Design Specification
Introduction
Goals and Objectives
GameForge is a graphical tool used to aid in the design and creation of video games. It attempts to bring game development down to a level that any computer savvy user can understand, without requiring masterful programming ability. A user with limited Microsoft DirectX and/or Visual C++ programming knowledge will be able to construct a basic, 2-D arcade game. GameForge limits the amount of actual code written by the user, if not eliminating it completely. It will also assist experienced programmers in generating the Microsoft DirectX and Microsoft Windows9x overhead necessary for basic game construction, allowing them to concentrate on more detailed game design issues and implementation.
Three-dimensional real-time graphics are a prevalent technology nowadays, thanks
to the steady rise of processing power and specialized graphics hardware. One of the
goals of computer graphics most sought after and difficult to achieve is to reproduce realistic outdoor environments featuring dense forests.
Over the years, this bestselling guide has helped countless programmers learn how to support computer peripherals under the Linux operating system, and how to develop new hardware under Linux. Now, with this third edition, it s even more helpful, covering all the significant changes to Version 2.6 of the Linux kernel. Includes full-featured examples that programmers can compile and run without special hardware.
Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement formany computer vision applications. One of the key fac-ors in enhancing the robustness of dynamic scene analy-sis that of accurate and reliable means for shadow de-ection. Shadowdetectioniscriticalforcorrectobjectde-ection in image sequences. Many algorithms have beenproposed in the literature that deal with shadows. How-ever,acomparativeevaluationoftheexistingapproachesisstill lacking. In this paper, the full range of problems un-derlyingtheshadowdetectionareidenti?edanddiscussed.Weclassifytheproposedsolutionstothisproblemusingaaxonomyoffourmainclasses, calleddeterministicmodeland non-model based and statistical parametric and non-parametric. Novelquantitative(detectionanddiscrimina-ionaccuracy)andqualitativemetrics(sceneandobjectin-dependence,?exibilitytoshadowsituationsandrobustnesso noise) are proposed to evaluate these classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.
來自澳大利亞Qeensland大學(xué)的計(jì)算機(jī)視覺Matlab工具箱。
This Toolbox provides a number of functions that are useful in computer vision,
machine vision and related areas. It is a somewhat eclectic collection reflecting
the author s personal interest in areas of photometry, photogrammetry, colorimetry. It
covers functions such as image file reading and writing, filtering, segmentation,
feature extraction, camera calibration, camera exterior orientation, display,
color space conversion and blackbody radiators. The Toolbox, combined
with MATLAB and a modern workstation computer, is a useful and convenient
environment for investigation of machine vision algorithms. It is possible to use
MEX files to interface with image acquisition hardware ranging from simple
framegrabbers to Datacube servers.
This handbook presents a thorough overview in 45 chapters from more than 100 renowned experts in the field. It provides the tools to help overcome the problems of video storage, cataloging, and retrieval, by exploring content standardization and other content classification and analysis methods. The challenge of these complex problems make this book a must-have for video database practitioners in the fields of image and video processing, computer vision, multimedia systems, data mining, and many other diverse disciplines. Topics include video segmentation and summarization, archiving and retrieval, and modeling and representation.
penMesh is a generic and efficient data structure for representing and manipulating polygonal meshes. OpenMesh is developed at the Computer Graphics Group, RWTH Aachen , as part of the OpenSGPlus project, is funded by the German Ministry for Research and Education ( BMBF), and will serve as geometry kernel upon which the so-called high level primitives (e.g. subdivision surfaces or progressive meshes) of OpenSGPlus are built.
It was designed with the following goals in mind :
Flexibility : provide a basis for many different algorithms without the need for adaptation.
Efficiency : maximize time efficiency while keeping memory usage as low as possible.
Ease of use : wrap complex internal structure in an easy-to-use interface.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
Some algorithms of variable step size LMS adaptive filtering are studied．The VS—LMS algorithm is improved．
Another new non-linear function between肛and e(／ t)is established．The theoretic analysis and computer
simulation results show that this algorithm converges more quickly than the origina1．Furthermore，better antinoise
property is exhibited under Low—SNR environment than the original one．
