Fourier analysis and gabor filtering for texture analysis and local reconstruction of general shapes

Since the pioneering work of Gibson in 1950, Shape- From-Texture has been considered by researchers as a hard problem, mainly due to restrictive assumptions which often limit its applicability. We assume a very general stochastic homogeneity and perspective camera model, for both deterministic and stochastic textures. A multi-scale distortion is efficiently estimated with a previously presented method based on Fourier analysis and Gabor filters. The novel 3D reconstruction method that we propose applies to general shapes, and includes non-developable and extensive surfaces. Our algorithm is accurate, robust and compares favorably to the present state of the art of Shape-From- Texture. Results show its application to non-invasively study shape changes with laid-on textures, while rendering and retexturing of cloth is suggested for future work. © 2009 IEEE.

Ultra-structural defects cause low bone matrix stiffness despite high mineralization in osteogenesis imperfecta mice.

Bone is a complex material with a hierarchical multi-scale organization from the molecule to the organ scale. The genetic bone disease, osteogenesis imperfecta, is primarily caused by mutations in the collagen type I genes, resulting in bone fragility. Because the basis of the disease is molecular with ramifications at the whole bone level, it provides a platform for investigating the relationship between structure, composition, and mechanics throughout the hierarchy. Prior studies have individually shown that OI leads to: 1. increased bone mineralization, 2. decreased elastic modulus, and 3. smaller apatite crystal size. However, these have not been studied together and the mechanism for how mineral structure influences tissue mechanics has not been identified. This lack of understanding inhibits the development of more accurate models and therapies. To address this research gap, we used a mouse model of the disease (oim) to measure these outcomes together in order to propose an underlying mechanism for the changes in properties. Our main finding was that despite increased mineralization, oim bones have lower stiffness that may result from the poorly organized mineral matrix with significantly smaller, highly packed and disoriented apatite crystals. Using a composite framework, we interpret the lower oim bone matrix elasticity observed as the result of a change in the aspect ratio of apatite crystals and a disruption of the crystal connectivity.

Separating poroviscoelastic deformation mechanisms in hydrogels

Hydrogels have applications in drug delivery, mechanical actuation, and regenerative medicine. When hydrogels are deformed, load-relaxation arising from fluid flow - poroelasticity - and from rearrangement of the polymer network - viscoelasticity - is observed. The physical mechanisms are different in that poroelastic relaxation varies with experimental length-scale while viscoelastic does not. Here, we show that poroviscoelastic load-relaxation is the product of the two individual responses. The difference in length-scale dependence of the two mechanisms can be exploited to uniquely determine poroviscoelastic properties from simultaneous analysis of multi-scale indentation experiments, providing insight into hydrogel physical behavior. © 2013 American Institute of Physics.

小波多尺度跟踪技术研究

本论文研究的主要内容为基于小波多尺度特性的序列图像目标跟踪技术。目标跟踪作为一个在军事、工业和科学研究方面有着广泛应用背景的研究领域，一直以来吸引了大批国内外学者。由于小波变换具有多分辨率分析的特点，而且在时频两域都具有表征信号局部特征的能力，使得基于小波变换的目标跟踪算法具有传统算法无法比拟的优势。针对目标跟踪技术的研究现状和存在问题，本文着重从目标分割和特征检测与匹配两个角度对基于小波变换的几种新的目标跟踪方法进行了研究。 1. 采用基于多尺度Gabor小波的特征点检测算法对序列图像进行跟踪。借助图像的金字塔变换得到多尺度的Gabor小波特征图像，并对特征图像进行特征点检测，提取对图像变换具有鲁棒性的特征。针对两种特征检测方案，提出不同的特征匹配准则，按照分层匹配的策略由粗到精逐步定位目标的准确位置，具有较快的搜索速度。 2. 采用多尺度小波函数所提取的相位一致性特征进行基于目标分割和基于角点特征的跟踪。 对目标图像进行相位一致性检测，得到一个具有光照不变性的无量纲特征量—相位一致系数。利用相位一致性检测的这种特性，针对孤立目标的情况，提出了两种自适应目标分割和跟踪的算法。基于区域增长的目标分割算法利用从相位一致图像中找到的对比度最大点及其法线方向两边的灰度分布确定目标和背景的种子像素，进行自适应目标分割。基于相位一致性检测的目标分割算法只需确定一个阈值即可利用相位一致特征图像的方向性，依据目标在不同方向响应的不同将目标和背景区分开，适应于复杂纹理背景中的目标分割。最后，分别将两种算法所得的分割结果向水平和垂直方向投影即可确定各自的质心位置，实现自适应的质心跟踪。 进一步提取相位一致性图像的最小矩特征就能得到目标的角点信息。文中用实验验证了此方法检测到角点的综合性能。在此基础上，提出了利用单演相位差进行角点匹配跟踪的算法，并将其同基于灰度相关的匹配算法进行了对比，证明了本算法能够检测出更多准确匹配的角点、减少误匹配，同时具有较小的匹配运算量。 对以上提出的几种目标跟踪算法进行了大量的仿真实验，实验结果表明，这几种方法均取得了较好的跟踪效果，能够实现稳定、精确的跟踪。

基于多特征模糊聚类的图像融合方法

首先利用模糊C-均值聚类算法在多特征形成的特征空间上对图像进行区域分割,并在此基础上对区域进行多尺度小波分解;然后利用柯西函数构造区域的模糊相似度,应用模糊相似度及区域信息量构造加权因子,从而得到融合图像的小波系数;最后利用小波逆变换得到融合图像·采用均方根误差、峰值信噪比、熵、交叉熵和互信息5种准则评价融合算法的性能·实验结果表明,文中方法具有良好的融合特性·

地震岩性与物性联合反演技术研究和应用

This paper studies how to more effectively invert seismic data and predict reservoir under complicated sedimentary environment, complex rock physical relationships and fewer drills in offshore areas of China. Based on rock physical and seismic amplitude-preserving process, and according to depositional system and laws of hydrocarbon reservoir, in the light of feature of seismic inversion methods present applied, series methods were studied. A joint inversion technology for complex geological condition had been presented, at the same time the process and method system for reservoir prediction had been established. This method consists four key parts. 1)We presented the new conception called generalized wave impedance, established corresponding inversion process, and provided technical means for joint inversion lithology and petrophysical on complex geological condition. 2)At the aspect of high-resolution nonlinear seismic wave impedance joint inversion, this method used a multistage nonlinear seismic convolution model rather than conventional primary structure Robinson seismic convolution model, and used Caianiello neural network implement inversion. Based on the definition of multistage positive and negative wavelet, it adopted both deterministic and statistical physical mechanism, direct inversion and indirect inversion. It integrated geological knowledge, rock physical theory, well data, and seismic data, and improved the resolution and anti-noise ability of wave impedence inversion. 3)At the aspect of high-resolution nonlinear reservoir physical property joint inversion, this method used nonlinear rock physical model which introduced convolution model into the relationship between wave impedance and porosity/clay. Through multistage decomposition, it handles separately the large- and small-scale components of the impedance-porosity/clay relationships to achieve more accurate rock physical relationships. By means of bidirectional edge detection with wavelets, it uses the Caianiello neural network to finish statistical inversion with combined applications of model-based and deconvolution-based methods. The resulted joint inversion scheme can integrate seismic data, well data, rock physical theory, and geological knowledge for estimation of high-resolution petrophysical parameters. 4)At the aspect of risk assessment of lateral reservoir prediction, this method integrated the seismic lithology identification, petrophysical prediction, multi-scale decomposition of petrophysical parameters, P- and H-spectra, and the match relationship of data got from seismics, well logging and geology. It could describe the complexity of medium preferably. Through applications of the joint inversion of seismic data for lithologic and petrophysical parameters in several selected target areas, the resulted high-resolution lithologic and petrophysical sections(impedance, porosity, clay) show that the joint inversion can significantly improve the spatial description of reservoirs in data sets involving complex deposits. It proved the validity and practicality of this method adequately.

多尺度分解与相位反褶积提高海上地震资料分辨率和信噪比

Offshore seismic exploration is full of high investment and risk. And there are many problems, such as multiple. The technology of high resolution and high S/N ratio on marine seismic data processing is becoming an important project. In this paper, the technology of multi-scale decomposition on both prestack and poststack seismic data based on wavelet and Hilbert-Huang transform and the theory of phase deconvolution is proposed by analysis of marine seismic exploration, investigation and study of literatures, and integration of current mainstream and emerging technology. Related algorithms are studied. The Pyramid algorithm of decomposition and reconstruction had been given by the Mallat algorithm of discrete wavelet transform In this paper, it is introduced into seismic data processing, the validity is shown by test with field data. The main idea of Hilbert-Huang transform is the empirical mode decomposition with which any complicated data set can be decomposed into a finite and often small number of intrinsic mode functions that admit well-behaved Hilbert transform. After the decomposition, a analytical signal is constructed by Hilbert transform, from which the instantaneous frequency and amplitude can be obtained. And then, Hilbert spectrum. This decomposition method is adaptive and highly efficient. Since the decomposition is based on the local characteristics of the time scale of data, it is applicable to nonlinear and non-stationary processes. The phenomenons of fitting overshoot and undershoot and end swings are analyzed in Hilbert-Huang transform. And these phenomenons are eliminated by effective method which is studied in the paper. The technology of multi-scale decomposition on both prestack and poststack seismic data can realize the amplitude preserved processing, enhance the seismic data resolution greatly, and overcome the problem that different frequency components can not restore amplitude properly uniformly in the conventional method. The method of phase deconvolution, which has overcome the minimum phase limitation in traditional deconvolution, approached the base fact well that the seismic wavelet is phase mixed in practical application. And a more reliable result will be given by this method. In the applied research, the high resolution relative amplitude preserved processing result has been obtained by careful analysis and research with the application of the methods mentioned above in seismic data processing in four different target areas of China Sea. Finally, a set of processing flow and method system was formed in the paper, which has been carried on in the application in the actual production process and has made the good progress and the huge economic benefit.

基于随机介质理论的多尺度储层非均质性定量描述

Application of long-term exploration for oil and gas shows that the reservoir technology of prediction is one of the most valuable methods. Quantitative analysis of reservoir complexity is also a key technology of reservoir prediction. The current reservoir technologies of prediction are based on the linear assumption of various physical relationships. Therefore, these technologies cannot handle complex reservoirs with thin sands, high heterogeneities in lithological composition and strong varieties in petrophysical properties. Based on the above-mentioned complex reservoir, this paper conducts a series of researches. Both the comprehending and the quantitative analysis of reservoir heterogeneities have been implemented using statistical and non-linear theories of geophysics. At the beginning, the research of random media theories about reservoir heterogeneities was researched in this thesis. One-dimensional (1-D) and two-dimensional (2-D) random medium models were constructed. The autocorrelation lengths of random medium described the mean scale of heterogeneous anomaly in horizontal and deep directions, respectively. The characteristic of random medium models were analyzed. We also studied the corresponding relationship between the reservoir heterogeneities and autocorrelation lengths. Because heterogeneity of reservoir has fractal nature, we described heterogeneity of reservoir by fractal theory based on analyzing of the one-dimensional (1-D) and two-dimensional (2-D) random medium models. We simulated two-dimensional (2-D) random fluctuation medium in different parameters. From the simulated results, we can know that the main features of the two-dimensional (2-D) random medium mode. With autocorrelation lengths becoming larger, scales of heterogeneous geologic bodies in models became bigger. In addition, with the autocorrelation lengths becoming very larger, the layer characteristic of the models is very obvious. It would be difficult to identify sandstone such as gritstone, clay, dense sandstone and gas sandstone and so on in the reservoir with traditional impedance inversion. According to the obvious difference between different lithologic and petrophysical impedance, we studied multi-scale reservoir heterogeneities and developed new technologies. The distribution features of reservoir lithological and petrophysical heterogeneities along vertical and transverse directions were described quantitatively using multi-scale power spectrum and heterogeneity spectrum methods in this paper. Power spectrum (P spectrum) describes the manner of the vertical distribution of reservoir lithologic and petrophysical parameters and the large-scale and small-scale heterogeneities along vertical direction. Heterogeneity spectrum (H spectrum) describes the structure of the reservoir lithologic and petrophysical parameters mainly, that is to say, proportional composition of each lithological and petrophysical heterogeneities are calculated in this formation. The method is more reasonable to describe the degree of transverse multi-scale heterogeneities in reservoir lithological and petrophysical parameters. Using information of sonic logs in Sulige oil field, two spectral methods have been applied to the oil field, and good analytic results have been obtained. In order to contrast the former researches, the last part is the multi-scale character analysis of reservoir based on the transmission character of wave using the wavelet transform. We discussed the method applied to demarcate sequence stratigraphy and also analyzed the reservoir interlayer heterogeneity.

尺度匹配方法在井震联合重建和多次波衰减中的应用

Scale matching method means adjusting information with different scale to the same level. This thesis focuses on scale unification of information with different frequency bandwidth. Well-seismic cooperate inversion is an important component of reservoir geophysics; multiple prediction & subtraction is a development of multiple attenuation in recent years. The common ground of these two methods is that they both related to different frequency bandwidth unification. Well log、cross-hole seismic、VSP、3D seismic and geological information have different spatial resolution, we can decrease multi-solution of reservoir inversion and enhance the vertical and lateral resolution of the geological object by integrate those information together; Compare the predicted multiple generated by SRME with the real multiple, we find the predicted multiple convolutes at least one wavelet more, which brings frequency bandwidth difference between them. So the subtraction method also relates to multi-scale information unification. This thesis gives a method of well constrained seismic high resolution processing basing on auto gain control modulation. It uses base function method which utilizes original well-seismic match result as initial condition and processed seismic trace as initial model to extrapolate the high frequency information of the well logs to the seismic profiles. In this way we can broaden the bandwidth of the seismic and make the high frequency gain geological meaning. In this thesis we introduce the revised base function method to adaptive subtraction and verify the validity of the method using models. Key words: high frequency reconstruction, scale matching, base function, multiple, SRME prediction & subtraction

HHT在电磁信号处理中的应用

In modem signal Processing，non－linear，non－Gaussian and non－stable signals are usually the analyzed and Processed objects，especially non－stable signals. The convention always to analyze and Process non－stable signals are： short time Fourier transform，Wigner－Ville distribution，wavelet Transform and so on. But the above three algorithms are all based on Fourier Transform，so they all have the shortcoming of Fourier Analysis and cannot get rid of the localization of it. Hilbert－Huang Transform is a new non－stable signal processing technology，proposed by N. E. Huang in 1998. It is composed of Empirical Mode Decomposition (referred to as EMD) and Hilbert Spectral Analysis (referred to as HSA). After EMD Processing，any non－stable signal will be decomposed to a series of data sequences with different scales. Each sequence is called an Intrinsic Mode Function (referred to as IMF). And then the energy distribution plots of the original non－stable signal can be found by summing all the Hilbert spectrums of each IMF. In essence，this algorithm makes the non－stable signals become stable and decomposes the fluctuations and tendencies of different scales by degrees and at last describes the frequency components with instantaneous frequency and energy instead of the total frequency and energy in Fourier Spectral Analysis. In this case，the shortcoming of using many fake harmonic waves to describe non－linear and non－stable signals in Fourier Transform can be avoided. This Paper researches in the following parts： Firstly，This paper introduce the history and development of HHT，subsequently the characters and main issues of HHT. This paper briefly introduced the basic realization principles and algorithms of Hilbert－Huang transformation and confirms its validity by simulations. Secondly, This paper discuss on some shortcoming of HHT. By using FFT interpolation, we solve the problem of IMF instability and instantaneous frequency undulate which are caused by the insufficiency of sampling rate. As to the bound effect caused by the limitation of envelop algorithm of HHT, we use the wave characteristic matching method, and have good result. Thirdly, This paper do some deeply research on the application of HHT in electromagnetism signals processing. Based on the analysis of actual data examples, we discussed its application in electromagnetism signals processing and noise suppression. Using empirical mode decomposition method and multi-scale filter characteristics can effectively analyze the noise distribution of electromagnetism signal and suppress interference processing and information interpretability. It has been founded that selecting electromagnetism signal sessions using Hilbert time-frequency energy spectrum is helpful to improve signal quality and enhance the quality of data.

Local Rotational Symmetries

This thesis describes a new representation for two-dimensional round regions called Local Rotational Symmetries. Local Rotational Symmetries are intended as a companion to Brady's Smoothed Local Symmetry Representation for elongated shapes. An algorithm for computing Local Rotational Symmetry representations at multiple scales of resolution has been implemented and results of this implementation are presented. These results suggest that Local Rotational Symmetries provide a more robustly computable and perceptually accurate description of round regions than previous proposed representations. In the course of developing this representation, it has been necessary to modify the way both Smoothed Local Symmetries and Local Rotational Symmetries are computed. First, grey-scale image smoothing proves to be better than boundary smoothing for creating representations at multiple scales of resolution, because it is more robust and it allows qualitative changes in representations between scales. Secondly, it is proposed that shape representations at different scales of resolution be explicitly related, so that information can be passed between scales and computation at each scale can be kept local. Such a model for multi-scale computation is desirable both to allow efficient computation and to accurately model human perceptions.

MosaicShape: Stochastic Region Grouping with Shape Prior

A novel method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework and solved by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. By this means, object segmentation and recognition are accomplished simultaneously. Within each sampling move during the simulation process,probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized parallel algorithm by Barbu and Zhu,combined with a large sampling jump and other implementation improvements, greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.

CONFIGR: A Vision-Based Model for Long-Range Figure Completion

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the “early vision” stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.

Women's help seeking behaviour and the associated influencing factors on self discovery of a breast symptom

To investigate women’s help seeking behavior (HSB) following self discovery of a breast symptom and determine the associated influencing factors. A descriptive correlation design was used to ascertain the help seeking behavior (HSB) and the associated influencing factors of a sample of women (n = 449) with self discovered breast symptoms. The study was guided by the ‘Help Seeking Behaviour and Influencing Factors” conceptual framework (Facione et al., 2002; Meechan et al., 2003, 2002; Leventhal, Brissette and Leventhal, 2003 and O’Mahony and Hegarty, 2009b). Data was collected using a researcher developed multi-scale questionnaire package to ascertain women’s help seeking behavior on self discovery of a breast symptom and determine the factors most associated with HSB. Factors examined include: socio-demographics, knowledge and beliefs (regarding breast symptom; breast changes associated with breast cancer; use of alternative help seeking behaviours and presence or absence of a family history of breast cancer),emotional responses, social factors, health seeking habits and health service system utilization and help seeking behavior. A convenience sample (n = 449 was obtained by the researcher from amongst women attending the breast clinics of two large urban hospitals within the Republic of Ireland. All participants had self-discovered breast symptoms and no previous history of breast cancer. The study identified that while the majority of women (69.9%; n=314) sought help within one month, 30.1% (n=135) delayed help seeking for more than one month following self discovery of their breast symptom. The factors most significantly associated with HSB were the presenting symptom of ‘nipple indrawn/changes’ (p = 0.005), ‘ignoring the symptom and hoping it would go away’ (p < 0.001), the emotional response of being ‘afraid@ on symptom discovery (p = 0.005) and the perception/belief in longer symptom duration (p = 0.023). It was found that women who presented with an indrawn/changed nipple were more likely to delay (OR = 4.81) as were women who ‘ignored the symptoms and hoped it would go away’ (OR = 10.717). Additionally, the longer women perceived that their symptom would last, they more likely they were to delay (OR = 1.18). Conversely, being afraid following symptom discovery was associated with less delay (OR = 0.37; p=0.005). This study provides further insight into the HSB of women who self discovered breast symptoms. It highlights the complexity of the help seeking process, indicating that is not a linear event but is influenced by multiple factors which can have a significant impact on the outcomes in terms of whether women delay or seek help promptly. The study further demonstrates that delayed HSB persists amongst women with self discovered breast symptoms. This has important implications for continued emphasis on the promotion of breast awareness, prompt help seeking for self discovered breast symptoms and early detection and treatment of breast cancer, amongst women of all ages.