| 1. | Firstly , we do video segmentation and key frame extraction
首先，进行视频分割和提取关键帧。 |
| 2. | Semantic extraction of video keyframe using support vector machines
基于支持向量机的视频关键帧语义提取 |
| 3. | The only difference is that , the vertex positions are stored only for key - frames
其主要区别是，它的顶点位置仅用来存储关键帧。 |
| 4. | Select the keyframe start and end range you want the animation you record to start and end on
选择你想记录的关键帧的开始和结束的范围 |
| 5. | An algorithm to extract key - frame from motion capture data is introduced in chapter 5
第五章介绍了我们提出的一种从运动捕捉数据中提取关键帧的算法。 |
| 6. | As soon as one key is present in a lattice , the buttons that are used to determine the resolution are blocked
一个关键帧应用于一个格子上，用于解决问题的按钮都会不可用。 |
| 7. | This property can be useful if you need to store lots of rotations in a file ( e . g . for a keyframed animation )
这个属性可有助于将你许多的旋转处理存储到文件中去（例如关键帧动画） 。 |
| 8. | Note that you will need to change the key - frame types to linear , if you want the lava ' s speed to be constant
（注意，你需要改变关键帧类型为直线，如果你希望岩浆的速度保持恒定的话。 ） |
| 9. | And also , the number of key frames can be slightly lesser than what it would have been with pure vertex based animation
因此，需要关键帧的数量较少一些，与基于顶点的动画而言它干净些。 |
| 10. | The key frames are detected using the motion features , color features and texture features of skin in key frame images
关键帧图象皮肤检测方法从皮肤的运动特征、颜色特征和纹理特征三个方面进行综合考量。 |
