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Patent Agency Ranking
公开(公告)号：US09208567B2
公开(公告)日：2015-12-08
申请号：US13909838
申请日：2013-06-04
Applicant: Apple Inc.
Inventor： Jan Erik Solem , Jerome Piovano , Michael Rousson
CPC classification number: G06T7/0046 , G06K9/00281 , G06K9/469 , G06K9/621 , G06T7/75 , G06T2207/20081 , G06T2207/30201
Abstract: Techniques are provided to improve the performance and accuracy of landmark point detection using a Constrained Local Model. The accuracy of feature filters used by the model may be improved by supplying positive and negative sets of image data from training image regions of varying shapes and sizes to a linear support vector machine training algorithm. The size and shape of regions within which a feature filter is to be applied may be determined based on a variance in training image data for a landmark point with which the feature filter is associated. A sample image may be normalized and a confidence map generated for each landmark point by applying the feature filters as a convolution on the normalized image. A vector flow map may be pre-computed to improve the efficiency with which a mean landmark point is adjusted toward a corresponding landmark point in a sample image.
Abstract translation: 提供了技术来提高使用约束局部模型的地标点检测的性能和准确性。 模型使用的特征滤波器的精度可以通过从不同形状和尺寸的训练图像区域向线性支持向量机训练算法提供正和负的图像数据组来提高。 可以基于与特征滤波器相关联的地标点的训练图像数据的方差来确定要应用特征滤波器的区域的大小和形状。 可以对样本图像进行归一化，并通过将特征滤波器作为在归一化图像上的卷积应用来为每个地标点生成置信图。 可以预先计算矢量流程图，以提高将样本图像中的平均地标点调整到对应的地标点的效率。
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公开(公告)号：US10523894B2
公开(公告)日：2019-12-31
申请号：US15266460
申请日：2016-09-15
Applicant: Apple Inc.
Inventor： Brett Keating , Vincent Wong , Todd Sachs , Claus Molgaard , Michael Rousson , Elliott Harris , Justin Titi , Karl Hsu , Jeff Brasket , Marco Zuliani
Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.
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3.发明申请公开(公告)号：US20150071547A1
公开(公告)日：2015-03-12
申请号：US14021857
申请日：2013-09-09
Applicant: Apple Inc.
Inventor： Brett Keating , Vincent Wong , Todd Sachs , Claus Molgaard , Michael Rousson , Elliott Harris , Justin Titi , Karl Hsu , Jeff Brasket , Marco Zuliani
CPC classification number: H04N5/77 , G06K9/00765 , G06K9/46 , G06K9/6201 , G06K9/6267 , G06K2009/4666 , G06T7/11 , H04N5/147 , H04N5/23219 , H04N5/23222 , H04N5/23229 , H04N5/23293 , H04N21/8456
Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.
Abstract translation: 描述了用于改善从一组共同拍摄的图像中保持图像的自动选择的系统和方法。 可以使用图像类型识别和图像质量度量的组合来识别该集合中的一个或多个图像作为保持器图像。 可以使用图像类型识别来将捕获的图像分类为例如三个或更多个类别。 类别可以包括纵向，动作或“其他”。根据所识别的类别，可以不同地分析图像以识别守护者图像。 对于肖像图像，可以使用操作来识别最佳面部组。 对于动作图像，该集合可以被划分为部分，使得从每个部分中选择的守护者图像讲述动作的故事。 对于“其他”类别，可以分析图像，以便选择对于所识别的感兴趣区域具有较高质量度量的图像。
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4.发明申请公开(公告)号：US20170006251A1
公开(公告)日：2017-01-05
申请号：US15266460
申请日：2016-09-15
Applicant: Apple Inc.
Inventor： Brett Keating , Vincent Wong , Todd Sachs , Claus Molgaard , Michael Rousson , Elliott Harris , Justin Titi , Karl Hsu , Jeff Brasket , Marco Zuliani
Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.
Abstract translation: 描述了用于改善从一组共同拍摄的图像中保持图像的自动选择的系统和方法。 可以使用图像类型识别和图像质量度量的组合来识别该集合中的一个或多个图像作为保持器图像。 可以使用图像类型识别来将捕获的图像分类为例如三个或更多个类别。 类别可以包括纵向，动作或“其他”。根据所识别的类别，可以不同地分析图像以识别守护者图像。 对于肖像图像，可以使用操作来识别最佳面部组。 对于动作图像，该集合可以被划分为部分，使得从每个部分中选择的守护者图像讲述动作的故事。 对于“其他”类别，可以分析图像，以便选择对于所识别的感兴趣区域具有较高质量度量的图像。
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公开(公告)号：US20140355821A1
公开(公告)日：2014-12-04
申请号：US13909838
申请日：2013-06-04
Applicant: Apple Inc.
Inventor： Jan Erik Solem , Jerome Piovano , Michael Rousson
CPC classification number: G06T7/0046 , G06K9/00281 , G06K9/469 , G06K9/621 , G06T7/75 , G06T2207/20081 , G06T2207/30201
Abstract: Techniques are provided to improve the performance and accuracy of landmark point detection using a Constrained Local Model. The accuracy of feature filters used by the model may be improved by supplying positive and negative sets of image data from training image regions of varying shapes and sizes to a linear support vector machine training algorithm. The size and shape of regions within which a feature filter is to be applied may be determined based on a variance in training image data for a landmark point with which the feature filter is associated. A sample image may be normalized and a confidence map generated for each landmark point by applying the feature filters as a convolution on the normalized image. A vector flow map may be pre-computed to improve the efficiency with which a mean landmark point is adjusted toward a corresponding landmark point in a sample image.
Abstract translation: 提供了技术来提高使用约束局部模型的地标点检测的性能和准确性。 模型使用的特征滤波器的精度可以通过从不同形状和尺寸的训练图像区域向线性支持向量机训练算法提供正和负的图像数据组来提高。 可以基于与特征滤波器相关联的地标点的训练图像数据的方差来确定要应用特征滤波器的区域的大小和形状。 可以对样本图像进行归一化，并通过将特征滤波器作为在归一化图像上的卷积应用来为每个地标点生成置信图。 可以预先计算矢量流程图，以提高将样本图像中的平均地标点调整到对应的地标点的效率。
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