AAM方法介绍

主动表观模型,电子工程学院 020751班 陶明亮,Active Appearance Models，AAM,Overview,1.Background,2.Basic thought,3.Search process,4.Experimental results,5.Conclusion and extensions,特征点定位,Interpretation:不仅仅是了解图像的结构，更重要的是理解图像表达的是什么,Basic thought,Iterative method of matching model to image treat interpretation as an optimisation problem difference vector minimise the magnitude by varying the model parameters(50-100) diffcult high-dimensional optimisation problem,standard methods： rather slow and liable to become trapped in local minima,Basic thought,Place model in image,Measure Difference,Update Model,Iterate,learning the relationship between I and the error in the model parameters c,Basic thought,Training set of facial images corresponding landmark points a statistical model of the shape variation a model of the texture variation a model of the correlations between shape and texture,Shape,What is shape? Geometric information that remains when location, scale and rotational effects removed (Kendall),Same Shape,Different Shape,Shape,More generally Shape is the geometric information invariant to a particular class of transformations Transformations: Similarity (translation+rotation+scaling) Affine,texture,What is texture? the pattern of intensities or colors across an image patch.,Basic thought,shape vector and texture vector,Shape, x = (x1,y1, , xn, yn)T,Texture, g,Warp to mean shape,Eigen-analysis，PCA,Basic thought,由控制参数矢量c控制形状(shape)和纹理(texture)的变化， 调节c可以得到不同的形状和脸部纹理，从而合成不同的人脸,调节c可以合成不同的人脸,形状(shape),脸部纹理(texture),轮廓变化模式矩阵,脸部纹理变化模式矩阵,Multi-resolution model,level 0 - original image. level 1 Smooth image at level L-1 with gaussian filter Sub-sample every other pixel ,Each level half the size of the one below,Multi-resolution model,Train models at each level of pyramid use the same set of landmarks and the same shape model given the global shape model and the particular texture model,Multi-resolution model,Start at coarse resolution For each resolution Search along profiles for best matches Update parameters to fit matches (Apply constraints to parameters) Until converge at this resolution refine at finer resolution,improved speed and robustness,Search process,精细定位,粗略定位,原图像,得到粗 略定位,不断调整C，使 合成人脸的灰度 与图像灰度差达 到最小值,得到 精细 定位,由计算公式 可得各特征 点的位置,对每一个比例、 位置、旋转角度 进行迭代搜索,Experimental results,a training set of 400 images of faces each labeled with 68 points around the main features sampled approximately 10,000 intensity values from the facial region,Experimental results,generated an appearance model required 55 parameters to explain 95% of the observed variation.,Effect of varying first four facial appearance model parameters, c1 -c4,by +(-)3 standard deviations from the mean.,Experimental results,Experimental results,quantitative evaluation(定量分析） trained a model on 100 hand-labeled face images(200 pixels wide) tested it on a different set of 100 labeled images A variety of different people and expressions were included. displaced model from true position by 10% and changed its scale by 10 % ran the multi-resolution search, starting with the mean appearance parameters .,Experimental results,the proportion of 100 multi-resolution searches which converged correctly,Mean intensity error per pixel as search progresses （initially displaced by 5 percent of the face width）,Conclusions,Advantages: fast, accurate and reliable makes use of learned correlation between errors in model parameters and the resulting residual texture errors,Disadvantage： the method cannot be used for tree like structures with varying numbers of branches, but can be used for various organs which exhibit shape variation but not a change in topology(拓扑） a reasonable initial starting position is required,Conclusions,Extensions,Extend to color images sampling each color(R,G,B) at each sample point Extend to 3D images many more modes is required Difficult to obtain well annotated training data the definition of surfaces and 3D topology is more complex,3D Models,=,+,Mesh,Texture,3D face,参考文献,1 Timothy F.Cootes,Gareth J.Edwards, and Chrisopher J.Taylor,Active Appearance Models, IEEE Transactions on pattern analysis and machine intelligence, VOL. 23, NO. 6, June,2001 2 Timothy F.Cootes and Chrisopher J.Taylor ,St