Application of multi-layer perceptron neural networks to vision problems

This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.     

Incremental Feature Selection

Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is large and can scale up without sacrificing the quality of selected features.     

Polarization Phase-Based Method For Material Classification In Computer Vision

A robust and accurate polarization phase-based technique for material classification is presented. The novelty of this technique is three-fold in (i) its theoretical development, (ii) application, and, (iii) experimental implementation. The concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly conducting dielectrics. Previous work has used intensity, color and polarization component ratios. This new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. This method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. Experiments for real circuit board inspection, nonconductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.     

一种融合PCA 和KFDA 的人脸识别方法

提出一种融合PCA和KFDA的人脸识别方法，即在进行非线性映射之前，首先利用经典的主分量分析(C—PCA)进行降维，然后执行KFDA．为进一步降低整个算法的计算时问，又提出一种I—PCA KFDA方法，它直接基于图像矩阵的主分量分析(I—PCA)．ORL标准人脸库的试验结果表明，与现有的核Fisher鉴别分析方法相比，两种方法可将特征抽取的速度分别提高3倍和7倍，其识别精度没有丝毫的降低．     

浅谈工程技术口译

结合口译的特点及笔的翻译实践讨论了如何作好工程技术口译工作，具体包括克服心理障碍；积累知识，充分准备；勤于实践；灵活的应变能力等。     

高等教育要走专业教育和通识教育相结合之路

当前,以专业教育思想为指导的高等教育已不能适应素质教育及创新性人才培养的要求,迫切需要进行改革,树立起以通识教育和专业教育相融合的教育理念。     

基于子带处理多分类器融合的说话人识别

提出了一种基于子带处理多分类器融合的说话人识别方法 .宽带语音信号通过Mel滤波器组转变为多个子带信号 ,对各子带数据独立分析提取相应的特征参数 ,进而对每个子带分别建立识别模型进行判决 ,最后利用分类器融合规则 ,给出总体判决 .研究表明 ,该方法在子带数目选为 16时可以得到最好的识别效果 ,并且在有窄带噪声的情况下 ,子带多分类器融合法比宽带语音数据建模表现出更好的鲁棒性 .     

基于计算机视觉的电厂车辆智能监测系统设计与实现

针对电厂运煤车辆的管理,设计并实现了一个基于计算机视觉的电厂运煤车辆智能监测系统.提出了一种基于颜色分割和数字形态学处理相结合的车牌定位算法和一种基于Sobel灰度边缘算子的车牌倾斜校正算法.实际应用表明,该系统有很强的鲁棒性,可以处理污损、变形、各种光照条件下的车牌,并且速度快、准确率高     

智能卡话者确认系统的研究

介绍了一个将参考模板存放在智能卡上的与相关的话者确认系统及其工作原理、相关算法和参数设置。     

会计职业道德对提高会计信息质量的影响

会计信息质量与环境、会计系统等诸多因素有关,同时会计职业道德也是一个非常重要的因素。它作为一种引导和制约会计行为的社会规范,与会计信息的质量息息相关。我国会计信息质量的现状不容乐观,重整我国会计职业道德,将对我国会计信息质量的提高起到积极的推动作用。文中分析了会计职业道德的特征和现状,提出了重整会计职业道德的几点思考.