Description: Chapter 2: Getting Started Hello World
Hello World Enhanced A simple "Hello World" application that shows the basics of how to use an ActionScript 3.0 class in an application. The Enhanced version adds a name-checking feature. These examples are meant to be built from scratch, tutorial-style. The example files are provided so you can see how they should look when completed.
Chapter 4: Object-Oriented Programming Geometric Shapes Uses the object-oriented concepts of class inheritance and the implementation of interfaces to provide an application that calculates values for simple geometric shapes.
Chapter 5: Display Programming Sprite Arranger Adds graphical Sprite objects to a drawing area and let you manipulate their placement in the display list. Builds upon the classes from the geometric Shapes example.
Chapter 6: Working with Dates and Times Simple Clock Displays a simple analog clock face using methods of the Date and flash.util.Timer classes.
Chapter 7: Working with Strings ASCII Art Loads bitmap images and coverts them into ASCII character equivalents, using a number of the methods in the String class.
Chapter 8: Working with Arrays Play List Demonstrates a number of methods of the Array class while building and presenting a play list of music files.
Chapter 9: Handling Errors Custom Errors Presents a simple framework containing a set of custom ApplicationError classes and shows how to throw, catch, and handle such errors.
Chapter 10: Using Regular Expressions Wiki Editor Uses regular expressions to convert text containing wiki-style codes into formatted HTML text. Also shows how to use regular expressions for other conversions, such as numeric calculations.
Chapter 11: Working With XML RSS Viewer Reads an RSS feed and formats the entries as HTML, including hyperlinks to the stories being referenced. This example shows the powerful new E4X statements and operators in action.
Chapter 13: Event Handling Alarm Clock Demonstrates how to define, dispatch, and handle custom event classes. Extends the Simple Clock application to create an Alarm Clock with specialized AlarmEvents.
Chapter 14: Networking and Communications File I/O Shows how to use the FileReference class to upload files from your local disk to a remote server, and how to download files from a remote server to your local disk.
Note: To run this example you will need to set the UPLOAD_URL and DOWNLOAD_URL variables in the code to the address of a web server that will accept uploads and allow downloads.
Telnet Socket Connects to a Telnet server and shows how to send and read bytes from the socket connection.
Note: To run this example you will need to have access to a running Telnet server.
Chapter 15: Working with Geometry Display Object Transformer Uses methods of the flash.geom.Matrix class to apply multiple geometric transformations to a DisplayObject.
Chapter 16: Client System Environment Capabilities Info Lists the capabilities of your current browser and operating system, by using the SystemCapabilities class and an ExternalInterface call that uses Javascript to retrieve browser properties.
Chapter 19: Using the External API IntrovertIM_CSharp
IntrovertIM_HTML A tiny instant messenger application the uses the flash.external.ExternalInterface class to send messages between a Flex/ActionScript application and an external application. Two versions are provided. One uses HTML and Javascript for the external application, and the other uses C#.
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Description: 关于人脸识别的一篇论文.人脸识别系统中的特征提取的一种新方法.-A paper on face recognition. Face Recognition System, a new feature extraction method. Platform: |
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Author:曹彪 |
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Description: 特征提取是人脸识别技术中一个基本而
又十分重要的环节，寻找有效的特征是解决识别问题的关键本文提出了一种新的人脸特征提取方法。该方法通过可调因子有效结合人脸局部流形结构信息和样本
的类别信息，充分提取样本的判别信息，将LPP 发展成为有监督方法。实验结果表明，该方法具有较好的
识别效果。-Feature Extraction in Face Recognition Technology is a fundamental and very important to find the characteristics of an effective solution to identify the crux of the problem is proposed in this paper a new facial feature extraction methods. This method is through the effective integration of tunable factor partial face manifold structure of information and samples of the types of information, the full extraction of the sample discriminant information, will be developed into a supervised LPP method. Experimental results show that the method has better recognition effect. Platform: |
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Author:张波 |
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Description: A new approach toward target representation and localization, the central component in visual tracking
of non-rigid objects, is proposed. The feature histogram based target representations are regularized
by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions
suitable for gradient-based optimization, hence, the target localization problem can be formulated using
the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya
coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the
presented tracking examples the new method successfully coped with camera motion, partial occlusions,
clutter, and target scale variations. Integration with motion filters and data association techniques is also
discussed. We describe only few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking . Platform: |
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Description: Magento 是一款新的专业开源电子商务平台，在设计上，包含相当全面，以模块化架构体系，让应用组合变得相当灵活，功能也相当丰富。其面向企业级应用，可处理更方面的需求，以及建设一个多种用途和适用面的电子商务网站。-Magento is a new professional open-source e-commerce platform, in the design, contains a fairly comprehensive, modular architecture system to allow the application of combinations become very flexible and feature rich. Its enterprise-class application-oriented, can handle more demand, as well as the construction of a multi-purpose and application of e-commerce sites face. Platform: |
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Author:chen |
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Description: 基于小波变换和神经网络的人脸识别：本论文围绕人脸识别问题对人脸特征提取及识别技术进行了研究。主要有：对人脸识别的研究工作进行了综述；在KL算法的基础上提出了新的基于KL的特征提取方法，克服了KL算法计算量大，计算时间长的缺点，-Based on Wavelet Transform and Neural Network Face Recognition: In this paper, issues surrounding the face recognition feature extraction and face recognition technology is studied. Mainly include: the research work on face recognition are reviewed in the KL algorithm is proposed based on the new KL-based feature extraction methods, the KL algorithm to overcome the large amount of computing time of shortcomings, Platform: |
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Author:hyh |
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Description: 基于PSO训练SVM的人脸识别
利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径-PSO-based SVM for face recognition training using support vector machine learning ability in the performance of good performance, combined with KPCA feature extraction method, applied to face recognition, the method in experiments to identify the performance of a good performance for the field of face recognition has provided a new way to identify Platform: |
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Author:彭伟 |
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Description: （压缩包里一共有5个代码）
pca+lda+粗糙集+模糊神经网络
saveORLimage.m将ORL人脸库分为测试集ptest和训练集pstudy存为imagedata.mat
1.savelda.m将人脸库先进行pca降维，再用lda进行特征提取，得到新的测试集ldatest和训练集ldastudy存为imageldadata.mat
2.对ldastudy进行离散化(discretimage.m)，得到离散化矩阵disdata,存入到imagedisdata.mat
3.将disdata组成决策表(savers.m)，通过对disdata的条件属性进行约简，得到其一个约简，组成新的测试集rstest和训练集rsstudy存为imagersdata.mat
4.对rsstudy进行模糊神经网络训练(savecul.m)，对模糊神经网络的参数进行调整学习将其存入culdata.mat
5.用runfnn.m对rstest进行测试得到其识别率
savem.m和cm.m是用最小距离分类器对训练集和测试集进行分类.-pca+ lda+ Rough Set+ fuzzy neural network
saveORLimage.m will ORL face database is divided into test set and training set ptest for pstudy keep imagedata.mat
Treasury will face 1.savelda.m first dimensionality reduction pca, lda used feature extraction, a new test set and training set ldatest for ldastudy keep imageldadata.mat
2. Ldastudy carried out on the discretization (discretimage.m), to be discrete matrix of disdata, deposited to imagedisdata.mat
3. Disdata the composition of the decision table (savers.m), the conditions on the attributes disdata about Jane, has been one of its reduction to form the new test set and training set rstest for rsstudy keep imagersdata.mat
4. Rsstudy training fuzzy neural network (savecul.m), on the parameters of fuzzy neural network to learn to adjust their deposit culdata.mat
5. Rstest used to test for runfnn.m by its recognition rate
cm.m is savem.m and minimum distance classifier on the training set and test set classificati Platform: |
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Author:dong |
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Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking. Platform: |
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Author:Ali |
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Description: This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method.-This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This is based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method. Platform: |
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Author:Swati |
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Description: Real-Time Facial Feature Point Extraction-Localization of facial feature points is an important step for many subsequent facial image analysis tasks. In this paper, we proposed a new coarse-to-fine method for extracting 20 facial feature points from image sequences. In particular, the Viola-Jones face detection method is extended to detect small-scale facial components with wide shape variations, and linear Kalman filters are used to smoothly track the feature points by handling detection errors and head rotations. The proposed method achieved higher than 90 detection rate when tested on the BioID face database and the FG-NET facial expression database. Moreover, our method shows robust performance against the variation of face resolutions and facial expressions. Platform: |
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Author:Ng Jack |
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Description: Abstract—This paper proposes a new technique for face detection
and lip feature extraction. A real-time field-programmable
gate array (FPGA) implementation of the two proposed techniques
is also presented. Face detection is based on a naive Bayes classifier
that classifies an edge-extracted representation of an image. Using
edge representation significantly reduces the model’s size to only
5184 B, which is 2417 times smaller than a comparable statistical
modeling technique, while achieving an 86.6 correct detection
rate under various lighting conditions. Lip feature extraction uses
the contrast around the lip contour to extract the height and width
of the mouth, metrics that are useful for speech filtering. The
proposed FPGA system occupies only 15 050 logic cells, or about
six times less than a current comparable FPGA face detection
system.-Abstract—This paper proposes a new technique for face detection
and lip feature extraction. A real-time field-programmable
gate array (FPGA) implementation of the two proposed techniques
is also presented. Face detection is based on a naive Bayes classifier
that classifies an edge-extracted representation of an image. Using
edge representation significantly reduces the model’s size to only
5184 B, which is 2417 times smaller than a comparable statistical
modeling technique, while achieving an 86.6 correct detection
rate under various lighting conditions. Lip feature extraction uses
the contrast around the lip contour to extract the height and width
of the mouth, metrics that are useful for speech filtering. The
proposed FPGA system occupies only 15 050 logic cells, or about
six times less than a current comparable FPGA face detection
system. Platform: |
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Author:ramanaidu |
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Description: 人脸识别技术是计算机模式识别领域非常活跃的研究课题，在法律、商业等领域有
着广泛的应用前景。自动人脸识别系统一般由两个模块组成：定位与检测模块，特征提
取与识别模块。本文对两个子模块进行了详细讨论，通过实验仿真了一个基于静态图像
的人脸识别系统。为提高系统的识别率，本文对定位检测模块和特征提取模块进行了深
入研究。
针对复杂多变人脸检测和定位问题，实现了一种基于对称特征的人脸定位方法。该
算法首先基于肽色特征提取出人脸区域，根据眼睛的颜色和梯度特征在肤色区找到眼睛
可能存在的有限的候选区域：根据人脸的对称特征，把这些候选区域分别进行匹配，找
到最佳匹配的区域就是眼睛区域。本算法适用于表情变化，姿态变化，有胡须，戴眼镜
的多种情况。特征提取是人脸识别系统中非常重要的技术，本文仿真的人脸识别系统采
用一种结合主元分析(PCA)和F．LDA的人脸识别方法。为了解决F．LDA直接应用到
高维模式识别中计算复杂度太大的问题，算法中首先应用主元分析进行降维。该算法能
克服LDA的小样本问题。-ne technology of face recognition is all active subject in the area of pattern recognition．
There are broad applications in the fields of law，business ere．A face recognition system
includes two parts：face detection and localization．feature extraction and classification,which
are discussed in detail．A face recognition system based on static image is simulated．In order
to improve the recognition rate，a new face detection and localization method and a new
feature extraction method are proposed． Platform: |
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Author:maple |
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Description: This paper presents a hybrid framework of feature extraction and hidden Markov modeling (HMM) for two-dimensional pattern recognition. Importantly, we explore a new discriminative training criterion to assure model compactness and discriminability. This criterion is derived from the hypothesis test theory via maximizing the confidence of accepting the hypothesis that observations are from target HMM states rather than competing HMM states. Accordingly, we develop the maximum confidence hidden Markov modeling (MC-HMM) for face recognition. Platform: |
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Author:rupesh |
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Description: 高独特性特征的选择以及合适匹配策略的选用是人脸识别技术的关键。讨论了基于仿射不变的几何特
征SIFT算子进行人脸识别的方法。SIFT算子的计算复杂度较高，并且不同的人脸表情和图像模糊会加大特征匹
配的难度。为克服上述缺点，提出了一种新的算法，将选择6个人脸上感兴趣子区域进行描述，并根据各自的独特
性赋予不同的权值，最后在匹配过程中使用相似度的平方来减小偏差数据造成的影响。实验结果表明，该方法能
有效减轻表情变化对于身份识别率急剧下降的影响，并可显著减少计算复杂度和特征匹配时间。-Choosing a distinctive feature and matching criterion is key to developing a reliable face recognition system．
This paper discusses the availability of one of geometric feature invariants，scale invariant feature transform (SIFT)
descriptor based face recognition． The SIFT feature description of an image is typically complex． In most cases， the
difficulty of feature matching problem is aggravated when the diferent face expressions and image blur exist． For
abovementioned issues，in this paper we proposes a new method that six interest sub—regions from the face are selected to be
described and later be calculated through diferent weights according to their distinctiveness．The square of the similarity is
used to solve the problem of data deviation．The experimental results demonstrate that our method does effectively moderate
the face expression efect． It also successfully reduces the complexity and matching time of SIFT feature sets． Platform: |
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Author:陈方芳 |
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Description: 人脸的研究是跨越人文科学与自然科学的新兴交叉研究领域，在最近几年得到了模式识别领域众多学者的重视，也取得了良好的研究成果。针对人脸这种生物特征的识别，在其过程中最为重要的一个环节是特征的提取，更好的提取出人脸的特征，将会使得识别更有有效和准确，提高分类的同时，也是的识别率有良好的提高。-Face recognition research is a new cross field across the humanities and natural science, had many scholars and pattern recognition fields in recent years, also achieved good results. According to the biological characteristics of human face recognition, during which the most important one link is feature extraction, extracted face feature better, will make the recognition more effectively and accurately, improve the classification at the same time, but also improve the recognition rate with good. Platform: |
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Author:张超杰 |
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Description: 由一个3D变形人脸模型取自动生成适应的训练样本。由统计视角，tailored训练数据保证了所有的数据变化且由任意的人脸属性丰富训练样本，例如，年龄或体重。更进一步，它可能自动适应到环境约束，例如，来自于监控摄像机的照明或视角约束。我们使用裁剪的（tailor）图象训练一个新的Viola Jones的adaboost 目标检测框架的多核实现。这个新的实现不仅快速的，而且多特征通道的使用成为可能，例如，在训练期间的颜色特征。在我们实验中，我们训练7个依赖视角的人脸检测子并在Face Detection Data Set 和 Benchmark (FDDB)中评估它们。- takes a look
into the automated generation of adaptive training samples
a 3D morphable face model. Using statistical insights,
the tailored training data guarantees full data variability
and is enriched by arbitrary facial attributes such as age
or body weight. Moreover, it can automatically adapt to
environmental constraints, such as illumination or viewing
angle of recorded video footage surveillance cameras.
We use the tailored imagery to train a new many-core implementation of Viola Jones’ AdaBoost object detection framework. The new implementation is not only faster but also
enables the use of multiple feature channels such as color
features at training time. Platform: |
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Author:郭继东 |
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Search - new feature face