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    • 1. 发明授权
    • Shape priors for level set representations
    • 形成级别表示的先验
    • US07177471B2
    • 2007-02-13
    • US10356093
    • 2003-01-31
    • Nikolaos ParaglosMikael Rousson
    • Nikolaos ParaglosMikael Rousson
    • G06K9/46
    • G06K9/00362G06K9/48G06K9/6206G06T7/12G06T7/149
    • This invention relates to shape priors for level set representations. An embodiment of the invention comprises a first stage and a second stage. In the first stage, a shape model can be built directly on level set space using a collection of samples. The shape model can be constructed using a variational framework to create a non-stationary pixel-wise model that accounts for shape variabilities. Then, in the second stage, the shape model can be used as basis to introduce the shape prior in an energetic form. In terms of level set representations, the shape prior aims at minimizing non-stationary distance between the evolving interface and the shape model. An embodiment according to the present invention can be integrated with an existing, data-driven variational method to perform image segmentation for physically corrupted and incomplete data.
    • 本发明涉及水平集表示的形状先验。 本发明的实施例包括第一阶段和第二阶段。 在第一阶段,可以使用样本集合直接在级别集空间上构建形状模型。 可以使用变分框架来构建形状模型,以创建考虑到形状变异性的非平稳像素模型。 然后,在第二阶段中,形状模型可以用作以能量形式引入形状的基础。 在水平集表示方面,先前的形状旨在最小化不断变化的界面和形状模型之间的非固定距离。 根据本发明的实施例可以与现有的数据驱动变分方法集成,以对物理损坏和不完整的数据执行图像分割。
    • 5. 发明授权
    • Efficient segmentation of piecewise smooth images
    • 分段平滑图像的高效分割
    • US07889941B2
    • 2011-02-15
    • US11696869
    • 2007-04-05
    • Jerome PiovanoMikael Rousson
    • Jerome PiovanoMikael Rousson
    • G06K9/44
    • G06T7/149G06T7/11G06T2207/20161
    • A fast and robust segmentation model for piecewise smooth images is provided. Local statistics in an energy formulation are provided as a functional. The shape gradient of this new functional gives a contour evolution controlled by local averaging of image intensities inside and outside the contour. Fast computation is realized by expressing terms as the result of convolutions implemented via recursive filters. Results are similar to the general Mumford-Shah model but realized faster without having to solve a Poisson partial differential equation at each iteration. Examples are provided. A system to implement segmentation methods is also provided.
    • 提供了一种用于分段平滑图像的快速且鲁棒的分割模型。 提供能量公式中的本地统计作为功能。 这种新功能的形状梯度给出了通过轮廓内部和外部的图像强度的局部平均来控制的轮廓演化。 通过表达术语作为通过递归滤波器实现的卷积的结果来实现快速计算。 结果与一般的Mumford-Shah模型相似,但实现得更快,而无需在每次迭代中解决泊松偏微分方程。 提供了实例。 还提供了一种实现分割方法的系统。
    • 8. 发明授权
    • Method of computing global-to-local metrics for recognition
    • 计算用于识别的全局到本地度量的方法
    • US08488873B2
    • 2013-07-16
    • US12574717
    • 2009-10-07
    • Mikael RoussonJan Erik SolemJerome Piovano
    • Mikael RoussonJan Erik SolemJerome Piovano
    • G06K9/62G06K9/00
    • G06K9/6215G06N99/005
    • A method of computing global-to-local metrics for recognition. Based on training examples with feature representations, the method automatically computes a local metric that varies over the space of feature representations to optimize discrimination and the performance of recognition systems.Given a set of points in an arbitrary features space, local metrics are learned in a hierarchical manner that give low distances between points of same class and high distances between points of different classes. Rather than considering a global metric, a class-based metric or a point-based metric, the proposed invention applies successive clustering to the data and associates a metric to each one of the clusters.
    • 计算用于识别的全局到本地度量的方法。 基于具有特征表示的训练示例,该方法自动计算在特征表示空间上变化的局部度量,以优化识别系统的识别和性能。 给定任意特征空间中的一组点,以分级方式学习局部度量,这样可以在不同类别的点之间提供相同类别的点和高距离之间的较低距离。 所提出的发明不是考虑全局度量,基于类的度量或基于点的度量,而是将连续的聚类应用于数据并将度量与每个集群相关联。