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Patent Agency Ranking
公开(公告)号：US20090080774A1
公开(公告)日：2009-03-26
申请号：US11860428
申请日：2007-09-24
Applicant: Zhouchen Lin , Guangcan Liu , Xiaoou Tang
Inventor： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
IPC: G06K9/34
Abstract: This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background.
Abstract translation: 本公开描述了用于无人监督的对象分割的集成框架。 通过以集成的方式使用算法将自上而下的约束和自下而上的对象形状约束集成在一起，进行类无监督对象分割。 该算法描述了对象部分和超像素之间的关系。 该过程通过对象部分形成对象形状，并将像素图像监视到超像素中，该算法与约束相结合。 本公开描述了从混合图计算掩模图，将图像分割成前景对象和背景，以及从背景显示前景对象。
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公开(公告)号：US20110206276A1
公开(公告)日：2011-08-25
申请号：US13100891
申请日：2011-05-04
Applicant: Zhouchen Lin , Guangcan Liu , Xiaoou Tang
Inventor： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
IPC: G06K9/34
Abstract: This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background.
Abstract translation: 本公开描述了用于无人监督的对象分割的集成框架。 通过以集成的方式使用算法将自上而下的约束和自下而上的对象形状约束集成在一起，进行类无监督对象分割。 该算法描述了对象部分和超像素之间的关系。 该过程通过对象部分形成对象形状，并将像素图像监视到超像素中，该算法与约束相结合。 本公开描述了从混合图计算掩模图，将图像分割成前景对象和背景，以及从背景显示前景对象。
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公开(公告)号：US08238660B2
公开(公告)日：2012-08-07
申请号：US13100891
申请日：2011-05-04
Applicant: Zhouchen Lin , Guangcan Liu , Xiaoou Tang
Inventor： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
Abstract: This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background.
Abstract translation: 本公开描述了用于无人监督的对象分割的集成框架。 通过以集成的方式使用算法将自上而下的约束和自下而上的对象形状约束集成在一起，进行类无监督对象分割。 该算法描述了对象部分和超像素之间的关系。 该过程通过对象部分形成对象形状，并将像素图像监视到超像素中，该算法与约束相结合。 本公开描述了从混合图计算掩模图，将图像分割成前景对象和背景，以及从背景显示前景对象。
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公开(公告)号：US07995841B2
公开(公告)日：2011-08-09
申请号：US11860428
申请日：2007-09-24
Applicant: Zhouchen Lin , Guangcan Liu , Xiaoou Tang
Inventor： Zhouchen Lin , Guangcan Liu , Xiaoou Tang
Abstract: This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background.
Abstract translation: 本公开描述了用于无人监督的对象分割的集成框架。 通过以集成的方式使用算法将自上而下的约束和自下而上的对象形状约束集成在一起，进行类无监督对象分割。 该算法描述了对象部分和超像素之间的关系。 该过程通过对象部分形成对象形状，并将像素图像监视到超像素中，该算法与约束相结合。 本公开描述了从混合图计算掩模图，将图像分割成前景对象和背景，以及从背景显示前景对象。
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5.发明申请公开(公告)号：US20100067799A1
公开(公告)日：2010-03-18
申请号：US12212222
申请日：2008-09-17
Applicant: Guangcan Liu , Zhouchen Lin , Xiaoou Tang
Inventor： Guangcan Liu , Zhouchen Lin , Xiaoou Tang
IPC: G06K9/46
CPC classification number: G06K9/4647
Abstract: A “globally invariant Radon feature transform,” or “GIRFT,” generates feature descriptors that are both globally affine invariant and illumination invariant. These feature descriptors effectively handle intra-class variations resulting from geometric transformations and illumination changes to provide robust texture classification. In general, GIRFT considers images globally to extract global features that are less sensitive to large variations of material in local regions. Geometric affine transformation invariance and illumination invariance is achieved by converting original pixel represented images into Radon-pixel images by using a Radon Transform. Canonical projection of the Radon-pixel image into a quotient space is then performed using Radon-pixel pairs to produce affine invariant feature descriptors. Illumination invariance of the resulting feature descriptors is then achieved by defining an illumination invariant distance metric on the feature space of each feature descriptor.
Abstract translation: “全局不变的氡特征变换”或“GIRFT”产生全局仿射不变和照明不变的特征描述符。 这些特征描述符有效地处理由几何变换和照明变化产生的类内变化，以提供鲁棒的纹理分类。 一般来说，GIRFT在全球范围内考虑图像，以提取对本地区域的大量材料较不敏感的全局特征。 通过使用Radon变换将原始像素表示的图像转换为氡像素图像来实现几何仿射变换不变性和照度不变性。 然后使用氡 - 像素对执行氡像素图像到商空间的规范投影，以产生仿射不变特征描述符。 然后通过在每个特征描述符的特征空间上定义照明不变距离度量来实现所得特征描述符的照明不变性。
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