Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay.
IEEE Trans Image Process. 2010 Mar;19(3):625-35. doi: 10.1109/TIP.2009.2038759. Epub 2009 Dec 18.
Shape models (SMs), capturing the common features of a set of training shapes, represent a new incoming object based on its projection onto the corresponding model. Given a set of learned SMs representing different objects classes, and an image with a new shape, this work introduces a joint classification-segmentation framework with a twofold goal. First, to automatically select the SM that best represents the object, and second, to accurately segment the image taking into account both the image information and the features and variations learned from the online selected model. A new energy functional is introduced that simultaneously accomplishes both goals. Model selection is performed based on a shape similarity measure, online determining which model to use at each iteration of the steepest descent minimization, allowing for model switching and adaptation to the data. High-order SMs are used in order to deal with very similar object classes and natural variability within them. Position and transformation invariance is included as part of the modeling as well. The presentation of the framework is complemented with examples for the difficult task of simultaneously classifying and segmenting closely related shapes, such as stages of human activities, in images with severe occlusions.
形状模型（SM），通过捕获一组训练形状的公共特征，基于其在对应模型上的投影来表示一个新的传入物体。给定一组表示不同物体类别的学习过的 SM 和一张具有新形状的图像，这项工作引入了一个联合分类-分割框架，具有双重目标。首先，自动选择最能代表物体的 SM，其次，考虑到图像信息以及从在线选择的模型中学习到的特征和变化，准确地分割图像。引入了一个新的能量函数，同时实现了这两个目标。基于形状相似性度量进行模型选择，在线确定在梯度下降最小化的每次迭代中使用哪个模型，允许模型切换和适应数据。使用高阶 SM 来处理非常相似的物体类和其中的自然变化。位置和变换不变性也被包含在建模中。框架的介绍通过同时对严重遮挡的图像中的相关形状（如人类活动的各个阶段）进行分类和分割这一困难任务的示例进行补充。
