Search

In order to study the influence of the primary bedding structure on wave velocity and mechanical properties of coal-rock mass, multilayer and multi-directional ultrasonic and CT scanning tests were carried out in laboratory. It also aims to improve the accuracy, rapidity and convenience of obtaining the wave velocity and mechanical parameters of coal-rock mass in laboratory. Based on the ultrasonic test, the characteristics of wave velocity and wave velocity ratio at different layers of coal-rock mass under the primary bedding dip angle were obtained. Combined with CT scanning and 3D reconstruction technology, the calculation method of CT gray mean value in different layers was proposed by using the data of the gray frequency of coal and rock. And the variation law of gray value and coal and rock content of coal-rock mass in different layers under different primary bedding dip angle was obtained. Meanwhile, the relationships between CT gray mean value and wave velocity, mechanical parameters of coal-rock mass were established. Considering the effect of bedding dip angle and the content of coal and rock, the calculation model of longitudinal wave velocity of coal-rock mass was constructed. Then the correctness of the model is verified by comparing the test data. The results show that: ① the wave velocity and wave velocity ratio of coal-rock mass with primary bedding are linearly related to bedding dip angle; with the increase of bedding dip angle, the longitudinal wave velocity of coal-rock mass decreases linearly, while the distribution range of wave velocity ratio expands; ② the mean wave velocity of CT is linearly correlated under different bedding dip angles, and the wave velocity of coal-rock mass increases linearly with the increases of CT gray mean value; ③ the density of coal-rock mass increases linearly with the increase of CT gray mean value, the dynamic elastic modulus and shear modulus of coal-rock mass have third order polynomial relationships with the mean value of CT gray level, and they tend to increase with the increase of the mean value of CT gray level; ④ compared with the bedding dip angle, the wave velocity of coal-rock mass is more sensitive to the coal and rock content, and the wave velocity changes largest during the content of coal and rock similar.

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Uranium Geology is the property of Uranium Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the enhancement effect of infrared image, according to the imaging characteristics of existing infrared imaging, an infrared image enhancement method based on improved histogram is proposed in this paper. For the phenomenon that the infrared image may be highlighted around, this paper comprehensively considers the image brightness intensity and contrast, then determines the clipping area of the original image. On this basis, this paper proposes a method of uniform distribution of gray levels to avoid the loss of image details caused by the combination of gray levels with a small number of pixels. Experiments show that compared with the traditional histogram equalization method, the improved algorithm proposed in this paper has 21.87%, 2.60% and 14.52% improvements in entropy value, standard deviation and fuzzy linear index respectively. The key details of the image are enhanced while improving the image contrast, which confirms the correctness of the theoretical analysis.

Background. The present study was undertaken to compare the Hounsfield Unit (HU) in computed tomography (CT) with the gray level in CBCT in human tissues. Methods. In this study, 25 different soft and hard tissues were evaluated in 21 patients. CBCT images were taken with Newtom VGi machine (Verona, Italy) and CT images were prepared with Somatom Sensation unit (Siemens, Germany). The HU values of soft and hard tissues were compared with the gray level values of CBCT images. Results. There was a strong correlation between the HU in CT and the gray level in CBCT in soft tissues (P

In this paper, our main purpose is to demonstrate a new box counting method called 'pixel range calculation (PRC) method' to carry out image analysis to discover its smoothness or roughness. In the proposed method, we have mainly focused on the pixel intensity and their ranges so that it will give the appropriate gray-level count covered by each box. By getting the appropriate box count, we can measure the level of roughness or smoothness of various images. Lena and Baboon image of 64 × 64 pixels and then images with resolution of 256 × 256 pixels of Female, Areal, Moon surface, House, Tree, Female2 and Jelly Bean image have been taken for carrying out the experiment. The experiment was conducted using some of the existing methods available in the literature and the proposed method on the images mentioned, to verify the applicability and accuracy of the proposed method. The result of the experiment based on the box count shows that the proposed method is giving better result in terms of efficiency and accuracy as compared to other techniques. [ABSTRACT FROM AUTHOR]

Over 60 years, health organization's survey exposed that glaucoma leads to permanent vision disorder in human eye. Although 79 million of people were affected by this second major cause in 2020, glaucoma detection technique exploits preventing scheme at the earlier stage and reducing the necessary disease treatment cost. This research is trained probe a best analysis for glaucoma prediction in segmented retinal fundus images to assist an ophthalmologist. Our model utilizes a many feature extraction approach to detect glaucoma from digital fundus image using a fusion feature set. Along-with extraction way, Gray Level Coherence Matrix, Gray Level Run Length Matrix, Gray Level Difference Method approaches are used to extract texture based features. The contributions of extracted spatial domain from these approaches are expected to formulate higher efficiency of fusion feature set using classification. Thus, proposed work is presented with the comparison of four machine learning algorithms like support vector machine (SVM), k-nearest neighbors (kNN), Decision Tree (DT) and Naive Bayes (NB). Diagnosis methodology is clearly demonstrated a way to detect glaucoma in early stage from fundus image. Performance of the classifier is analyzed by computing the accuracy value. Then effectiveness of the system is improved by the combination of texture with supervised learning techniques. [ABSTRACT FROM AUTHOR]

• Quick automatic extracting the head photo. • Need not given tripmap or scribble. • Creating a new training data set for hair matting. • High performance of a strategy for reducing search space. Image matting is a process of extracting objects from background in an image, which is an important work in digital image processing and video editing. Previous methods have poor performance and most methods need trimap or scribble to compute to get accurate image matting results. In this paper a quick automatic head image matting method for certificate photo production is put forward, which could rapidly extract satisfied head photo from images that are shoot by portable camera. First a new training data set about hair matting is created, and the image is segmented into different regions according to different gray level; then we detect and locate the face and eyes to adjust the correct head position; Finally, the accurate hair pixels are extracted from the edge region around head by multimodal Gaussian process regression. It's the advantage that the regions with clear foreground and background could be quickly extracted by segmented method, and the regions with similar foreground and background colors or complicated textures were labeled (the edge regions with hair around head), thus the hair could be extracted in smaller area. In our method the quick and accurate extracting of head photo needn't the trimap or scribbling or lots of training. Experimental results clearly demonstrate the superiority of our algorithm over previous methods. [ABSTRACT FROM AUTHOR]

Background: Jaw bone quality plays an essential role in treatment planning and prognosis of dental implants. Regarding several available methods for bone density measurements, they are not routinely used before implant surgery due to hard accessibility. Objective: An in vitro investigation of correlation between average gray scale in direct digital radiographs and Hounsfield units in CT-Scan provides a feasible method for evaluating alveolar bone quality prior to implant surgery. Methods: 26 sheep’s mandibles in which a square shape ROI marked by gutta percha, were prepared. Three direct digital radiographs (CCD sensor) from every specimen were taken using 80, 100 and 200 milli-seconds. Then, the average gray levels for ROIs were calculated using a costume-made software. Next, the specimens were scanned using a 16-slice spiral CT and the Hounsfield Unit of each ROI was calculated. Pearson analysis measured the correlation between Hounsfield units and average gray levels. Results: There was a positive correlation between Hounsfield unit and average gray level in the radiographs and the correlation was better in higher exposure times. Conclusion: It is possible to estimate Hounsfield unit and bone density in the jaw bones using average gray scale in a digital radiograph. This approach is easy, simple and available and also results in lower patient exposure comparing other bone densitometric analysis methods.

Background. The present study was undertaken to compare the Hounsfield Unit (HU) in computed tomography (CT) with the gray level in CBCT in human tissues. Methods. In this study, 25 different soft and hard tissues were evaluated in 21 patients. CBCT images were taken with Newtom VGi machine (Verona, Italy) and CT images were prepared with Somatom Sensation unit (Siemens, Germany). The HU values of soft and hard tissues were compared with the gray level values of CBCT images. Results. There was a strong correlation between the HU in CT and the gray level in CBCT in soft tissues (P<0.001, R2=0.85) and hard tissues (P<0.001, R2=0.74) and in general (P<0.001, R2=0.91). Conclusion. A high degree of agreement was seen between HU in CT and gray level in CBCT in both hard and soft tissues. Since the gray level in CBCT was similar to HU in CT and can be used as a parameter determine bone density in implant treatment and also to determine the bone type, the CBCT technique is recommended in such cases due to its low radiation dose, short time and low cost compared to CT. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)