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    • 5. 发明申请
    • METHOD, APPARATUS, SYSTEM AND COMPUTER PROGRAM PRODUCT FOR DEPTH-RELATED INFORMATION PROPAGATION
    • 方法,装置,系统和计算机程序产品深度相关信息传播
    • WO2008152607A1
    • 2008-12-18
    • PCT/IB2008/052340
    • 2008-06-13
    • KONINKLIJKE PHILIPS ELECTRONICS N.V.PHILIPS INTELLECTUAL PROPERTY & STANDARDS GMBHPHILOMIN, VasanthLIU, FangSHEN, Chunfeng
    • PHILOMIN, VasanthLIU, FangSHEN, Chunfeng
    • H04N13/00
    • G06T7/579G06T7/215G06T7/277G06T2207/10016G06T2207/20076G06T2207/30241H04N13/128H04N13/261
    • The present invention relates to an apparatus, a system, a method and a computer program product of propagating depth-related information and segmentation.related information associated with at least one image from an image sequence to a consecutive image in the image sequence using a probabilistic network. The method comprising use of the probabilistic network to solve a Bayesian labeling problem wherein the labeling comprises the depth-related information and segmentation.related information and wherein the node links of the probabilistic network are configured to simultaneously account for constraints imposed by the depth-related information and segmentation.related information of the consecutive image and mapping information for the respective node from the at least one image to the consecutive image. The nodes taking into account evidence in the form of image characteristics from the consecutive image, such that propagated depth- related information and segmentation.related information is established for the consecutive image by establishing a Maximum A Posteriori solution for both the labeling and the mapping. The Bayesian problem comprises segmentation of the consecutive image, and propagation of depth information, based on segmentation and mapping information stored in the probabilistic network constructed from the at least one image to the consecutive image.
    • 本发明涉及一种将图像序列中的至少一个图像与图像序列中的连续图像相关联的与深度相关信息和分割相关信息的装置,系统,方法和计算机程序产品,其使用概率 网络。 该方法包括使用概率网络来解决贝叶斯标注问题,其中标签包括深度相关信息和分段相关信息,并且其中概率网络的节点链路被配置为同时考虑与深度相关的约束 连续图像的信息和分割相关信息以及从至少一个图像到连续图像的各个节点的映射信息。 考虑到来自连续图像的图像特征形式的证据的节点,通过为标签和映射建立最大A后验解,为连续图像建立传播的深度相关信息和分段相关信息。 贝叶斯问题包括基于从至少一个图像构建的概率网络中存储的分割和映射信息到连续图像的连续图像的分割和深度信息的传播。
    • 7. 发明申请
    • GENERATION OF OCCLUSION DATA FOR IMAGE PROPERTIES
    • 用于图像属性的信息数据的生成
    • WO2010049850A1
    • 2010-05-06
    • PCT/IB2009/054638
    • 2009-10-21
    • KONINKLIJKE PHILIPS ELECTRONICS N.V.PHILIPS INTELLECTUAL PROPERTY & STANDARDS GMBHGREMSE, FelixPHILOMIN, VasanthLIU, Fang
    • GREMSE, FelixPHILOMIN, VasanthLIU, Fang
    • H04N13/00
    • G06T15/40H04N13/10H04N13/161H04N13/261
    • A method of generating an occlusion image property map for an occlusion viewing position for a three dimensional scene is provided. The occlusion image property map comprises at least some image property values that are occluded from the occlusion viewing position. The method utilises an algorithm which can generate an image property map for an image representing the scene as a function of a viewing position. The method generates (701, 703) image property map for different viewing positions by performing the algorithm for these positions. The occlusion image property map is generated (705) from the image property maps of different viewing positions. Specifically, the image property maps may in some examples be shifted to the occlusion viewing position and data of the occlusion image property map is then selected as a pixel from the shifted image property maps which does not correspond to the most forward pixel (unless all pixels have equal depth).
    • 提供一种生成用于三维场景的遮挡观察位置的遮挡图像特性图的方法。 闭塞图像属性图包括从遮挡观察位置遮挡的至少一些图像特性值。 该方法利用一种算法,该算法可以生成表示场景的图像作为观看位置的函数的图像属性图。 该方法通过执行这些位置的算法来生成用于不同观看位置的图像属性映射(701,703)。 从不同观看位置的图像特征图生成遮蔽图像特性图(705)。 具体地说,在一些示例中,图像特性图可以被移动到遮挡观察位置,然后从不与最前进像素对应的移位图像属性图中选择遮挡图像特性图的数据作为像素(除非所有像素 具有相同的深度)。
    • 10. 发明申请
    • METHOD OF PERFORMING FACE RECOGNITION
    • WO2006097902A3
    • 2006-09-21
    • PCT/IB2006/050811
    • 2006-03-15
    • PHILIPS INTELLECTUAL PROPERTY & STANDARDS GMBHKONINKLIJKE PHILIPS ELECTRONICS N. V.GREMSE, FelixPHILOMIN, Vasanth
    • GREMSE, FelixPHILOMIN, Vasanth
    • G06K9/62
    • The invention describes a method of performing face recognition, which method comprises the steps of generating an average face model (M AV ) - comprising a matrix of states representing regions of the face - from a number of distinct face images (I 1 , I 2 , ...I j ) and training a reference face model (M 1 , M 2 , ..., M n ) for each one of a number of known faces, where the reference face model (M 1 , M 2 , ... , M n ) is based on the average face model (M AV ). A test image (I T ) is acquired for a face to be identified, and a best path through the average face model (MAv) is calculated, based on the test image (I T ). A degree of similarity is evaluated for each reference face model (M 1 , M 2 ,..., M n ) against the test image (I T ) by applying the best path of the average face model (M AV ) to each reference face model (M 1 , M 2 ,..., M n ) to identify the reference face model (M 1 , M 2 , ..., M n ) most similar to the test image (I T ), which identified reference face model (M 1 , M 2 , ..., M n ) is subsequently accepted or rejected on the basis of its degree of similarity. Furthermore, the invention describes a system for performing face recognition. Also, the invention describes a method of and system for training a reference face model (M 1 ) which may be used in the face recognition system, a method of and system for calculating a similarity threshold value for a reference face model (M n ) which may be used in the face recognition system, and a method of and system for optimizing images (I, I T , I T , G 1 , G 2 , ...G,, T 1 , T 2 , ..., Tm, Tnew) which may be used in the face recognition system.