Using extended feature objects for partial similarity retrieval

In this paper, we introduce the concept of extended feature objects for similarity retrieval. Conventional approaches for similarity search in databases map each object in the database to a point in some high-dimensional feature space and define similarity as some distance measure in this space. For many similarity search problems, this feature-based approach is not sufficient. When retrieving partially similar polygons, for example, the search cannot be restricted to edge sequences, since similar polygon sections may start and end anywhere on the edges of the polygons. In general, inherently continuous problems such as the partial similarity search cannot be solved by using point objects in feature space. In our solution, we therefore introduce extended feature objects consisting of an infinite set of feature points. For an efficient storage and retrieval of the extended feature objects, we determine the minimal bounding boxes of the feature objects in multidimensional space and store these boxes using a spatial access structure. In our concrete polygon problem, sets of polygon sections are mapped to 2D feature objects in high-dimensional space which are then approximated by minimal bounding boxes and stored in an R-tree. The selectivity of the index is improved by using an adaptive decomposition of very large feature objects and a dynamic joining of small feature objects. For the polygon problem, translation, rotation, and scaling invariance is achieved by using the Fourier-transformed curvature of the normalized polygon sections. In contrast to vertex-based algorithms, our algorithm guarantees that no false dismissals may occur and additionally provides fast search times for realistic database sizes. We evaluate our method using real polygon data of a supplier for the car manufacturing industry. Edited by R. Güting. Received October 7, 1996 / Accepted March 28, 1997     

A feature modeller for sculptured object modelling

There are numerous discussions concerning feature modelling. Most of these studies concentrate on regular shaped object modelling. The objective of this paper is to extend feature modelling coverage to include sculptured objects, so that feature technology is beneficial to modelling this class of objects. Feature modelling for a sculptured object consists of two basic elements: features and modelling operations. Feature semantics are declared by means of constraints in a feature class specification. These constraints are instantiated by the modelling operations during the modelling session. A feature modeller for sculptured objects is designed to generate the feature model from these constraints. A validation mechanism is introduced in the modeller to maintain the semantics of the feature model.     

Distributed boundary coverage with a team of networked miniature robots using a robust market-based algorithm

We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortended by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.     

Sharp feature preserving MLS surface reconstruction based on local feature line approximations

Sharp features in manufactured and designed objects require particular attention when reconstructing surfaces from unorganized scan point sets using moving least squares (MLS) fitting. It is an inherent property of MLS fitting that sharp features are smoothed out. Instead of searching for appropriate new fitting functions our approach computes a modified local point neighborhood so that a standard MLS fitting can be applied enhanced by sharp features reconstruction.We present a two-stage algorithm. In a pre-processing step sharp feature points are marked first. This algorithm is robust to noise since it is based on Gauss map clustering. In the main phase, the selected feature points are used to locally approximate the feature curve and to segment and enhance the local point neighborhood. The MLS projection thus leads to a piecewise smooth surface preserving all sharp features. The method is simple to implement and able to preserve line-type features as well as corner-type features during reconstruction.     

一种图像噪声的形态学多尺度去除方法

提出了一种基于形态学多尺度的图像噪声去除方法，该方法首先利用形态学多尺度开闭重建运算对噪声图像进行多尺度重建，将噪声图像分解为一系列尺度不同的特征图像叠加，然后对叠加特征图像进行尺度模式谱分析，确定图像中噪声对应的尺度范围，最后将噪声尺度对应的特征图像从噪声中去除，达到同时消除噪声和保持图像目标信息完整及准确定位的目的。仿真实验表明，该方法能够有效地去除不同类型的图像噪声，具有较高的输出信噪比，同时保持了图像信息的完整和图像目标的准确定位。     

Confidence estimation methods for neural networks: a practicalcomparison

Feedforward neural networks, particularly multilayer perceptrons, are widely used in regression and classification tasks. A reliable and practical measure of prediction confidence is essential. In this work three alternative approaches to prediction confidence estimation are presented and compared. The three methods are the maximum likelihood, approximate Bayesian, and the bootstrap technique. We consider prediction uncertainty owing to both data noise and model parameter misspecification. The methods are tested on a number of controlled artificial problems and a real, industrial regression application, the prediction of paper "curl". Confidence estimation performance is assessed by calculating the mean and standard deviation of the prediction interval coverage probability. We show that treating data noise variance as a function of the inputs is appropriate for the curl prediction task. Moreover, we show that the mean coverage probability can only gauge confidence estimation performance as an average over the input space, i.e., global performance and that the standard deviation of the coverage is unreliable as a measure of local performance. The approximate Bayesian approach is found to perform better in terms of global performance.     

Support vector machines for dyadic data

We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression functions for the column objects. Contrary to standard support vector machine approaches, the P-SVM minimizes a scale-invariant capacity measure and requires a new set of constraints. As a result, the P-SVM method leads to a usually sparse expansion of the classification and regression functions in terms of the row rather than the column objects and can handle data and kernel matrices that are neither positive definite nor square. We then describe two complementary regularization schemes. The first scheme improves generalization performance for classification and regression tasks; the second scheme leads to the selection of a small, informative set of row support objects and can be applied to feature selection. Benchmarks for classification, regression, and feature selection tasks are performed with toy data as well as with several real-world data sets. The results show that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as true dyadic data sets. In addition, a theoretical justification is provided for the new approach.     

Synthesis of digital H ∞-controllers for multidimensional systems with given accuracy with the mean squared criterion

We consider linear multidimensional objects subject to power bounded polyharmonic perturbances and measurement noise that contains an arbitrary number of harmonics with unknown amplitudes and frequencies. For such objects, we propose a synthesis method for digital state controllers and controllers with respect to measurable output. The problem of guaranteeing a desired accuracy is formulated as the problem of guaranteeing a given mean squared radius of the stabilized state [1]; our solution of this problem is based on the choice of weight matrices in the minimal quadratic functional of a discrete H _∞-optimization problem. We give a synthesis algorithm for a digital controller in the LMI Control Toolbox package and a numerical example for an interconnected electric drive.     

Path coverage properties of randomly deployed sensors with finite data-transmission ranges

We analyze the path coverage properties of a sensor network, where numerous sensors are randomly deployed. We characterize the path coverage in terms of three metrics: fraction of coverage, probability of complete coverage, and probability of partial coverage. We derive the expressions for the three coverage metrics as functions of the sensor density, the sensing range of a sensor, and the data-transmission range of a sensor. Based on the derived expressions, we study the impact of the data-transmission range on the path coverage and show, for example, that a sensor should have a long data-transmission range in order to achieve high detectability of intruding objects (partial coverage probability of the trajectories of objects) with a modest sensor density.     

基于类扩张矩阵的信息系统特征选取

特征选取是一个NP-Hard问题。为了快速完成信息系统的一个最小特征选取，引入了类扩张矩阵的定义。通过类扩张矩阵的元素表示对象的差异，并利用逻辑上包含关系，有效浓缩类扩张矩阵。最后，以类扩张矩阵的统计信息为启发式信息，在浓缩类扩张矩阵中实现一个最小特征子集的快速求解。通过理论分析和实验，证明了该特征选取方法的高效性。     

Robust speech recognition in noisy environments based on subband spectral centroid histograms

We investigate how dominant-frequency information can be used in speech feature extraction to increase the robustness of automatic speech recognition against additive background noise. First, we review several earlier proposed auditory-based feature extraction methods and argue that the use of dominant-frequency information might be one of the major reasons for their improved noise robustness. Furthermore, we propose a new feature extraction method, which combines subband power information with dominant subband frequency information in a simple and computationally efficient way. The proposed features are shown to be considerably more robust against additive background noise than standard mel-frequency cepstrum coefficients on two different recognition tasks. The performance improvement increased as we moved from a small-vocabulary isolated-word task to a medium-vocabulary continuous-speech task, where the proposed features also outperformed a computationally expensive auditory-based method. The greatest improvement was obtained for noise types characterized by a relatively flat spectral density.     

A graph-based framework for sub-pixel image segmentation

Many image segmentation methods utilize graph structures for representing images, where the flexibility and generality of the abstract structure is beneficial. By using a fuzzy object representation, i.e., allowing partial belongingness of elements to image objects, the unavoidable loss of information when representing continuous structures by finite sets is significantly reduced, enabling feature estimates with sub-pixel precision.This work presents a framework for object representation based on fuzzy segmented graphs. Interpreting the edges as one-dimensional paths between the vertices of a graph, we extend the notion of a graph cut to that of a located cut, i.e., a cut with sub-edge precision. We describe a method for computing a located cut from a fuzzy segmentation of graph vertices. Further, the notion of vertex coverage segmentation is proposed as a graph theoretic equivalent to pixel coverage segmentations and a method for computing such a segmentation from a located cut is given. Utilizing the proposed framework, we demonstrate improved precision of area measurements of synthetic two-dimensional objects. We emphasize that although the experiments presented here are performed on two-dimensional images, the proposed framework is defined for general graphs and thus applicable to images of any dimension.     

Multibody Grouping from Motion Images

We want to deduce, from a sequence of noisy two-dimensional images of a scene of several rigid bodies moving independently in three dimensions, the number of bodies and the grouping of given feature points in the images to the bodies. Prior processing is assumed to have identified features or points common to all frames and the images are assumed to be created by orthographic projection (i.e., perspective effects are minimal). We describe a computationally inexpensive algorithm that can determine which points or features belong to which rigid body using the fact that, with exact observations in orthographic projection, points on a single body lie in a three or less dimensional linear manifold of frame space. If there are enough observations and independent motions, these manifolds can be viewed as a set linearly independent, four or less dimensional subspaces. We show that the row echelon canonical form provides direct information on the grouping of points to these subspaces. Treatment of the noise is the most difficult part of the problem. This paper uses a statistical approach to estimate the grouping of points to subspaces in the presence of noise by computing which partition has the maximum likelihood. The input data is assumed to be contaminated with independent Gaussian noise. The algorithm can base its estimates on a user-supplied standard deviation of the noise, or it can estimate the noise from the data. The algorithm can also be used to estimate the probability of a user-specified partition so that the hypothesis can be combined with others using Bayesian statistics.     

基于覆盖分割和活动轮廓模型的图像边缘提取算法

针对传统算法对边界模糊的图像分割效果不理想,分割结果多毛刺的问题,提出了一种由粗到细的图像边缘提取方法,主要由像素覆盖分割方法和Chan-Vese模型组成。将改进的覆盖分割方法和活动轮廓模型相结合,首先使用原始覆盖分割算法对图像进行分割,利用多方向模糊形态学边缘检测算法提取不同物体之间的边界;然后采用改进的像素覆盖分割方法给边界像素重新分配覆盖值;最后,运用活动轮廓算法进行细化的图像边界提取;分别进行了分割结果的定性比较,抗噪性测试以及提取的边缘对比实验。实验结果表明,该方法对具有模糊边界的图像,提取边缘结果优于其他可比文献中提出的方法。     

Stochastic models for recognition of occluded targets

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. Stochastic models provide some attractive features for pattern matching and recognition under partial occlusion and noise. In this paper, we present a hidden Markov modeling based approach for recognizing objects in SAR images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabilistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.     

Error analysis of robust optical flow estimation by least median of squares methods for the varying illumination model

The apparent pixel motion in an image sequence, called optical flow, is a useful primitive for automatic scene analysis and various other applications of computer vision. In general, however, the optical flow estimation suffers from two significant problems: the problem of illumination that varies with time and the problem of motion discontinuities induced by objects moving with respect to either other objects or with respect to the background. Various integrated approaches for solving these two problems simultaneously have been proposed. Of these, those that are based on the LMedS (least median of squares) appear to be the most robust. The goal of this paper is to carry out an error analysis of two different LMedS-based approaches, one based on the standard LMedS regression and the other using a modification thereof as proposed by us recently. While it is to be expected that the estimation accuracy of any approach would decrease with increasing levels of noise, for LMedS-like methods, it is not always clear as to how much of that decrease in performance can be attributed to the fact that only a small number of randomly selected samples is used for forming temporary solutions. To answer this question, our study here includes a baseline implementation in which all of the image data is used for forming motion estimates. We then compare the estimation errors of the two LMedS-based methods with the baseline implementation. Our error analysis demonstrates that, for the case of Gaussian noise, our modified LMedS approach yields better estimates at moderate levels of noise, but is outperformed by the standard LMedS method as the level of noise increases. For the case of salt-and-pepper noise, the modified LMedS method consistently performs better than the standard LMedS method.     

Self-tuned Evolution-COnstructed features for general object recognition

Object recognition is a well studied but extremely challenging field. We present a novel approach to feature construction for object detection called Evolution-COnstructed Features (ECO features). Most current approaches rely on human experts to construct features for object recognition. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover multiple series of transforms that are highly discriminative. Using ECO features provides several advantages over other object detection algorithms including: no need for a human expert to build feature sets or tune their parameters, ability to generate specialized feature sets for different objects, no limitations to certain types of image sources, and ability to find both global and local feature types. We show in our experiments that the ECO features compete well against state-of-the-art object recognition algorithms.     

基于统计假设测试的噪声方差估计方法

在数字图像处理中,噪声方差估计是一个重要的研究课题。提出一种针对加性高斯噪声的噪声方差估计方法。利用一种基于统计假设测试的方法来度量图像结构特征度,基于图像结构特征度找出平滑子块和非平滑子块（含有边缘或纹理子块）;以平滑子块中的最小方差为参考方差,选择出方差与参考方差相差在一定范围内且不含边缘的所有子块;从选出的子块中求以图像结构特征度为权重的方差平均值作为噪声方差估计值。相比于现有的噪声估计方法,该方法具有非常高的估计精度,适合感染高斯噪声的各种图像。     

Combining object and feature dynamics in probabilistic tracking

Objects can exhibit different dynamics at different spatio-temporal scales, a property that is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image features that are then used as inputs to a system for tracking the object using a global dynamic model. Approximate local dynamics may be brittle—point trackers drift due to image noise and adaptive background models adapt to foreground objects that become stationary—and constraints from the global model can make them more robust. We propose a probabilistic framework for incorporating knowledge about global dynamics into the local feature extraction processes. A global tracking algorithm can be formulated as a generative model and used to predict feature values thereby influencing the observation process of the feature extractor, which in turn produces feature values that are used in high-level inference. We combine such models utilizing a multichain graphical model framework. We show the utility of our framework for improving feature tracking as well as shape and motion estimates in a batch factorization algorithm. We also propose an approximate filtering algorithm appropriate for online applications and demonstrate its application to tasks in background subtraction, structure from motion and articulated body tracking.     

基于二阶非线性投票的多目标跟踪算法*

提出了基于二阶非线性投票的多目标跟踪算法。该算法通过目标匹配得到同一目标在不同帧中的位置,同时利用特征监测来处理目标的遮挡、分裂问题,并实现目标特征的实时更新。在目标匹配过程中,通过对目标前一帧与当前帧的特征相似性进行投票,得到匹配目标。利用视频图像进行实验,结果表明,该方法对噪声、阴影、遮挡、分裂等具有良好的鲁棒性,较好地实现了多目标的跟踪。