基于马尔可夫随机场模型的图像分割技术研究

本文针对基于马尔可夫随机场模型（MRF）的图像分割技术进行研究，通过深入分析马尔可夫随机场模型用于图像分割时的优缺点，提出了改进方案，将其用于单帧图像的无监督分割和动态场景下的运动目标分割。主要研究内容包括以下几部分。 第一部分详细介绍了马尔可夫随机场模型，包括邻域系统和基团的概念、初始标记场的获取、能量函数的确立和MAP估算方法。 第二部分针对噪声图像的预处理，提出一种多尺度双边滤波算法来综合不同尺度下双边滤波的去噪效果。为降低双边滤波的计算复杂性，提出一种双边滤波快速计算方法。该算法能够在去除噪声的同时较好地保留边缘。 第三部分针对MRF模型用于图像分割中遇到的过平滑问题，定义了一种间断自适应高斯马尔可夫随机场模型（DA-GMRF），提出一种基于该模型的无监督图像分割方法。利用灰度直方图势函数自动确定分类数及分割阈值，进行多阈值分割得到标记场的初始化，用Metroplis采样器算法进行标记场的优化，得到最终的分割结果。该方法考虑了平滑约束在图像边缘处的自适应性，避免了边缘处的过平滑，将其应用于无监督图像分割取得了较好的效果。 第四部分针对动态场景下的运动目标分割，提出一种基于间断自适应时空马尔可夫随机场模型的运动目标分割方法。解决了传统时空马尔可夫随机场模型不能对运动造成的显露遮挡现象进行处理问题，也克服了全局一致平滑假设造成的过平滑问题。帧差图像二值化得到初始标记场，初始标记场进行‘与’操作获得共同标记场，用Metroplis采样器算法实现共同标记场的优化。该方法既使用了平滑约束，而又保留了间断，从而使分割得到的运动目标边缘更加准确。

Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the state of contact. The current state of contact is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the measurement We show that their parameters can be estimated from measurements concurrently with the estimation of the parameters of the movement in each state of contact. The learning algorithm is a variant of the EM procedure. The E step is computed exactly; solving the M step exactly would require solving a set of coupled nonlinear algebraic equations in the parameters. Instead, gradient ascent is used to produce an increase in likelihood.

Equivalence and Reduction of Hidden Markov Models

This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.

Reflectance Map Techniques for Analyzing Surface Defects in Metal Castings

This report explores the relation between image intensity and object shape. It is shown that image intensity is related to surface orientation and that a variation in image intensity is related to surface curvature. Computational methods are developed which use the measured intensity variation across surfaces of smooth objects to determine surface orientation. In general, surface orientation is not determined locally by the intensity value recorded at each image point. Tools are needed to explore the problem of determining surface orientation from image intensity. The notion of gradient space , popularized by Huffman and Mackworth, is used to represent surface orientation. The notion of a reflectance map, originated by Horn, is used to represent the relation between surface orientation image intensity. The image Hessian is defined and used to represent surface curvature. Properties of surface curvature are expressed as constraints on possible surface orientations corresponding to a given image point. Methods are presented which embed assumptions about surface curvature in algorithms for determining surface orientation from the intensities recorded in a single view. If additional images of the same object are obtained by varying the direction of incident illumination, then surface orientation is determined locally by the intensity values recorded at each image point. This fact is exploited in a new technique called photometric stereo. The visual inspection of surface defects in metal castings is considered. Two casting applications are discussed. The first is the precision investment casting of turbine blades and vanes for aircraft jet engines. In this application, grain size is an important process variable. The existing industry standard for estimating the average grain size of metals is implemented and demonstrated on a sample turbine vane. Grain size can be computed form the measurements obtained in an image, once the foreshortening effects of surface curvature are accounted for. The second is the green sand mold casting of shuttle eyes for textile looms. Here, physical constraints inherent to the casting process translate into these constraints, it is necessary to interpret features of intensity as features of object shape. Both applications demonstrate that successful visual inspection requires the ability to interpret observed changes in intensity in the context of surface topography. The theoretical tools developed in this report provide a framework for this interpretation.

A velocity map ion-imaging study on ketene photodissociation at 208 and 213 nm: Rotational dependence of product angular anisotropy

Photodissociation dynamics of ketene following excitation at 208.59 and 213.24 nm have been investigated using the velocity map ion-imaging method. Both the angular distribution and translational energy distribution of the CO products at different rotational and vibrational states have been obtained. No significant difference in the translational energy distributions for different CO rotational state products has been observed at both excitation wavelengths. The anisotropy parameter beta is, however, noticeably different for different CO rotational state products at both excitation wavelengths. For lower rotational states of the CO product, beta is smaller than zero, while beta is larger than zero for CO at higher rotational states. The observed rotational dependence of angular anisotropy is interpreted as the dynamical influence of a peculiar conical intersection between the B-1(1) excited state and (1)A(2) state along the C-S-I coordinate.

Publicando mapas na Web: uso do ALOV Map.

Estudo de caso com a microbacia do Taquara Branca, Sumaré, SP. Definindo a estratégia de interação com mapas. Definindo o arquivo do projeto.

Servidor de mapas para Web: aplicação cliente com o ALOV Map.

introdução. Por onde começar?. Alov Mapa. Interface do Alov mapa. Criar arquivo HTML. Definir o arquivo de configuração do projeto. Usando a opção de busca. Usando o botão de informações. Usando o botão de ligação Web. Usando o botão de seleção (SELECT). Conclusões.

Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision

F. Smith and Q. Shen. Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 34(5):649-663, 2004.

Construction of a Festuca pratensis BAC library for map-based cloning in IFestulolium substitution lines

Iain S. Donnison, Donal M. O Sullivan, Ann Thomas, Peter Canter, Beverley Moore, Ian Armstead, Howard Thomas, Keith J. Edwards and Ian P. King (2005). Construction of a Festuca pratensis BAC library for map-based cloning in Festulolium substitution lines. Theoretical and Applied Genetics, 110 (5) pp.846-851 Sponsorship: BBSRC;BBSRC RAE2008

Head Tracking via Robust Registration in Texture Map Images

A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported.

Multi-Area Visuotopic Map Complexes in Macaque Striate and Extra-striate Cortex

We propose that a simple, closed-form mathematical expression--the Wedge-Dipole mapping--provides a concise approximation to the full-field, two-dimensional topographic structure of macaque V1, V2, and V3. A single map function, which we term a map complex, acts as a simultaneous descriptor of all three areas. Quantitative estimation of the Wedge-Dipole parameters is provided via 2DG data of central-field V1 topography and a publicly available data set of full-field macaque V1 and V2 topography. Good quantitative agreement is obtained between the data and the model presented here. The increasing importance of fMRI-based brain imaging motivates the development of more sophisticated two-dimensional models of cortical visuotopy, in contrast to the one-dimensional approximations that have been in common use. One reason is that topography has traditionally supplied an important aspect of "ground truth", or validation, for brain imaging, suggesting that further development of high-resolution fMRI will be facilitated by this data analysis. In addition, several important insights into the nature of cortical topography follows from this work. The presence of anisotropy in cortical magnification factor is shown to follow mathematically from the shared boundary conditions at the V1-V2 and V2-V3 borders, and therefore may not causally follow from the existence of columnar systems in these areas, as is widely assumed. An application of the Wedge-Dipole model to localizing aspects of visual processing to specific cortical areas--extending previous work in correlating V1 cortical magnification factor to retinal anatomy or visual psychophysics data--is briefly discussed.

Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning

This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.