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Alternative TitleKey Techniques Discussion for Small Nature Scene Aumented Reality
Thesis Advisor王阳生
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword增强现实 物体识别 特征点匹配 最小二乘法 随机采样一致性 视觉注意 特征点跟踪 Augmented Reality Object Recongnition Point Matching Least Squares Ransac Visual Attention Points Tracking
Abstract增强现实自从作为虚拟现实技术的一个分支形成以来,一直是计算机视觉和计算机图形学的研究热点。其中基于自然小场景的增强现实技术在军事、工业制造、医疗卫生、教育娱乐、广告设计、市政建设等方面有着广泛的应用前景。本文主要围绕自然小场景的增强现实技术进行讨论,着重针对场景自动初始化,光照变化和场景部分遮挡情况下的虚实融合稳定性进行研究,主要工作和贡献包括: (1)提出了一种基于几何的双视图三维重建方法。基本思路是先去除旋转,只保留摄像机平移引起的像素偏移,分解三维平移为二维平面移动,根据三角形相似原理获得深度信息与平移距离的比例关系,最后给出显式重建公式。实验表明:给定同样的输入,几何法在重建精度上与传统的方程求解法基本一致。在增强现实应用方面,本技术为基于特征点的未知场景自动初始化提供了一种快捷方法。 (2)提出了一种基于视觉注意机制的实时嘈杂背景物体初识别方法。首先,图像在多尺度上进行信息融合形成一张视觉注意区域标记图像;接着,采用均值聚类法大致估计出图像的视觉注意区域;最后,基于颜色的相关系数方法用于区别出图像视觉注意区域的物体。实验结果表明本文提出的方法能实时地从嘈杂背景中鉴别出物体的大概范围,该方法为基于模型的增强现实初始化提供了一种初定位的思路。 (3)在遮挡和光照条件剧烈变化的情况下,图像特征点初匹配率比较低,导致后续排除错误匹配非常耗时。针对这种情况,提出了一种基于特征点邻域统计的可靠匹配对筛选模型,其基本依据是:如果某初匹配对的邻域存在更多的匹配对,则此初匹配对是正确匹配的可能性就更大。我们讨论了常见特征点分布密度情况下的可靠匹配筛选模型,并在特征点初匹配率低的情况下验证模型的可靠性。实验结果表明该模型对于提取可靠匹配对是有效的,结合RANSAC方法能够很大程度降低排除误匹配时间。本技术为增强现实取得鲁棒的虚实融合效果,在前期阶段,即特征匹配阶段争取了时间。 (4)提出了一种从杂乱数据进行模型拟合的方法:异类逐个排除法。思路是每次从集合中删除最可能是异类的元素,直到满足模型精度要求或者集合中没有足够元素剩余为止。集合中某元素是否是异类的判断根据是该元素到其余元素均值的距离,距离越大,则该元素是异类的概率就越大。我们将其应用于特征点初匹配和跟踪后的错误匹配排除。结果表明:本方法能够有效地去除集合中的异类元素,同时完成模型拟合。对于增强现实应用,本技术能有效地提高虚实融合的鲁棒性。 总的说来,本文在自然小场景增强现实场景自动初始化,光照变化和场景部分遮挡情况下的虚实融合稳定性进行了一些有益的尝试和探索,并根据前面提出的方法搭建了基于视频的自然小场景增强现实系统,取得了一些初步成果,希望本文的结果为后续的研究提供一定借鉴和启发。
Other AbstractAugmented Reality(AR) is a challenging topic in computer graphics and computer vision, in which AR techniques for small nature scenes are widely used in military, manufacturing, medical, entertainment,advertisement and cultural construction. This paper focuses on small nature scenes AR techniques, mainly discuses about automatic initiation methods, robust augmentation technique under illumination change and partial occlusion. Our contributions are: 1) We present a complete geometry reconstruction method from two views. In computer vision, given two images and corresponding cameras' inner parameters and poses, depth of points in 3D scene can be obtained by solving equations. Being different from solving equations, this paper presents a complete geometry method for 3D points' depth estimation. Its key idea is eliminating the parallax caused by rotation between two cameras, and then breaking down 3D translation to 2D move. The distance of 3D points to camera is obtained by similar principle of triangles in 2D planes. Experiments show that geometry method gives as satisfactory reconstructions results as those of least squares on precision. In some applications such as embedded development for robot stereo vision, our method has advantage for its computing and storage costs are economical. 2) A real-time framework for objects cursory recognition in cluster scene based on visual attention is introduced. First, multi-scale image features are combined into a single saliency map. Then, k-means method is used to estimate the position of objects from cluster scene by saliency map. Finally, we construct global color feature vector for saliency regions and recognize the objects by their correlation coefficients from patterns. Results show that this framework is efficient for objects cursory recognition in random cluster scene. 3) Outlier rejection is a challenging topic in computer vision, especially where illumination change or partial occlusion happen in real-time image matching. This paper presents a mechanism for reliable match selection from ambiguous match which are obtained by invariant feature operator. Its basic idea is that if one pair is a good match, then many matches will be expected to see in their neighbors. We use this mechanism for high reliable point selection and compare our results with those of classical method. In the last part of this paper, we combine our method with RANSAC for outlier rejection in point match. Experiences show that our met...
Other Identifier200618014629084
Document Type学位论文
Recommended Citation
GB/T 7714
杨明浩. 自然小场景增强现实关键技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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