Hexagonal CNN and its applications in sphalerite banding texture simulation

نویسندگان

  • Deyi Xu
  • Qiuming Cheng
  • Zhijing Wang
چکیده

Banding textures are commonly observed in mineral crystals for example in sphalerites in Mississippi Valley-type deposit. Cellular Nonlinear Network (CNN) has been developed in the literature for characterizing complexity especially for reaction-diffusion dynamic systems. These include models describing periodic precipitation incorporating nucleation and banding pattern formation in minerals. Based on observation on hexagonal microtexture of the sphalerite in Jingding Pb-Zn large mineral deposit, Yunnan, China, a CNN model in hexagonal coordinate system was developed in this paper to simulate the process for the forming of sphalerite under the presumption that the dynamic procedure begins from the rim of the system inward to the center. The simulated results show that the sphalerite formed has Liesegang band texture and the radii of the crystallites oscillating decrease from the rim of the sphalerite crystal inward forming a big core at the center. The results are in accordance with the observations which demonstrated that the CNN model introduced in this paper is reasonable and can be used to interpret the mechanism for the forming of various textures of sphalerite. Keywards Hexagonal coordinate system, CNN, Liesegang pattern, Sphalerite Correspondance to Qiuming Cheng at: E-mail: [email protected] Tel.: +1 416 736 5245 Fax: +1 416 736 5817

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Texture Segmentation by the 64x64 Cnn Chip

CNN’s fast image processing technology helps us to run high-speed filtering tasks for image enhancement, recognition or segmentation. Texture analysis is a specific task, since the whole image is processed massively parallel while we have a limited number of texture-specific filtering and evaluation steps. Former results of simulations and recognition results of simple CNN chips show that the C...

متن کامل

Color Texture Boundary Detection Using Three-Layer CNN Based on Hybrid-Feature

In this paper, a color texture boundary detection using cellular neural networks (CNN) is presented. The approach is similar to the early vision system of the human brain. The proposed algorithm has been tested on synthetic color texture images. Additionally, we carry out the error evaluation between ideal boundaries and detected boundaries. From our simulations, we confirm that the boundaries ...

متن کامل

Fabrication and Optical Behaviors of Core–Shell ZnS Nanostructures

Novel core-shell nanostructures comprised of cubic sphalerite and hexagonal wurtzite ZnS have been synthesized at 150°C by a simple hydrothermal method. The results of HR-TEM and SAED investigation reveal that the cores of hexagonal wurtzite ZnS (ca. 200 nm in average diameter) are encapsulated by a shell of cubic sphalerite ZnS. The FE-SEM image of the nanomaterials shows a surface tightly pac...

متن کامل

Texture Classification and Segmentation by Cellular Neural Networks Using Genetic Learning

We present a new single-chip texture classifier based on the cellular neural network (CNN) architecture. Exploiting the dynamics of a locally interconnected 2D cell array of CNNs we have developed a theoretically new method for texture classification and segmentation. This technique differs from other convolution-based feature extraction methods since we utilize feedback convolution, and we use...

متن کامل

Deep convolutional filter banks for texture recognition and segmentation

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Computers & Geosciences

دوره 36  شماره 

صفحات  -

تاریخ انتشار 2010