Multiple descent cost competitive learning and data-compressed 3-D morphing
Multiple descent cost competitive learning and data-compressed 3-D morphing
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DOI：
10.1109/iconip.1999.844017
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发表时间：
1999-11
期刊：
影响因子：
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通讯作者：
Y. Matsuyama;T. Shimazu;G. Matsuo;T. Arisaka
中科院分区：
文献类型：
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作者：
Y. Matsuyama;T. Shimazu;G. Matsuo;T. Arisaka
将多次下降成本竞争学习应用于三维图像处理和图形的数据压缩纹理生成。这种学习方法通过生成两种类型的特征图来组织自己：分组特征图和权重向量特征图，这两种特征图都可以改变区域形状。这一优点使得用户可以生成数据压缩图像变形。生成的纹理可用于创建虚拟3D对象。给出了产生情感表达的例子。阐明了/spl alpha/-EM（期望最大化）算法与多重下降成本竞争学习算法之间的理论关系。
Multiple descent cost competitive learning is applied to data-compressed texture generation for 3D image processing and graphics. This learning method organizes itself by generating two types of feature maps: the grouping feature map and the weight vector feature map, which can both change regional shapes. This merit makes it possible for users to generate data-compressed image morphing. The resulting textures can be used to create virtual 3D objects. Examples are given of generating emotional expressions. The theoretical relationship between the /spl alpha/-EM (expectation maximization) algorithm and the multiple descent cost competitive learning algorithm is also clarified.