人脸检测的一种混合方法研究

人脸检测作为自动人脸识别系统的第一个环节具有非常重要的作用,为了解决目前大部分人脸检测方法存在的分类器训练困难和检测计算量大等问题,提出了一种人脸检测的混合方法。该方法由两级分类器组成,第一级为粗分类器主要过滤大部分非人脸区域,第二级为核心分类器,在由第一级粗分类的基础上利用非线性SVM算法进行人脸检测。在CMU数据库上的实验结果表明,该方法具有较高的人脸检测率,检测速度得到大幅提高。

自主机器人的强化学习研究进展

虽然基于行为控制自主机器人具有较高的鲁棒性，但其对于动态环境缺乏必要的自适应能力，强化学习方法使机器人可以通过学习来完成任务，而无需设计者完全预先规定机器人的所有动作，它是将动态规划和监督学习结合的基础上发展起来的一种新颖的学习方法，它通过机器人与环境的试错交互，利用来自成功和失败经验的奖励和惩罚信号不断改进机器人的性能，从而达到目标，并容许滞后评价，由于其解决复杂问题的突出能力，强化学习已成为一种非常有前途的机器人学习方法，本文系统论述了强化学习方法在自主机器人中的研究现状，指出了存在的问题，分析了几种问题解决途径，展望了未来发展趋势。

混合模式识别系统研究

讨论基于多种分类方法的模块组合实现的混合模式识别系统，它不同于利用多分类器输出结果表决的集成系统．提出两个系统：一个面向印刷体汉字文本识别，另一个面向自由手写体数字识别．利用多种特征和多种分类方法的组合、部分识别信息控制混淆字判别策略以及提出的动态模板库匹配后处理方法，使系统的性能与传统单一分类器系统比较，获得明显改善．实验表明：多方法多策略混合是解决复杂和增强系统鲁棒性的一条途径

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.

Stable Mixing of Complete and Incomplete Information

An increasing number of parameter estimation tasks involve the use of at least two information sources, one complete but limited, the other abundant but incomplete. Standard algorithms such as EM (or em) used in this context are unfortunately not stable in the sense that they can lead to a dramatic loss of accuracy with the inclusion of incomplete observations. We provide a more controlled solution to this problem through differential equations that govern the evolution of locally optimal solutions (fixed points) as a function of the source weighting. This approach permits us to explicitly identify any critical (bifurcation) points leading to choices unsupported by the available complete data. The approach readily applies to any graphical model in O(n^3) time where n is the number of parameters. We use the naive Bayes model to illustrate these ideas and demonstrate the effectiveness of our approach in the context of text classification problems.

Learning object segmentation from video data

This memo describes the initial results of a project to create a self-supervised algorithm for learning object segmentation from video data. Developmental psychology and computational experience have demonstrated that the motion segmentation of objects is a simpler, more primitive process than the detection of object boundaries by static image cues. Therefore, motion information provides a plausible supervision signal for learning the static boundary detection task and for evaluating performance on a test set. A video camera and previously developed background subtraction algorithms can automatically produce a large database of motion-segmented images for minimal cost. The purpose of this work is to use the information in such a database to learn how to detect the object boundaries in novel images using static information, such as color, texture, and shape. This work was funded in part by the Office of Naval Research contract #N00014-00-1-0298, in part by the Singapore-MIT Alliance agreement of 11/6/98, and in part by a National Science Foundation Graduate Student Fellowship.

Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection.

Recovery of linear and nonlinear heart rate dynamics after coronary artery bypass grafting surgery

Background: Conventional coronary artery bypass grafting (C-CABG) and off-pump CABG (OPCAB) surgery may produce different patients' outcomes, including the extent of cardiac autonomic (CA) imbalance. the beneficial effects of an exercise-based inpatient programme on heart rate variability (HRV) for C-CABG patients have already been demonstrated by our group. However, there are no studies about the impact of a cardiac rehabilitation (CR) on HRV behaviour after OPCAB. the aim of this study is to compare the influence of both operative techniques on HRV pattern following CR in the postoperative (PO) period.Methods: Cardiac autonomic function was evaluated by HRV indices pre- and post-CR in patients undergoing C-CABG (n = 15) and OPCAB (n = 13). All patients participated in a short-term(approximately 5 days) supervised CR programme of early mobilization, consisting of progressive exercises, from active-assistive movements at PO day 1 to climbing flights of stairs at PO day 5.Results: Both groups demonstrated a reduction in HRV following surgery. the CR programme promoted improvements in HRV indices at discharge for both groups. the OPCAB group presented with higher HRV values at discharge, compared to the C-CABG group, indicating a better recovery of CA function.Conclusion: Our data suggest that patients submitted to OPCAB and an inpatient CR programme present with greater improvement in CA function compared to C-CABG.

Adaptive denoising in spectral analysis by genetic programming

Rowland, J.J. and Taylor, J. (2002). Adaptive denoising in spectral analysis by genetic programming. Proc. IEEE Congress on Evolutionary Computation (part of WCCI), May 2002. pp 133-138. ISBN 0-7803-7281-6

Machine learning of functional class from phenotype data

Clare, A. and King R.D. (2002) Machine learning of functional class from phenotype data. Bioinformatics 18(1) 160-166

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

Survey of Rough and Fuzzy Hybridization.

P. Lingras and R. Jensen, 'Survey of Rough and Fuzzy Hybridization,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 125-130, 2007.

Tolerance-based and Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Tolerance-based and Fuzzy-Rough Feature Selection,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 877-882, 2007.

Evolutionary approaches to fuzzy modelling for classification

M. Galea, Q. Shen and J. Levine. Evolutionary approaches to fuzzy modelling. Knowledge Engineering Review, 19(1):27-59, 2004.