Research on Coal and Rock Type Recognition Based on Mechanical Vision

In order to identify different kinds of coal, rock, and gangue, the FPV integrated image transmission camera is used to collect images of 6 types of coal, 8 types of rocks, and 2 types of coal gangue, and the images are processed based on the two-dimensional discrete wavelet transform (2D-DWT) based on the steerable pyramid decomposition (SPD). The maximum likelihood estimation method is used to estimate the parameters, and, the coal and rock types are judged by comparing the similarity of each image. The results show the following: (1) in the eight kinds of rocks, the recognition accuracy of shale and limestone is 90%, that of anorthosite is 95%, and those of other rocks are 100%; (2) the accuracy of comprehensive identification of coal, rock, and gangue is 93%, the comprehensive of coal and gangue is 78%, and the rock classification is 97%; (3) the identification time of 6 types of coal samples, 8 types of rock samples, and 2 types of coal gangue samples are in the range of 2 s∼3 s, which is far less than 10 s, which can meet the requirements of coal and rock identification in terms of recognition speed; and (4) according to 20 groups of data, the range, variance, and standard deviation of the same coal gangue sample meet the accuracy requirements of coal and rock identification. The identification method provides an effective method to improve the efficiency of coal separation, effectively determine the distribution of coal and rock, and timely adjust the cutting height of shearer drum and the operation parameters of various fully mechanized mining equipment, so as to improve the recovery rate of coal resources.

Statistical Goodness Factor ‘ᴦ’ for Image Fusion Algorithm Based on UGGD Parameters

In this paper we propose a novel pyramid decomposition based Image fusion metric, Gamma Factor or Goodness of Fit ‘ᴦ’ which describes the statistically amount of information fused by the image fusion algorithm. We first apply steerable pyramid decomposition and then a fitting model for Univariate Generalised Gaussian Distribution (UGGD) parameter estimation. From the UGGD; P and S fitting model coefficients are computed. To estimate the optimum weights for computation a huge data set of complimentary images are used. Using these weights, amount of information contributed by each image to form a fused image can be estimated. Experimental results show the tremendous matching with the quantise information

A CORRELATIVE TWO-STEP APPROACH TO HALLUCINATING FACES

Face hallucination is to synthesize high-resolution face image from the input low-resolution one. Although many two-step learning-based face hallucination approaches have been developed, they suffer from the expensive computational cost due to the separate calculation of the global and local models. To overcome this problem, we propose a correlative two-step learning-based face hallucination approach which bridges the gap between the global model and the local model. In the global phase, we build a global face hallucination framework by combining the steerable pyramid decomposition and the reconstruction. In the residue compensation phase, based on the combination weights and constituent samples obtained in the global phase, a residue face image is synthesized by the neighbor reconstruction algorithm to compensate the hallucinated global face image with subtle facial features. The ultimate hallucinated result is synthesized by adding the residue face image to the global face image. Compared with existing methods, in the global phase, our global face image is more similar to the original high-resolution face image. Furthermore, in the residue compensation phase, we use the combination weights and constituent samples obtained in the global phase to compute the residue face image, by which the computational efficiency can be greatly improved without compromising the quality of facial details. The experimental results and comparisons demonstrate that our approach can not only generate convincible high-resolution face images efficiently, but also has high computational efficiency. Furthermore, our proposed approach can be used to restore the damaged face images in image inpainting. The efficacy of our approach is validated by recovering the damaged face images with visually good results.