Optimal randomized RANSAC

A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-specified probability. The solution is found in time that is (i) close to the shortest possible and (ii) superior to any deterministic verification strategy. A provably fastest model verification strategy is designed for the (theoretical) situation when the contamination of data by outliers is known. In this case, the algorithm is the fastest possible (on average) of all randomized \\RANSAC algorithms guaranteeing a confidence in the solution. The derivation of the optimality property is based on Wald's theory of sequential decision making, in particular a modified sequential probability ratio test (SPRT). Next, the R-RANSAC with SPRT algorithm is introduced. The algorithm removes the requirement for a priori knowledge of the fraction of outliers and estimates the quantity online. We show experimentally that on standard test data the method has performance close to the theoretically optimal and is 2 to 10 times faster than standard RANSAC and is up to 4 times faster than previously published methods.     

Temporal difference learning applied to sequential detection

This paper proposes a novel neural-network method for sequential detection, We first examine the optimal parametric sequential probability ratio test (SPRT) and make a simple equivalent transformation of the SPRT that makes it suitable for neural-network architectures. We then discuss how neural networks can learn the SPRT decision functions from observation data and labels. Conventional supervised learning algorithms have difficulties handling the variable length observation sequences, but a reinforcement learning algorithm, the temporal difference (TD) learning algorithm works ideally in training the neural network. The entire neural network is composed of context units followed by a feedforward neural network. The context units are necessary to store dynamic information that is needed to make good decisions. For an appropriate neural-network architecture, trained with independent and identically distributed (iid) observations by the TD learning algorithm, we show that the neural-network sequential detector can closely approximate the optimal SPRT with similar performance. The neural-network sequential detector has the additional advantage that it is a nonparametric detector that does not require probability density functions. Simulations demonstrated on iid Gaussian data show that the neural network and the SPRT have similar performance.     

基于随机森林的目标检测与定位

为解决复杂图像中的目标检测与定位问题,提出一种基于随机森林的目标检测与定位算法。采用SIFT局部特征构造随机森林分类器,以一个决策树中的全部叶子节点构成一个树型结构的判别式码本模型,从而获得更可靠的概率Hough投票,加快目标检测速度。实验结果证明,该算法效率较高,可用于复杂场景下的目标检测与定位。     

基于改进Harris算法的角点检测

提出了一种改进的Harris角点检测方法.该方法在Harris角点检测求得角点响应函数后,利用双掩膜来定义进行非极大值抑制的局部范围,结合K均值聚类方法进行非极大值抑制,若像素点的角点响应函数值满足预设角点判定条件,则将该像素点定义为角点.实验结果表明,该方法无需进行阈值选择,提高了角点检测精度.     

改进PM-MD的分类器集成

Hub会对高维数据分析产生显著消极影响,现有研究分别采用了五种降Hubness策略以提高分类效果,但单个降Hubness策略适用范围有限.为解决这一问题,提出对五种降Hub分类器进行基于PM-MD的集成,PM-MD集成是通过KNN确定分类对象的决策表以及通过分类器得到分类对象的类支持向量,最后通过比较决策表和类支持向量的相似性计算分类器的竞争力权重.由于PM-MD在处理高维数据集时高斯势函数存在弱化距离导致区分度不足的倾向,因此提出了采用欧氏距离直接计算决策表以提高分类精度.在12个UCI数据集上的实验结果表明：PM-MD不仅获得更好且稳定的分类结果,而改进后的PM-MD则进一步提高了分类精度.     

Fast image classification using a sequence of visual fixations

Based on human retinal sampling distributions and eye movements, a sequential resolution image preprocessor is developed. Combined with a nearest neighbor classifier, this preprocessor provides an efficient image classification method, the sequential resolution nearest neighbor (SRNN) classifier. The human eye has a typical fixation sequence that exploits the nonuniform sampling distribution of its retina. If the retinal resolution is not sufficient to identify an object, the eye moves in such a way that the projection of the object falls onto a retinal region with a higher sampling density. Similarly, the SRNN classifier uses a sequence of increasing resolutions until a final class decision is made. Experimental results on texture segmentation show that the preprocessor used in the SRNN classifier is considerably faster than traditional multiresolution algorithms which use all the available resolution levels to analyze the input data.     

C-FCN: Corners-based fully convolutional network for visual object detection

Object detection has achieved significantly progresses in recent years. Proposal-based methods have become the mainstream object detectors, achieving excellent performance on accurate recognition and localization of objects. However, region proposal generation is still a bottleneck. In this paper, to address the limitations of conventional region proposal network (RPN) that defines dense anchor boxes with different scales and aspect ratios, we propose an anchor-free proposal generator named corner region proposal network (CRPN) which is based on a pair of key-points, including top-left corner and bottom-right corner of an object bounding box. First, we respectively predict the top-left corners and bottom-right corners by two sibling convolutional layers, then we obtain a set of object proposals by grouping strategy and non-maximum suppression algorithm. Finally, we further merge CRPN and fully convolutional network (FCN) into a unified network, achieving an end-to-end object detection. Our method has been evaluated on standard PASCAL VOC and MS COCO datasets using a deep residual network. Experiment results present that the proposed method outperforms previous detectors in the term of precision. Additionally, it runs with a speed of 76 ms per image on a single GPU by using ResNet-50 as the backbone, which is faster than other detectors.

     

Extended SPRT for structural change detection of time series based on a multiple regression model

It is important to detect a structural change in a time series quickly as a trigger to remodeling the forecasting model. The well-known Chow test has been used as the standard method for detecting change, especially in economics. However, we have proposed the application of the sequential probability ratio test (SPRT) for detecting the change in single-regression modeled time-series data. In this article, we show experimental results using SPRT and the Chow test when applied to time-series data that are based on multiple regression models. We also clarify the effectiveness of SPRT compared with the Chow test in its ability to detect change early and correctly, and its computational complexity. Moreover, we extend the definition of the point at which structural change is detected with the SPRT method, and show an improvement in the accuracy of change detection.     

Training Effective Node Classifiers for Cascade Classification

Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al. (linear asymmetric classifier for cascade detectors, 2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.     

A near-optimal probing strategy for workpiece localization

This paper addresses an optimal planning problem for workpiece localization with coordinate measurements. The fundamental issue is to find the best probing locations and a suitable sampling size, such that the uncertainty of the localization error is within a predefined limited bound. First, we introduce two sequential optimization algorithms to incrementally increase the localization accuracy, defined by the determinant of the information matrix of the measurements. Then, a reliability analysis method is incorporated for finding a sample size that is sufficient to reduce the uncertainty of the localization error to a limited bound. By combining these two analysis tools, we present a near-optimal probing strategy for finding the best probing locations and a suitable sampling size. With this strategy, given the desired translation and orientation error bounds and desired confidence limit, we can experimentally determine the least number of points needed to measure. Simulation and experimental results show the efficiency of the proposed probing strategy.     

一种改进的基于R-FCN模型的人脸检测算法

基于区域的卷积神经网络在目标检测中有着广泛的应用,吸引了研究者的广泛兴趣。针对人脸检测问题,本文基于区域的全卷积网络（Region-based Fully Convolutional Networks, R-FCN）,提出一种改进的人脸检测算法。为了使模型训练更加充分,利用在线难例样本挖掘法放宽正负样本的约束,扩充训练集的范围,针对人脸目标存在重叠问题,采用线性非极大值抑制法避免漏检重叠人脸。在人脸检测数据库（FDDB）上的实验结果表明,改进的R-FCN模型比原始的R-FCN模型有着更高的精度。     

基于SMT方法的测试性验证试验方案设计

针对现有导弹测试性验证试验方案确定的故障样本量较大以及序贯类试验方案最大样本量上界无法确定的问题,提出一种基于序贯网图检验（SMT）方法的测试性验证试验方案。通过对序贯概率比检验（SPRT）的检验问题进行拆分,在确定检验点数目的基础上,提出一种检验点取值与最大样本量的优化求解方法。同时考虑最大样本量较大的情形,基于承制方风险和使用方风险设计了SMT方法的截尾策略。通过案例验证与经典验证方法以及SPRT方法进行对比分析。结果表明,该方法能控制验证试验的样本量且所确定的平均样本量也优于其他方法,导弹装备的测试性验证试验的实施具备实际指导意义。     

基于MSET和SPRT的导弹振动故障诊断

提出了一种基于多元状态估计技术(MSET,multivariate state estimation techniques)和序贯概率比检验(SPRT,sequential probability ratio test)的导弹机构振动故障诊断方法.首先建立常规情况下导弹3处振动传感器所收集的振动信号的关联模型;然后根据导弹3处异常振动信号的当前观测测特征向量与各建模样本特征向量之间的相似性程度,使用MSET对当前异常信号特征向量进行估计,得到与异常信号特征向量相对应的估计残差;最后使用SPRT对异常信号的估计残差进行均值和方差检验,确定系统的工作状态.仿真结果表明,MSET可有效地增强故障状态下的信号特征呈现,而SPRT可在较少的周期内实现对弹体机构异常工作的识别,MSET和SPRT的结合有效地实现了对导弹机构异常工作的早期诊断.     

Classification of dissimilarity data with a new flexible Mahalanobis-like metric

Statistical pattern recognition traditionally relies on feature-based representation. For many applications, such vector representation is not available and we only possess proximity data (distance, dissimilarity, similarity, ranks, etc.). In this paper, we consider a particular point of view on discriminant analysis from dissimilarity data. Our approach is inspired by the Gaussian classifier and we defined decision rules to mimic the behavior of a linear or a quadratic classifier. The number of parameters is limited (two per class). Numerical experiments on artificial and real data show interesting behavior compared to Support Vector Machines and to kNN classifier: (a) lower or equivalent error rate, (b) equivalent CPU time, (c) more robustness with sparse dissimilarity data.     

SEQUENTIAL PARTITION DETECTORS WITH DEPENDENT SAMPLING

In this paper, the theory of sequential partition detectors with dependent sampling is introduced. A new formulation is given which predicts the thresholds under q-dependent sampling in order to maintain the same error probabilities as in the independent sampling case. A comparison is made between independent and dependent sequential partition detectors based on the average time to detection. Under stated conditions dependent sequential partition detectors show improved efficiency for both Lehmann and shift of the mean alternatives.     

Object Detection Using the Statistics of Parts

In this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size, location, and pose. To cope with variation in object orientation, the detector uses multiple classifiers, each spanning a different range of orientation. Each of these classifiers determines whether the object is present at a specified size within a fixed-size image window. To find the object at any location and size, these classifiers scan the image exhaustively.Each classifier is based on the statistics of localized parts. Each part is a transform from a subset of wavelet coefficients to a discrete set of values. Such parts are designed to capture various combinations of locality in space, frequency, and orientation. In building each classifier, we gathered the class-conditional statistics of these part values from representative samples of object and non-object images. We trained each classifier to minimize classification error on the training set by using Adaboost with Confidence-Weighted Predictions (Shapire and Singer, 1999). In detection, each classifier computes the part values within the image window and looks up their associated class-conditional probabilities. The classifier then makes a decision by applying a likelihood ratio test. For efficiency, the classifier evaluates this likelihood ratio in stages. At each stage, the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation—labeling the input as non-object—or to continue further evaluation. The detector orders these stages of evaluation from a low-resolution to a high-resolution search of the image. Our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.     

一种基于分布式最小均方差序贯估计的气体泄漏源定位算法

基于无线传感网络的气体泄漏源定位在环境监测、安全防护和污染控制等多个领域具有重要意义。提出一种基于分布式最小均方差(D-MMSE)序贯估计的气体泄漏源定位算法。其通过构建一个包含节点之间信息增益与网络能量消耗两方面参数的信息融合目标函数,并对目标函数寻优实现路由节点的调度与选择。所选节点在其测量值和前节点估计值并通过与邻居节点信息交互的基础上完成气体泄漏源位置参数估计量及其方差的更新与传递。为了降低网络能耗,邻居节点集的选择半径随估计量方差做动态调整。仿真分析表明所提算法对比单节点序贯估计定位算法在一定的能耗条件下可获得较高的定位精度和速度。     

Adaptive color segmentation-a comparison of neural and statisticalmethods

With the availability of more powerful computers it is nowadays possible to perform pixel based operations on real camera images even in the full color space. New adaptive classification tools like neural networks make it possible to develop special-purpose object detectors that can segment arbitrary objects in real images with a complex distribution in the feature space after training with one or several previously labeled image(s). The paper focuses on a detailed comparison of a neural approach based on local linear maps (LLMs) to a classifier based on normal distributions. The proposed adaptive segmentation method uses local color information to estimate the membership probability in the object, respectively, background class. The method is applied to the recognition and localization of human hands in color camera images of complex laboratory scenes.     

SMC框架中引入残差信息的分层重采样策略

近年来序列蒙特卡罗理论及其应用在自动导航﹑非线性估计与金融等诸多领域受到了越来越广泛的关注。提出了一种引入残差信息的分层重采样策略,通过引入当前粒子集权值的残差来构建累积分布函数,同时针对随机区间逐级分层以产生有序的随机数集合,从而提高重采样的合理性与采样效率。首先从仿真实验的角度证明了它的有效性,对比残差重采样﹑多项式重采样与遗传重采样,提出的重采样策略在后验均值误差﹑均方差与运行时间方面均为最小;将提出的重采样策略嵌入到运动目标跟踪算法中,基于标准测试视频的跟踪结果同样佐证了该重采样策略的收敛性及良好的抗噪性能。