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| 1 | 2-D IMAGE-BASED VOLUMETRIC MODELING FOR PARTICLE OF RANDOM SHAPE显示文摘In this paper, an approach to predicting randomly-shaped particle volume based on its two-Dimensional (2-D) digital image is explored. Conversion of gray-scale image of the particles to its binary coun-terpart is first performed using backlighting technique. The silhouette of particle is thus obtained, and conse-quently, informative features such as particle area, centroid and shape-related descriptors are collected. Several dimensionless parameters are defined, and used as regressor variables in a multiple linear regression model to predict particle volume. Regressor coefficients are found by fitting to a randomly selected sample of 501 parti-cles ranging in size from 4.75mm to 25mm. The model testing experiment is conducted against a different ag-gregate sample of the similar statistical properties, the errors of the model-predicted volume of the batch is within ±2%. | Chen Ken Larry E. Banta Jiang Gangyi | 2006 | Journal of Electronics(China)2006，23，6： | 6 |
| 2 | SADDLE-POINT BASED SEPARATION OF TOUCHED OBJECTS IN 2-D IMAGE显示文摘In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging. | Chen Ken Larry E. Banta Jiang Gangyi | 2006 | Journal of Electronics(China)2006，23，3： | 5 |
| 3 | Estimation of lime stone particle mass from 2D images显示文摘 | Larry Banta Ken Cheng Zaniewski J | 2003 | Powder Technology2003，132，： | 1 |
| 4 | Estimation of limestone particle mass from 2-D images 显示文摘 | Larry Banta Ken Chen Zaniewski J | 2003 | Powder Technology2003，132，： | 1 |
| 5 | Estimation of limestone particle mass from 2D images显示文摘 | Larry Banta Ken Cheng John Zaniewski | 2003 | Powder Tech nology2003，132，23： | 1 |
| 6 | Estimation of limestone particle mass from 2-D images 显示文摘 | Larry Banta Ken Cheng Zaniewski J | 2003 | Powder Technology2003，132，： | 1 |
| 7 | Estimation of limestone particle mass from 2-D images 显示文摘 | Larry Banta Ken Cheng Zaniewski J | 2003 | Powder Technology2003，，： | 1 |
| 8 | AGGREGATE IMAGE BASED TEXTURE IDENTIFICATION USING GRAY LEVEL CO-OCCURRENCE PROBABILITY AND BP NEURAL NETWORK显示文摘Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by jointly using Gray Level Co-occurrence Probability(GLCP) and BP neural network techniques.First, up to 8 GLCP-associated texture feature parameters are defined and computed, and these consequent parameters next serve as the inputs feeding to the BP neural network to calculate the similarity to any of given aggregate texture type.A finite number of aggregate images of 3 kinds, with each containing specific type of mineral particles, are put to the identification test, experimentally proving the feasibility and robustness of the proposed method. | Chen Ken Wang Yicong Zhao Pan Larry E. Banta Zhao Xuemei | 2009 | Journal of Electronics(China)2009，26，3： | 0 |
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