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In this tutorial I ll teach you how to use three different texture filters. I ll teach you how to move an object using keys on the keyboard, and I ll also teach you how to apply simple lighting to your OpenGL scene. Lots covered in this tutorial, so
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用来对图像进行分类。Source code for Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework. Computer Vision and Pattern Recognition (CVPR) 2009，Li-Jia Li, Richard Socher and Li Fei-Fei. -Source code for Towards
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一个简单的骨骼动画人物行走的场景，有利于理解骨骼动画的原理-A simple skeletal animation characters walking scene, understanding the principle of skeletal animation
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基于预计算的交互式全局光照明研究 摘要 全局光照明效果为用户提供了虚拟场景中的物体形状、材质以及相互位置关系的重要信息，从而能够大大提高计算机生成图像的真实感。尽管在很多交互应用中都需要全局光照明效果，但过高的计算代价还是使得其一直局限于离线应用中。近年来，很多研究都试图将全局光照明集成到交互式应用中，例如光照明设计、虚拟现实系统以及电子游戏等等。为了降低运行时的计算开销，以提供交互级的绘制速度，一条主要的思路是引入预计算，并将一部分运行时的计算量前移到预计算阶段执行。 本学位论文研究通过预计算
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作者:Li-Jia Li, Richard Socher and Li Fei-Fei.
将分类 分割 注释结合起来处理场景分析的源代码-Source code for Towards Total Scene Understanding:Classification, Annotation and Segmentation
author:Li-Jia Li, Richard Socher and Li
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自己编的小波图像融合代码 直接matlab可以运行-----------------图像融合以图像作为研究和处理对象，是一种综合多个源图像信息的先进图像处理技术，它把对同一目标或场景的多重源图像根据需要通过一定的融合规则融合成为一幅新图像，在这一幅新图像中能反映多重源图像中的信息，以达到对目标或场景的综合描述，以及精确的分析判断，有效地提高图像信息的利用率、系统对目标探测识别的可靠性及系统的自动化程度。其目的是集成多个源图像中的冗余信息和互补信息，以强化图像中的可读信息、增加图像理解的可靠性等。
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一种通用的运动分割框架：独立的、铰接的、刚体的、非刚体的、退化的以及非退化的
相关PPT报告-Motion segmentation of trajectory data has been an essential issue in understanding and reconstructing dynamic scenes. Dynamic scene consists of multiple
moving objects with a static or moving camera
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Scene completion.This is a toy implementation of Scene Completion Using Millions of Photographs for 6.870 Object Recognition and Scene Understanding.-This implementation uses a of 54,000 256x256 images the Oliva lab for which we already have gist-b
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Since most current scene understanding approaches operate
either on the 2D image or using a surface-based representation, they do
not allow reasoning about the physical constraints within the 3D scene.
Inspired by the “Blocks World” work in the
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Image segmentation is among the most studied problems in image understanding and
computer vision. The goal of image segmentation is to partition the image plane into
a set of meaningful regions. Here meaningful typically refers to a semantic part
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