( E ), entropy ( ENTS ), inertia ( I ) of SGLDM and small number emphasis ( Fine ), large number emphasis ( Coarse ), entropy ( ENTN ), second moment ( SM ), number nonuniformity ( NN ) of NGLDM are proposed to describe the coal froth textural characteristics. By using the software developed by the author with DELPHI language, the textural features of flotation froth images captured in laboratory experiments are extracted. The change tendency of each feature with flotation time is analyzed, and the relationship between each feature and froth textural feature is pointed out qualitatively. It is found { E, ENTS, I } of SGLCM and { Fine, Coarse, ENTN } of NGLDM really reveal the variation tendency of image texture of coal froth. However, the { ENTN, SM,NN } of NGLDM have little relationship with image textural characteristics of coal flotation froths. Choosing the reliable textural features { E, ENTS, I, Fine, Coarse, ENTN } as the input to a set of neural network called self-organizing feature mapping, all the images are classified into four patterns, and the average correct rate is 76.5%.