Face Transfer is a method for mapping videorecorded perfor-mances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or 鏗乴m footage.
Video-DVM is a very cheap DVM that shows how an output as complex as a videocomposite signal can be generated entirely in software: two I/O pins and three resistors are all the hardware required. Connected to any TV set it displays voltages, included max and min peaks, using both giant digits and an analog bar-display . A serial data output for computer data logging is provided, too.
If a tree falls in the forest, and there s nobody there to hear, does it make a sound? This classic conundrum was coined by George Berkeley (1685-1753), the Bishop and influential Irish philosopher whose primary philosophical achievement is the advancement of what has come to be called subjective idealism. He wrote a number of works, of which the most widely-read are Treatise Concerning the Principles of Human Knowledge (1710) and Three Dialogues between Hylas and Philonous (1713) (Philonous, the "lover of the mind," representing Berkeley himself).
As I write this foreword, I am collaborating with four leading user interface
(UI) component vendors on a presentation for the 2004 JavaOneSM conference.
In our presentation, the vendors will show how they leverage JavaServerTM
Faces technology in their products. While developing the presentation, I am
learning some things about the work we’ve been doing on JavaServer Faces for
the past three years. The vendors have their own set of concerns unique to
adapting their product for JavaServer Faces, but they all voice one opinion
loud and clear: they are very relieved to finally have a standard for web-based
user interfaces.
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.
Objectives
The purpose of this notebook is to give you a brief introduction to the
DiscreteWavelets Toolbox and show you how to use it to load
images. Some basic image manipulation is illustrated as well. You will
also learn how to use measures and tools such as cumulative energy,
entropy, PSNR, and Huffman coding.
Help on the DiscreteWavelets Toolbox
Help for the toolbox is available by clicking on Help and then Product
Help (or press F1) and then clicking on the DiscreteWavelets Toolbox.
Several demos and examples are available as well by clicking on the Demos
tab on the Help menu.
Image Basics
The DiscreteWavelets Toolbox comes with 18 grayscale images and 9 color
images for you to use. There are three functions available to tell you more about these images.
The first function is called |ImageList|. This function can tell you the
names and sizes of the digital images in the Toolbox.
KML 2.0介紹 KML全稱是Keyhole Markup Language KML，是一個基于XML語法和文件格式的文件，用來描述和保存地理信息如點、線、圖片、折線并在Google Earth客戶端之中顯示
The core of the project is the KMLCreator.cs. This has three classes, KMLCoordinates, KMLPoint and KMLLine
Because WDM networks are circuit switched loss networks blocking may occur because of lack of resources. Also in circuit switched networks many paths use the same links. This toolbox answers the question how different paths with different loads influence on each other and what is the blocking on each of the defined path. Toolbox is capable of computing blocking for three different WDM network types: with no wavelength conversion, with full wavelength conversion and with limited range wavelength conversion. It is worth noting that case for full conversion can be usefull for any circuit switched network without additional constraints (i.e. wavelength continuity constraint in WDM), for example telephone network.
Toolbox contains also scripts for defining network structures (random networks, user defined networks) and traffic matrixes. Three graph algorithms for shortest path computation are also in this toolbox (they are used for traffic matrix creation).
This article discusses some issues that a typical Windows C++ programmer will encounter when approaching
Symbian OS for the first time. Our experience in developing for three successive versions of Symbian OS has
given us considerable perspective on what can be difficult when working in this otherwise rich and stable
environment. While one reason for Symbian s success may be the desire of many mobile phone manufacturers not
to be tied to Microsoft, the other reason is that Symbian has put together a lightweight, elegant system that
succeeds in providing a very impressive range of functionality. Here are some pointers to help ease the transition to
successful Symbian OS application development.
ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, simpler network structures and
faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
