Research of Face Location System Based on Human Vision Simulations

In this paper we present a robust face location system based on human vision simulations to automatically locate faces in color static images. Our method is divided into four stages. In the first stage we use a gauss low-pass filter to remove the fine information of images, which is useless in the initial stage of human vision. During the second and the third stages, our technique approximately detects the image regions, which may contain faces. During the fourth stage, the existence of faces in the selected regions is verified. Having combined the advantages of Bottom-Up Feature Based Methods and Appearance-Based Methods, our algorithm performs well in various images, including those with highly complex backgrounds.

基于DM642的香烟小包装外观检测系统

本文设计与实现了一种基于TMS320DM642的香烟小包装外观质量检测系统,详细阐述了该系统的硬件构成、软件流程、检测算法以及针对DSP处理器进行的系统优化。系统以TMS320DM642处理器为核心建立硬件平台,通过摄像头获取香烟小包装图像,采用改进的模板匹配算法对当前图像进行质量检测,最终在监视器上显示检测结果并将检测结果送执行单元进行处理。实验结果表明基于TMS320DM642的香烟小包装检测系统,检测效果快速、准确、有效,应用前景广泛。

视频图像处理在小包外观检测中的应用研究

香烟小包装在线实时检测系统是一种烟草行业产品包装检测设备,具有广阔的应用前景。在现代生产过程中，生产速度越来越快，对产品质量的要求也越来越高。烟草企业在香烟的生产过程中，从烟叶制丝到卷、接、包装都已经实现了自动化。香烟小包装的外观质量，反映了烟厂的技术装备水平，涉及到企业的形象、信誉问题，同时，有质量缺陷的香烟小包装被市场反馈回企业，也会带来企业成本的增加。 随着计算机软件、硬件技术的发展，以及机器视觉理论的完善，采用机器视觉的方法来检测香烟小包装的外观质量，已经开始应用。机器视觉在检测方面具有先天优势检测速度快、分辨能力高、规范化程度高、可重复性好。采用现代机器视觉技术来进行香烟小包装外观质量的检测，可以大大降低检验人员的劳动强度，提高产品的质量，减少烟厂的人力成本和管理成本，改善企业形象。 本文分析了国内外烟包包装质量检测的许多方法，设计了一套基于DSP的香烟小包装外观质量检测系统，可以对香烟小包装进行实时检测，达到实时剔除有包装质量缺陷的香烟小包装的目的。 从机器视觉的角度出发，本文阐述了视觉检测系统的工作原理、总体机构及系统的工作流程，同时对比各种硬件特性，进行了光源、传感器、相机、镜头及DSP芯片的选型，着重介绍了图像处理算法，尤其是本系统中用到的图像配准、模板匹配以及各种缺陷的识别进行了详细的描述，并给出了程序在DSP中的优化方法。 本文对设计的系统进行了一系列的实验和测试，结果表明，本系统具有速度快，总体检测效果好，稳定性好的特点，可以达到香烟小包装实时检测的要求。

Strain Rate-Dependent Compressive Deformation Behavior of Nd-Based Bulk Metallic Glass

Compressive deformation behavior of the Nd60Fe20Co10Al10 bulk metallic glass was characterized over a wide strain rate range (6.0 x 10(-4) to 1.0x10(3) s(-1)) at room temperature. Fracture stress was found to increase and fracture strain decrease with increasing applied strain rate. Serrated flow and a large number of shear bands were observed at the quasi-static strain rate (6.0 x 10(-4)s(-1)). The results suggest that the appearance of a large number of shear bands is probably associated with flow serration observed during compression; and both shear banding and flow serration are a strain accommodation and stress relaxation process. At dynamic strain rates (1.0 x 10(3) s(-1)), the rate of shear band nucleation is not sufficient to accommodate the applied strain rate and thus causes an early fracture of the test sample. The fracture behavior of the Nd60Fe20Co10Al10 bulk metallic glass is sensitive to strain rate.

Deformation Behavior Of Fe-Based Bulk Metallic Glass During Nanoindentation

Fe-based bulk metallic glasses (BMGs) normally exhibit super high strength but significant brittleness at ambient temperature. Therefore, it is difficult to investigate the plastic deformation behavior and mechanism in these alloys through conventional tensile and compressive tests due to lack of distinct macroscopic plastic strain. In this work, the deformation behavior of Fe52Cr15Mo9Er3C15B6 BMG was investigated through instrumented nanoindentation and uniaxial compressive tests. The results show that serrated flow, the typical plastic deformation feature of BMGs, could not be found in as-cast and partially crystallized samples during nanoindentation. In addition, the deformation behavior and mechanical properties of the alloy are insensitive to the applied loading rate. The mechanism for the appearance of the peculiar deformation behavior in the Fe-based BMG is discussed in terms of the temporal and spatial characteristics of shear banding during nanoindentation.

Plastic deformation in Ce-based bulk metallic glasses during depth-sensing indentation

The deformation behavior and the effect of the loading rate on the plastic deformation features in (numbers indicate at.%) Ce60Al15Cu10Ni15, Ce65Al10Cu10Ni10Nb5, Ce68Al10Cu20Nb2, and Ce70Al10Cu20 bulk metallic glasses (BMGs) were investigated through nanoindentation. The load-displacement (P-h) curves of Ce65Al10Cu10Ni10Nb5, Ce68Al10Cu2, and Ce70Al10Cu20 BMGs exhibited a continuous plastic deformation at all studied loading rate. Whereas, the P-h curves of Ce60Al15Cu10Ni15 BMG showed a quite unique feature, i.e. homogeneous plastic deformation at low loading rates, and a distinct serrated flow at high strain rates. Moreover, a creep deformation during the load holding segment was observed for the four Ce-based BMGs at room temperature. The mechanism for the appearance of the "anomalous" plastic deformation behavior in the Ce-based BMGs was discussed. (c) 2006 Elsevier B.V. All rights reserved.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

Incremental pairwise discriminant analysis based visual tracking

The distinguishment between the object appearance and the background is the useful cues available for visual tracking in which the discriminant analysis is widely applied However due to the diversity of the background observation there are not adequate negative samples from the background which usually lead the discriminant method to tracking failure Thus a natural solution is to construct an object-background pair constrained by the spatial structure which could not only reduce the neg-sample number but also make full use of the background information surrounding the object However this Idea is threatened by the variant of both the object appearance and the spatial-constrained background observation especially when the background shifts as the moving of the object Thus an Incremental pairwise discriminant subspace is constructed in this paper to delineate the variant of the distinguishment In order to maintain the correct the ability of correctly describing the subspace we enforce two novel constraints for the optimal adaptation (1) pairwise data discriminant constraint and (2) subspace smoothness The experimental results demonstrate that the proposed approach can alleviate adaptation drift and achieve better visual tracking results for a large variety of nonstationary scenes (C) 2010 Elsevier B V All rights reserved

A Review of Active Appearance Models

Active appearance model (AAM) is a powerful generative method for modeling deformable objects. The model decouples the shape and the texture variations of objects, which is followed by an efficient gradient-based model fitting method. Due to the flexible and simple framework, AAM has been widely applied in the fields of computer vision. However, difficulties are met when it is applied to various practical issues, which lead to a lot of prominent improvements to the model. Nevertheless, these difficulties and improvements have not been studied systematically. This motivates us to review the recent advances of AAM. This paper focuses on the improvements in the literature in turns of the problems suffered by AAM in practical applications. Therefore, these algorithms are summarized from three aspects, i.e., efficiency, discrimination, and robustness. Additionally, some applications and implementations of AAM are also enumerated. The main purpose of this paper is to serve as a guide for further research.