Optimal band selection from hyperspectral data for Lantana camara discrimination

The primary objective of this research was to determine the optimal hyperspectral wavelengths based on spectroscopy data over the spectral range of 450–2500 nm for the detection of the invasive species Lantana camara L. (lantana) from seven of its co-occurring species. A procedure based on statistical analysis of the reflectance and the first derivative reflectance (FDR) identified 86 and 18 bands, respectively, where lantana significantly differed from its co-occurring species. The effectiveness of the identified optimal bands was then evaluated using Hyperion imagery. The original Hyperion image with 155 bands gave an overall accuracy of 80% compared to 77% and 76% from the 86- and 18-band spectral subsets, respectively. A pairwise comparison of the three error matrices showed no significant difference in the accuracy achieved. The FDR analysis combined with the statistical analysis proved to be a useful procedure for data reduction by refining the discrimination to fewer optimal bands for lantana detection with no adverse impact on classification accuracy.     

Optimal decentralized Kalman filter and Lainiotis filter

A method to implement the optimal decentralized Kalman filter and the optimal decentralized Lainiotis filter is proposed; the method is based on the a priori determination of the optimal distribution of measurements into parallel processors, minimizing the computation time. The resulting optimal Kalman filter and optimal Lainiotis filter require uniform distribution or near to uniform distribution of measurements into parallel processors. The optimal uniform distribution has the advantages of elimination of idle time for the local processors and of low hardware cost, but it is not always applicable. The optimal filters present high parallelism speedup; this is verified through simulation results and is very important due to the fact that, in most real-time applications, it is essential to obtain the estimate in the shortest possible time.     

垃圾邮件处理中LDA特征选择方法

垃圾邮件处理是一项长期研究课题,越来越多的文本分类技术被移植到垃圾邮件处理应用当中。LDA（Latent Dirichlet Allocation）等topic模型在自动摘要、信息获取和其他离散数据应用中受到越来越多的关注。将LDA模型作为一种特征选择方法,引入垃圾邮件处理应用中。将LDA特征选择方法与质心＋KNN分类器结合,得到简单的测试用垃圾邮件过滤器。初步实验结果表明,基于LDA的特征选择方法优于通常的IG、MI特征选择方法;测试过滤器的过滤性能与其他过滤器相当。     

Using eigencolor normalization for illumination-invariant color object recognition

Color is one of salient features for color object recognition, however, the colors of object images sensitively depend on scene illumination. To overcome the lighting dependency problem, a color constancy or color normalization method has to be used. This paper presents a color image normalization method, called eigencolor normalization, which consists of two phases as follows. First, the compacting method, which was originally used for compensating the adverse effect due to shape distortion for 2-D planar objects, is exploited for 3-D color space to make the color distribution less correlated and more compact. Second, the compact color image is further normalized by rotating the histogram to align with the reference axis computed. Consequently, the object colors are transformed into a new color space, called eigencolor space, which reflects the inherent colors of the object and is more invariant to illumination changes. Experimental results show that our eigencolor normalization method is superior to other existing color constancy or color normalization schemes on achieving more accurate color object recognition.     

Feature selection for multiclass discrimination via mixed-integer linear programming

We reformulate branch-and-bound feature selection employing L/sub /spl infin// or particular L/sub p/ metrics, as mixed-integer linear programming (MILP) problems, affording convenience of widely available MILP solvers. These formulations offer direct influence over individual pairwise interclass margins, which is useful for feature selection in multiclass settings.     

Optimal feature selection for support vector machines

Selecting relevant features for support vector machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and feature interpretability. Traditional SVM approaches to feature selection typically extract features and learn SVM parameters independently. Independently performing these two steps might result in a loss of information related to the classification process. This paper proposes a convex energy-based framework to jointly perform feature selection and SVM parameter learning for linear and non-linear kernels. Experiments on various databases show significant reduction of features used while maintaining classification performance.     

Optimal stochastic fault detection filter

A fault detection and identification algorithm, called optimal stochastic fault detection filter, is determined. The objective of the filter is to detect a single fault, called the target fault, and block other faults, called the nuisance faults, in the presence of the process and sensor noises. The filter is derived by maximizing the transmission from the target fault to the projected output error while minimizing the transmission from the nuisance faults. Therefore, the residual is affected primarily by the target fault and minimally by the nuisance faults. The transmission from the process and sensor noises is also minimized so that the filter is robust with respect to these disturbances. It is shown that the filter recovers the geometric structure of the unknown input observer in the limit where the weighting on the nuisance fault transmission goes to infinity. Further, the asymptotic behavior of the filter near the limit is determined by using a perturbation method. Filter designs can be obtained for both time-invariant and time-varying systems.     

Optimal unambiguous discrimination of pure qudits

Linearly independent pure quantum states can be discriminated unambiguously, while linearly dependent states cannot. We use a physical accessible unitary transformation to map the nonorthogonal quantum states onto a set of orthogonal ones so that measuring the output states can discriminate the initial states with the deterministic and inconclusive results. The failure states that give an inconclusive result are linearly dependent ones. In finding the optimal unambiguous discrimination (UD), we show that a main constraint condition that the determinant constructed by the complex inner products of the failure states must be zero, along with two additional conditions, can provide solutions to the problem of the optimal UD for pure qudits. For any d, we give one analytical solution as all the Berry phases being zero. We also derive the lowest bound of the total failure probability of the optimal UD.     

Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition

Recognizing human faces in various lighting conditions is quite a difficult problem. The problem becomes more difficult when face images are taken in extremely high dynamic range scenes. Most of the automatic face recognition systems assume that images are taken under well-controlled illumination. The face segmentation as well as recognition becomes much simpler under such a constrained condition. However, illumination control is not feasible when a surveillance system is installed in any location at will. Without compensating for uneven illumination, it is impossible to get a satisfactory recognition rate. In this paper, we propose an integrated system that first compensates uneven illumination through local contrast enhancement. Then the enhanced images are fed into a robust face recognition system which adaptively selects the most important features among all candidate features and performs classification by support vector machines (SVMs). The dimension of feature space as well as the selected types of features is customized for each hyperplane. Three face image databases, namely Yale, Yale Group B, and Extended Yale Group B, are used to evaluate performance. The experimental result shows that the proposed recognition system give superior results compared to recently published literatures.     

A methodology for rapid illumination-invariant face recognition using image processing filters

Achieving illumination invariance in the presence of large pose changes remains one of the most challenging aspects of automatic face recognition from low resolution imagery. In this paper, we propose a novel recognition methodology for their robust and efficient matching. The framework is based on outputs of simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between two individuals. Specifically, we show how the discrepancy of the illumination conditions between query input and training (gallery) data set can be estimated implicitly and used to weight the contributions of the two competing representations. The weighting parameters are representation-specific (i.e. filter-specific), but not gallery-specific. Thus, the computationally demanding, learning stage of our algorithm is offline-based and needs to be performed only once, making the added online overhead minimal. Finally, we describe an extensive empirical evaluation of the proposed method in both a video and still image-based setup performed on five databases, totalling 333 individuals, over 1660 video sequences and 650 still images, containing extreme variation in illumination, pose and head motion. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50–75% in recognition error rates, the best performing method-filter combination correctly recognizing 97% of the individuals.     

一种优化的空间连接算法

空间数据库中空间连接操作是最重要、最耗时的操作之一,基于BFRJ算法研究了一种对中间连接索引优化排序的空间连接算法OBFRJ,该算法使用广度优先顺序对两棵R树进行同步遍历,对生成的中间连接索引采用了一种空间填充曲线进行排序,使得在下一层的连接时出现页错误的次数减少。实验结果表明,该算法在磁盘访问次数以及CPU代价上都要小于DFRJ和BFRJ算法。     

一种基于图的特征选择方法

在很多的机器学习和数据挖掘任务中,特征子集选择是重要的数据预处理步骤之一。提出一种基于图方法的无监督式特征选择方法（GBFS）,构造一个以样本数据为顶点,数据间相似性作为边的图,再根据各特征的得分优先选择那些具有局部信息保持和全局区分能力的特征。实验结果表明,基于该方法选择的特征子集,在大多数情况下都能取得较好的分类效果。     

Optimal algorithms for extracting spatial regularity in images

Finding spatial regularity in images is important in military applications (e.g., finding rows of landmines), texture analysis, and other areas. We give an optimal Θ(n²) algorithm for finding all maximal equally-spaced collinear subsets within a pointset in E^d. We also generalize this method to yield an optimal Θ(n³) algorithm for determining all maximal regular coplanar lattices.     

Optimal landmark selection for triangulation of robot position

A mobile robot can identify its own position relative to a global environment model by using triangulation based on three landmarks in the environment. It is shown that this procedure may be very sensitive to noise depending on spatial landmark configuration, and relative position between robot and landmarks. A general analysis is presented which permits prediction of the uncertainty in the triangulated position.

In addition an algorithm is presented for automatic selection of optimal landmarks. This algorithm enables a robot to continuously base its position computation on the set of available landmarks, which provides the least noise sensitive position estimate. It is demonstrated that using this algorithm can result in more than one order of magnitude reduction in uncertainty.     

Optimal and interactive keyframe selection for motion capture

Computational Visual Media - Motion capture is increasingly used in games and movies, but often requires editing before it can be used, for many reasons. The motion may need to be adjusted to...     

Optimal rendezvous-point selection for robotic interception ofmoving objects

A number of active prediction planning and execution (APPE) systems have recently been proposed for robotic interception of moving objects. The cornerstone of such systems is the selection of a robot-object rendezvous-point on the predicted object trajectory. Unlike tracking-based systems, which minimize the state difference between the object and the robot at each control period, in this methodology the robot is sent directly to the selected rendezvous-point. A fine-motion tracking strategy would then be employed for grasping the moving object. Herein, a novel strategy for selecting the optimal (earliest) rendezvous-point is presented. For objects with predictable trajectories, this is a significant improvement over previous APPE strategies which select the rendezvous-point from a limited number of non-optimally chosen candidates.     

Optimal recurrent logical-dynamical finite memory filter

We consider the treatment problem of imprecise or incomplete observations of the state of a discrete Markovian stochastic object with a variable random structure. We aim to exactly estimate and forecast in one step this object’s variables of state and structure type. We propose to synthesize a simple finite dimensional switch filter which remembers only the last few measurements in its state vector. The dimension of this vector (the memory volume of the filter) can be chosen from the condition of the compromise between the achievable accuracy of the estimation and the complexity of the hardware implementation of the filter. We gain an impression of the optimal structural functions of the filter through the corresponding probability distributions and propose a numerical algorithm to compute them by the Monte-Carlo method.     

一种形态学滤波结构元的选择方法

形态学滤波作为一种非线性滤波方法在目标提取、抑制噪声等图像处理领域有着广泛的应用。针对形态学滤波中的一个关键问题—结构元的选择,通过对结构元分解和形态学滤波运算的研究,提出了一种自适应选择结构元尺度的二值形态学滤波方法。通过实验表明该方法可以快速、准确地确定滤波中结构元的尺度,具有较强的去噪性和抗干扰性。     

Genetic algorithm for effective open port selection for a web filter

As cyber security is a major challenge in the widespread deployment of the latest technologies, the importance of selecting the open ports for a given web filter cannot be overemphasized. A network administrator would want to select a combination of ports that would be most beneficial to the users and these ports would be treated as least vulnerable. However, this is not a trivial task and can be very time-consuming, O(n!), if brute force or other naïve approaches are used to select a given number of ports from 65,536 ports available. As genetic algorithms (GAs) are commonly used to obtain near-optimal solution for complex and time-consuming tasks, this paper proposes a GA for the selection of open ports for a web filter. The gene value for each port is based on the malicious issues associated with the port and the importance of the port to the client using the web filter. The proposed algorithm is implemented in Java, and the simulation results show that GA is very accurate in identifying open ports for a given web filter.