Three-dimensional microfabrication by localized electrochemicaldeposition

A microfabrication technology capable of electrodepositing truly three-dimensional metal structures is introduced. Micrometer-scale nickel structures including a multicoiled helical spring have been fabricated. Electrodeposition is localized by placing a sharp-tipped electrode in a plating solution, near a substrate, and applying a voltage. Structures are built by moving the electrode appropriately with respect to the substrate. Vertical deposition rates of 6 μm/s are observed, two orders of magnitude greater than those of conventional electrodeposition. The theory of mass transport to a region of localized field is discussed, and a model of deposition profile is presented. The process can potentially produce submicrometer feature sizes using a range of materials including pure metals, alloys, and polymers     

Using association rules as texture features

A new type of texture feature based on association rules is proposed in this paper. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. Association rules capture both structural and statistical information, and automatically identifies the structures that occur most frequently and relationships that have significant discriminative power. Methods for classification and segmentation of textured images using association rules as texture features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. It is shown that association rule features can distinguish texture pairs with identical first, second, and third order statistics, and texture pairs that are not easily discriminable visually     

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

In this work, we present an automated method for the detection and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The detection of the candidate nuclei areas is based on a morphological image reconstruction process and the segmentation of the nuclei boundaries is accomplished with the application of the watershed transform in the morphological color gradient image, using the nuclei markers extracted in the detection step. For the elimination of false positive findings, salient features characterizing the shape, the texture and the image intensity are extracted from the candidate nuclei regions and a classification step is performed to determine the true nuclei. We have examined the performance of two unsupervised (K-means, spectral clustering) and a supervised (Support Vector Machines, SVM) classification technique, employing discriminative features which were selected with a feature selection scheme based on the minimal-Redundancy-Maximal-Relevance criterion. The proposed method was evaluated on a data set of 90 Pap smear images containing 10,248 recognized cell nuclei. Comparisons with the segmentation results of a gradient vector flow deformable (GVF) model and a region based active contour model (ACM) are performed, which indicate that the proposed method produces more accurate nuclei boundaries that are closer to the ground truth.     

基于多特征的图像检索研究

为了有效地检索图像,基于内容的图像检索技术应运而生。利用直方图表达图像的颜色特征,利用Gabor滤波器组提取图像的纹理特征,在此基础上又提出综合利用颜色特征和纹理特征共同进行图像检索的方法。实验结果表明,综合特征检索的效果比单一特征检索的效果好。     

Classification of SAR images using morphological texture features

This paper presents applications of Synthetic Aperture Radar (SAR) image classification using morphological texture features. The texture features are based on morphological residues of opening and closing by reconstruction. It is shown that this set of features shows high 'robustness' to speckle perturbation in SAR images compared with those derived from traditionalmorphological residues. An algorithm based on estimating the divergence between and within classes was constructed in order to search for a discriminating feature subset. Higher classification accuracy was obtained by the optimized feature subset than by using other feature subsets derived from some well known texture characterization approaches. The classification accuracy was continuously improved by the introduction of post-processing filtering.     

Surface curvature as a measure of image texture

Methods from the theory of surfaces in differential geometry are applied to the gray tone intensity and absorbance surfaces defined by a digital image. In particular, the first fundamental form, the second fundamental form, the two principal curvatures, the Gaussian curvature, and mean curvature from classical differential geometry are presented. New curvatures are introduced which appear to be more appropriate for the purposes of image texture. For each curvature, a texture feature is generated. These features are applied to biological cell nuclei and it is found that they are useful for purposes of discrimination.     

基于统计模型的遗传粒子滤波器人体运动跟踪

提出了一种基于统计模型的遗传粒子滤波器人体运动跟踪算法。引入局域二值模式(LBP)算子提取纹理特征,利用颜色直方图与纹理直方图相似度的加权和表示目标相似度,以有效解决自遮挡对跟踪的影响。利用该统计模型精确表示运动人体轮廓,目标形状可由一可变形状参数确定;采用遗传粒子滤波器作为跟踪算法以提高粒子滤波器的鲁棒性和精度。通过预测更新可变形状参数,再利用统计模型中目标形状与形状可变参数的关系得到图像序列各帧中人体轮廓,有效降低了计算量,从而达到快速而准确的跟踪目的。最后用上述方法进行了实验,验证了该方法的实用性和有效性。     

一种纹理特征分析与合成的方法

针对纹理合成中基本的 MRF 类模型在大尺度结构表达上的缺陷,提出了在纹理模式空间和素材空间内对纹元及其分布的新的描述框架:使用纹元基本形和特征向量表示纹元,使用特征纹理表示纹元分布状况.在此结构上进行素材合成,可以保留 MRF 类模型在素材合成上的优势.实验结果表明,对于结构性较强的纹理,文中方法可以得到更加接近纹理机制的合成结果和某些纹理合成特效.     

Bayesian texture classification and retrieval based on multiscale feature vector

This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.     

基于GPU加速的保结构纹理合成

提出一种基于Chamfer距离的保结构纹理合成方法。使用Chamfer距离度量纹理结构特征的相似度,在查找匹配块的同时计算纹理在颜色空间和结构特征空间的匹配度,从而解决以往纹理合成中有显著结构特征的纹理容易出现不连续的问题。但是Chamfer距离的计算量很大,而且随着纹理合成图分辨率的提高,计算成本会变得相当高昂以至于难以负担。因此,提出了基于GPU加速的保结构纹理合成方法,通过并行查找匹配块提高合成效率。实验证明本方法既提高了结构纹理的合成质量,又使得保结构纹理方法的合成速度大大提高,且与纹理合成图的分辨率无关。     

Color texture segmentation based on image pixel classification

Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Thus, image segmentation plays an important role in many multimedia applications. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. By combination of Fuzzy Support Vector Machine (FSVM) and Fuzzy C-Means (FCM), a color texture segmentation based on image pixel classification is proposed in this paper. Specifically, we first extract the pixel-level color feature and texture feature of the image via the local spatial similarity measure model and localized Fourier transform, which is used as input of FSVM model (classifier). We then train the FSVM model (classifier) by using FCM with the extracted pixel-level features. Color image segmentation can be then performed through the trained FSVM model (classifier). Compared with three other segmentation algorithms, the results show that the proposed algorithm is more effective in color image segmentation.     

Adaptive lifting for shape-based image retrieval

We propose to use adaptive wavelet lifting for image retrieval systems that are based on shape detection and multiresolution structures of objects in a database against a background of texture. To measure the performance of our approach, feature vectors are computed based on moment invariants of detail coefficients produced by the adaptive lifting scheme and retrieval rates are obtained by measuring distances between these vectors. Retrieval rates are compared with the rates obtained when using non-adaptive wavelet filtering as a preprocessing step. A synthetic database is created for this simulation.     

基于互补特征的纹理图像检索

针对互补特征可以有效地改善图像检索系统性能的特点,提出一种在改进Contourlet变换域采用L1能量与广义高斯分布参数特征的纹理图像检索方法。首先,应用改进的方法对方向子带系数进行广义高斯统计建模。然后,分别单独利用各个特征和相应的相似性测度进行检索。最后,基于直接的相似性测度和,采用这两种互补的特征进行检索。实验结果表明,和采用单一特征相比较,互补特征由于充分地反映了图像的结构信息和随机分布信息,从而有效地提高了纹理图像数据库的平均检索率。     

结合视觉注意和纹理特征提取感兴趣区域算法

针对图像底层特征特别是纹理特征对提取感兴趣区域(region of interest,ROI)影响程度的问题,利用眼动实验数据得到图像的ROIeye和最佳权重w,提出了一个结合视觉注意和纹理特征提取的ROI算法.该算法首先提取纹理特征并归一化特征关注图,然后计算图像在w下的显著图,通过二值化和形态学操作提取图像的特征ROI.采用相关性分析,分析纹理特征对ROI提取的影响.实验结果表明该算法的总体效果良好,特别是对于目标对象纹理信息较丰富的图像,能准确地提取图中ROI.     

ASM及其改进的人脸面部特征定位算法

为了提高主动形状模型(ASM)算法的性能,提出一种改进的ASM算法.首先,精确定位出瞳孔的位置用作平均形状模型的初始化;其次,采用全局形状模型、面部显著特征区域成分形状模型以及人脸面部的相似性构形相结合的办法来共同约束特征点的定位结果;最后,特征点周围采用Log-Gabor小波系数进行描述,并建立局部纹理模型,提高了算法对光照和噪声的鲁棒性.实验结果表明,与传统的ASM算法相比,该算法特征点定位精确度有显著的提高.     

小波域多尺度ARMA模型的纹理分割方法

纹理特征的提取是纹理分割的关键。在小波域内,对图像建立多尺度自回归滑动平均(MultiscaleAutoregressiveMovingAverage,简称MARMA)模型,并用小生境遗传算法对MARMA模型进行参数估计,将估计出的参数向量作为纹理分割的特征标准。不同纹理图像的MARMA模型的参数向量具有较好的可分性,这就保证了最终纹理分割的质量。     

Global Feature Tracking and Similarity Estimation in Time‐Dependent Scalar Fields

We present an algorithm for tracking regions in time‐dependent scalar fields that uses global knowledge from all time steps for determining the tracks. The regions are defined using merge trees, thereby representing a hierarchical segmentation of the data in each time step. The similarity of regions of two consecutive time steps is measured using their volumetric overlap and a histogram difference. The main ingredient of our method is a directed acyclic graph that records all relevant similarity information as follows: the regions of all time steps are the nodes of the graph, the edges represent possible short feature tracks between consecutive time steps, and the edge weights are given by the similarity of the connected regions. We compute a feature track as the global solution of a shortest path problem in the graph. We use these results to steer the – to the best of our knowledge – first algorithm for spatio‐temporal feature similarity estimation. Our algorithm works for 2D and 3D time‐dependent scalar fields. We compare our results to previous work, showcase its robustness to noise, and exemplify its utility using several real‐world data sets.     

基于遗传算法的海底沉积物纹理特征优化方法

为了提高水下机器人对海底沉积物的自主分类感知能力,解决特征冗余问题,对利用遗传算法优化海底沉积物纹理特征进行了研究。以基于灰度共生矩阵和分形理论提取多种海底沉积物视觉纹理特征实现海底沉积物分类识别为背景,提出利用遗传算法对纹理特征项进行优化选择以实现对提取特征的降维,并将降维后的特征项作为自组织映射神经网络模型的输入,对海底沉积物进行视觉分类,提高水下机器人作业时的环境感知能力。实验结果表明,相对于未优化的纹理特征,优化后的纹理特征在海底沉积物分类识别中具有更优的分类效果。