A better image segmentation method based on optical flow 基于光流场的图像分割
The method based on optical flow fields is a powerful one to vision motion analysis 基于光流场的研究方法是视觉运动分析的有效手段之一。
At first , this thesis provides a face tracking algorithm based on robust optical flow 首先,本文提出了一个基于鲁棒性光流场的人脸跟踪算法。
Additionally , they provide a great deal of information about the structure of the scene 本文在基于直线光流场重建3d运动和结构方面进行了大量的工作。
The main ways of visual motion analysis can be classified into feature based and optical flow based 视觉运动分析分为基于特征的方法和基于光流场的方法。
Experimental results show that the algorithm can provide a good estimation of the 3d motion and structure 采用好的计算模型能使光流场与3d物体运动和结构之间存在一种较简单的非线性关系。
This dissertation concentrates on how to estimate optical flow and reconstruct 3d structure and motion from image sequences 本论文主要研究图像光流场计算方法以及由光流场重建三维运动和结构的计算理论与方法。
This thesis adopts a pyramid structure image registration method which is based on the optical flow , since it can provide a good registration for big shifted image 本文采用了基于金字塔结构的光流场运动估计,因为它能很好地对大位移的图像进行精确的配准。
In the thesis , the algorithm of background subtracting , frame difference algorithm , motion estimation based on optical flow field and object tracking based on active shape contour are investigated 本文研究了背景相减算法、差分法、基于光流场的运动估计和基于主动形状模型的目标跟踪算法。
We present an algorithm of motion without movement to simulate swaying of aquatic in water , and improve the image quality of the motion aiming at the damage to the image quality 我们采用基于图像相位变化的动画算法,结合光流场两幅图像之间实现动画,针对算法对图像质量的损害,对图像后期处理,提高了动画的图像质量。