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Modeling and inferring 2.1D sketch with mixed Markov random field

Journal of Systems Engineering and Electronics 2017年第2期28卷 361-373页

作者： Anlong Ming Yu Zhou Tianfu Wu School of Computer Beijing University of Posts & Telecommunications Beijing 100876 China Institute of Sensing Technology and Business (Wuxi) Beijing University of Posts & Telecommunications Beijing 100876 China Department of Statistics University of California Los Angeles 90095 USA

This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: the input image layer, the graphical representation layer of the computed 2D atomic regions and 3-degree junctions (such as T or arrow junctions), and the 2.1D sketch layer. There are two types of vertices in the graphical representation of the 2D entities: (i) regions, which act as the vertices found in traditional MRF, and (ii) address variables assigned to the terminators decomposed from the 3-degree junctions, which are a new type of vertices for the mixed MRF. We formulate the inference problem as computing the 2.1D sketch from the 2D graphical representation under the Bayesian framework, which consists of two components: (i) region layering/coloring based on the Swendsen-Wang cuts algorithm, which infers partial occluding order of regions, and (ii) address variable assignments based on Gibbs sampling, which completes the open bonds of the terminators of the 3-degree junctions. The proposed method is tested on the D-Order dataset, the Berkeley segmentation dataset and the Stanford 3D dataset. The experimental results show the efficiency and robustness of our approach. © 2017 Beijing Institute of Aerospace Information.

关键词： Graphic methods Image segmentation Inference engines Markov processes structural frames

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MODAL PARAMETERS EXTRACTION WITH CROSS CORRELATION FUNCTION AND CROSS POWER SPECTRUM UNDER UNKNOWN EXCITATION

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Chinese Journal of Aeronautics 2000年第1期13卷 19-23页

作者：郑敏申凡陈怀海鲍明 Institute of Vibration Engineering Research Nanjing University of Aeronautics and AstronauticsNanjing 210016 China

In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation for the cross-correlation functions and cross-power spectra between the outputs under the assumption of white-noise excitation. It widens the field of modal analysis under ambient excitation because many classical methods by impulse response functions or frequency response functions can be used easily for modal analysis under unknown excitation. The Polyreference Complex Exponential method and Eigensystem Realization Algorithm using cross-correlation functions in time domain and Orthogonal Polynomial method using cross-power spectra in frequency domain are applied to a steel frame to extract modal parameters under operational conditions. The modal properties of the steel frame from these three methods are compared with those from frequency response functions analysis. The results show that the modal analysis method using cross-correlation functions or cross-power spectra presented in this paper can extract modal parameters efficiently under unknown excitation.

关键词： Algorithms Correlation methods Dynamic response Eigenvalues and eigenfunctions Frequency domain analysis Functions Modal analysis Parameter estimation structural frames Time domain analysis Vibrations (mechanical) White noise

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