利用局部图关联信息挖掘加权频繁模式*

遍历模式数据挖掘方法已经在多种应用中被提出,传统的遍历模式挖掘仅仅考虑了非加权遍历。为解决加权遍历模式挖掘问题,首先提出了一种从EWDG（边加权有向图）到VWDG（顶点加权有向图）的变换模型;基于这种模型,提出了在具有层次特性的局部图遍历中,挖掘加权频繁模式的LGTWFPMiner(局部图遍历加权频繁模式挖掘法)及其支持度/权值界的局部评估方法。针对合成数据的实验结果表明该算法能够有效地进行基于图遍历的加权频繁模式挖掘。     

结合纯像元提取和ICA的高光谱降维方法*

摘要：为了实现高光谱降维并保留重要的光谱特征,通过独立分量分析（Independent Component Analysis, ICA）混合模型和高光谱线性模型的对比分析,提出了结合纯像元提取和ICA的高光谱数据降维方法。该方法通过估计虚拟维数(Virtual Dimensionality, VD)确定特征个数,采用自动目标生成过程（Automatic Target Generation Process, ATGP）从原始数据中提取纯像元向量,作为ICA算法的初始化向量,以负熵为目标函数产生独立分量,并通过高阶统计量筛选实现高光谱数据的降维。分类实验结果表明,该方法不仅解决了传统ICA的随机排序问题,而且与经典降维算法主分量分析（Principal Components Analysis, PCA）相比,分类精度提高了6.83%,在大大降低高光谱数据量的情况下很好的保留了高光谱数据的特征,有利于数据的后续分析和应用。     

基于SWT区域对比度像素提取的多聚焦图像融合

在多聚焦图像融合算法中,针对多分辨率系数融合法无法提取源图像清晰像素点和分块法存在的块效应的现象,从多聚焦图像清晰像素点和人眼视觉对比度的特征出发,利用平稳小波变换(SWT)的非下采样性和平移不变性,定义基于SWT的图像像素点区域对比度作为提取像素点的依据,在研究区域对比度邻域大小对像素点提取影响的基础上,设定适合的阈值建立提取模板,对多聚焦源图像中清楚区域的像素点进行提取,并对小部分未能提取的像素位置采用基于局部能量策略进行融合。仿真实验结果表明,新算法既有效地提取源图像的清晰像素点,又改善了块效应现象,融合效果有了很大提升。     

压缩金字塔树:有效的高维数据索引结构

提出了一种压缩金字塔树,基本思想是,首先将d维数据空间划分为2d个金字塔,由于在低维空间中无效的信息在高维数据空间中往往无效,采用γ划分策略对低维空间中的数据进行压缩,减小索引结构,解决了金字塔技术的缺点,给出了压缩金字塔树的插入、查询、删除算法。最后经实验证明,压缩金字塔树是一种有效的空间划分策略,在高维稀疏空间有良好的性能。     

Depth from focus using a pyramid architecture

A method is presented for depth recovery through the analysis of scene sharpness across changing focus position. Modeling a defocused image as the application of a low pass filter on a properly focused image of the same scene, we can compare the high spatial frequency content of regions in each image and determine the correct focus position. Recovering depth in this manner is inherently a local operation, and can be done efficiently using a pipelined image processor. Laplacian and Gaussian pyramids are used to calculate sharpness maps which are collected and compared to find the focus position that maximizes high spatial frequencies for each region.     

Image segmentation using a dynamic thresholding pyramid

An image segmentation algorithm based on multi-resolution processing is presented. The algorithm is based on applying a local clustering at each level of a linked pyramid data structure allowing seed nodes to be defined. These seed nodes are the root nodes of regions at the base of the pyramid, appearing in the multi-resolution data structure at a level appropriate to the region size. By applying a merging process followed by a classification step, accurate segmentations are obtained for both natural and synthetic images without the need for a priori knowledge. Results show that the algorithm gives accurate segmentations even in low signal to noise ratios.     

唇读中基于像素的特征提取方法的研究

针对单独视觉通道唇读中的基于像素的特征提取问题,提出一个级联的特征提取策略。首先对图像采用相应的变换,然后对变换结果降维,最后进行特征归一化。基于对几种变换方法的比较与分析,提出利用PCA对DCT和Gabor小波变换结果降维的DCT-PCA和Gabor-PCA方法,与传统人工选择变换系数的方法相比识别率提高了约10%。     

足部标记图像中标尺提取与像素测量

测量足部关节受力后的位移大小,进而研究足部关节的运动特性,需要计算足部标记图像中每个标记点的位移。实验采集图像中,黑色标记点和矩形区域分别为探测电极和标尺。实现了足部标记图像中标尺的自动提取与像素测量,根据最小二乘直线拟合思想,提取标尺的边界直线,并求其下方两端点的像素距离,然后利用标尺长度求出每个像素所代表的实际长度;实现了像素距离与标尺长度的自动转换,为足部疾病的临床诊断与治疗提供服务。     

Image classification without segmentation using a hybrid pyramid kernel

Image classification usually requires complicated segmentation to separate foreground objects from the background scene. However, the statistical content of a background scene can actually provide very useful information for classification. In this paper, we propose a new hybrid pyramid kernel which incorporates local features extracted from both dense regular grids and interest points for image classification, without requiring segmentation. Features extracted from dense regular grids can better capture information about the background scene, while interest points detected at corners and edges can better capture information about the salient objects. In our algorithm, these two local features are combined in both the spatial and the feature-space domains, and are organized into pyramid representations. In order to obtain better classification accuracy, we fine-tune the parameters involved in the similarity measure, and we determine discriminative regions by means of relevance feedback. From the experimental results, we observe that our algorithm can achieve a 6.37 % increase in performance as compared to other pyramid-representation-based methods. To evaluate the applicability of the proposed hybrid kernel to large-scale databases, we have performed a cross-dataset experiment and investigated the effect of foreground/background features on each of the kernels. In particular, the proposed hybrid kernel has been proven to satisfy Mercer’s condition and is efficient in measuring the similarity between image features. For instance, the computational complexity of the proposed hybrid kernel is proportional to the number of features.     

Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure

The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches’ reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structure (RSRC-ASP) is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals’ aggregation. In addition, three strategies, local-neighbourhood representation, local intra-class Bayesian residual criterion, and local auxiliary dictionary, are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.     

A weighted normalized difference water index for water extraction using Landsat imagery

Water extraction is one of challenging topics in studies on remote-sensing applications. Spectral profiles and experiments indicate that existing water indices often misclassified turbid water, small waterbodies, and some land features in a shadow area. In this study, a new water index called weighted normalized difference water index (WNDWI) was proposed to reduce those errors and improve the mapping accuracy of waterbodies by using Landsat imagery. To test the performance of the newly proposed water index, two test sites (Tampa Bay, FL, USA and Xiangshan Harbour, Zhejiang, China) were selected and the performances of three existing water indices including the normalized difference water index (NDWI), the modified NDWI (MNDWI), and the automated water extraction index (AWEI) were compared with that of the WNDWI. In addition, a default threshold 0 and automatically thresholding methods including Otsu threshold method and multiple thresholds identified by valley points in a histogram curve were tested to determine an optimal threshold that can be used to separate water and non-water features from grey images created by the four water indices. The experimental results indicate that the overall accuracies (OAs) created with WNDWI were all higher than those created with the three existing water indices: NDWI, MNDWI, and AWEI in both sites. Moreover, the results thresholded by 0 owned or shared the highest OAs with the results segmented by some of non-zero thresholds obtained from Otsu method and multiple thresholds method. Therefore, using an appropriate threshold, the proposed method could extract waterbodies from Landsat TM imagery with a high accuracy.     

Video retrieval using salient foreground region of motion vector based extracted keyframes and spatial pyramid matching

Multimedia Tools and Applications - Despite enormous research efforts devoted by the research community to effectively and precisely perform video matching and retrieval among heterogeneous videos...     

Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation

The ability to quickly locate one or more instances of a model in a grey scale image is of importance to industry. The recognition/localization must be fast and accurate. In this paper we present an algorithm which incorporates normalized correlation into a pyramid image representation structure to perform fast recognition and localization. The algorithm employs an estimate of the gradient of the correlation surface to perform a steepest descent search. Test results are given detailing search time by target size, effect of rotation and scale changes on performance, and accuracy of the subpixel localization algorithm used in the algorithm. Finally, results are given for searches on real images with perspective distortion and the addition of Gaussian noise.     

结合高斯加权距离图的图像边缘提取

边缘是图像最为重要的特征之一,是图象分析与识别的基础。对于目标的分割、测量而言,边缘提取的连续性与抗噪性显得尤为重要,其可通过区域增长等算法提取目标区域,为抠图、统计测量提供必要的支持,本文以实现目标轮廓的有效提取为目的,提出一种结合高斯加权距离图的图像边缘提取方法。首先通过计算分块区域内像素间的高斯加权距离,获得高斯加权距离图,该图与原图相比,不仅可以较好地突出边缘轮廓,而且可以统一背景灰度。其次通过分析高斯加权距离图的灰度直方图,将灰度分为两类并计算类中心,以此作为无边缘活动轮廓（CV）模型的c1和c2参数,最后通过CV模型求解图像边缘。与其它边缘提取算法相比,该算法不仅具有较好的抗噪性,同时可以保证图像边缘提取的连续性,实验结果验证了本文算法的有效性。     

Robust foreground detection in video using pixel layers

A framework for robust foreground detection that works under difficult conditions such as dynamic background and moderately moving camera is presented in this paper. The proposed method includes two main components: coarse scene representation as the union of pixel layers, and foreground detection in video by propagating these layers using a maximum-likelihood assignment. We first cluster into "layers" those pixels that share similar statistics. The entire scene is then modeled as the union of such non-parametric layer-models. An in-coming pixel is detected as foreground if it does not adhere to these adaptive models of the background. A principled way of computing thresholds is used to achieve robust detection performance with a pre-specified number of false alarms. Correlation between pixels in the spatial vicinity is exploited to deal with camera motion without precise registration or optical flow. The proposed technique adapts to changes in the scene, and allows to automatically convert persistent foreground objects to background and re-convert them to foreground when they become interesting. This simple framework addresses the important problem of robust foreground and unusual region detection, at about 10 frames per second on a standard laptop computer. The presentation of the proposed approach is complemented by results on challenging real data and comparisons with other standard techniques.     

Predicting protein secondary structure using a mixed-modal SVM method in a compound pyramid model

Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly improve sequence analysis and sequence-structure threading for structure and function determination. Hence improving the accuracy of secondary structure prediction is essential for future developments throughout the field of protein research.In this article, we propose a mixed-modal support vector machine (SVM) method for predicting protein secondary structure. Using the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-BLAST multiple sequence alignment as input for a mixed-modal SVM, secondary structure can be predicted at significantly increased accuracy. Using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) method, we have proposed a compound pyramid model, which is composed of three layers of intelligent interface that integrate a mixed-modal SVM (MMS) module, a modified Knowledge Discovery in Databases (KDD1) process, a mixed-modal back propagation neural network (MMBP) module and so on.Testing against data sets of non-redundant protein sequences returned values for the Q₃ accuracy measure that ranged from 84.0% to 85.6%,while values for the SOV99 segment overlap measure ranged from 79.8% to 80.6%. When compared using a blind test dataset from the CASP8 meeting against currently available secondary structure prediction methods, our new approach shows superior accuracy.Availability: http://www.kdd.ustb.edu.cn/protein_Web/.     

Improving class separability using extended pixel planes: a comparative study

In this work we explored class separability in feature spaces built on extended representations of pixel planes (EPP) produced using scale pyramid, subband pyramid, and image transforms. The image transforms included Chebyshev, Fourier, wavelets, gradient, and Laplacian; we also utilized transform combinations, including Fourier, Chebyshev, and wavelets of the gradient transform, as well as Fourier of the Laplacian transform. We demonstrate that all three types of EPP promote class separation. We also explored the effect of EPP on suboptimal feature libraries, using only textural features in one case and only Haralick features in another. The effect of EPP was especially clear for these suboptimal libraries, where the transform-based representations were found to increase separability to a greater extent than scale or subband pyramids. EPP can be particularly useful in new applications where optimal features have not yet been developed.     

Compact laparoscopic assistant robot using a bending mechanism

This paper presents the development of a compact laparoscopic assistant robot. The robot was designed to increase convenience and reduce possible interference with surgical staff by confining the majority of motions inside the abdomen. Its size was miniaturized as much as possible for convenient handling. A bending mechanism composed of several articulated joints was introduced to produce motions inside the abdomen. The proposed assistant robot can generate 3-DOF motion, including 2-DOF internal bending motion and 1-DOF external linear motion. Since the robot itself functions as a laparoscope, a small CCD camera module and a bundle of optical fibers were integrated as part of the system. For accurate control, mathematical modeling of the bending mechanism and a method of hysteresis compensation were introduced and implemented. For the control of the robot, a voice interface and a visual-servoing method were implemented. The performance of the developed system was tested through solo-surgery of in vivo porcine cholecystectomy. It was found that the views generated by the bending mechanism were sufficient throughout the surgery. Since the robot has functions comparable to the previously developed systems, while retaining its compactness, it is expected to be a useful device for human cholecystectomy.     

加权局部二值模式的人脸特征提取

目的 为了能够得到图像更加丰富的纹理特征,提出一种新的自适应加权局部二值模式算法。方法 首先,将图像进行分块,利用新算法提取每个子块的局部二值模式的纹理直方图;然后,将各子图像的信息熵作为直方图的加权依据,对每个子块对应的直方图进行自适应加权,并将所有子块的直方图连接成最终的纹理特征。提取每个子块的局部纹理特征时的方法为:以某一像素点为中心取相邻的8个像素组成一个局部邻域,在该邻域内依据自适应设定的阈值分别比较3对水平方向和3对竖直方向像素值的大小,以此获得6位二进制码并将每位二进制码乘以相应的权重后相加,累加和即为该邻域新的局部二值模式纹理特征。结果 在两大人脸数据库上进行的实验结果表明,利用本文提出的方法提取纹理特征,并结合最近邻分类法可以得到85.29%和96.50%的正确识别率。结论 文中提出的自适应加权局部二值模式特征能够获取图像中更加丰富的纹理信息,因而具有较高的正确识别率,并且对于其他的物体识别也具有一定的参考价值。     

Torque distribution using a weighted pseudoinverse in a redundantly actuated mechanism

When mobility, the number of independent variables to describe system motion exactly, is greater than the degree-of-freedom of task space, the system is called a kinematically redundant system. On the other hand, redundant actuation indicates a situation when there are more actuators than a system's mobility. Redundant actuation yields many advantages. First, actuation redundancy can increase the force, velocity and acceleration of an end-effector. Second, if some actuators are out of order, the system can still work well. This fault-tolerant capability is useful for remote control robots in space or nuclear plants. Impulsive force can decrease when modulating arbitrary stiffness without feedback control. The performance of a system can be improved by optimizing redundancy. However, there are some issues of economic efficiency and minimization of a system, because redundant actuation may involve more actuators than non-redundant actuation. In addition, there are infinite torque sets of motors for the same task. We used a weighted pseudoinverse matrix for torque distribution. To reduce the maximum torque, we suggested the use of the minimum norm torque as the weighting values. This method allows for smaller motor capacity, and can contribute to economic efficiency and minimization of a system.