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    • 2. 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.
    • 5. 发明申请
    • COMPUTER VISION SYSTEM AND METHOD EMPLOYING ILLUMINATION INVARIANT NEURAL NETWORKS
    • 计算机视觉系统和使用照明不可逆神经网络的方法
    • WO2004053778A2
    • 2004-06-24
    • PCT/IB2003/005747
    • 2003-12-08
    • KONINKLIJKE PHILIPS ELECTRONICS N.V.PHILOMIN, VasanthGUTTA, SrinivasTRAJKOVIC, Miroslav
    • PHILOMIN, VasanthGUTTA, SrinivasTRAJKOVIC, Miroslav
    • G06K9/00
    • G06K9/6273
    • Objects are classified using a normalized cross correlation (NCC) measure to compare two images acquired under non-uniform illumination conditions. An input pattern is classified to assign a tentative classification label and value. The input pattern is assigned to an output node in the radial basis function network having the largest classification value. If the input pattern and an image associated with the node, referred to as a node image, both have uniform illumination, then the node image is accepted and the probability is set above a user specified threshold. If the test image or the node image are not uniform, then the node image is not accepted and the classification value is kept as the value assigned by the classifier. If both the test image and the node image are not uniform, then an NCC measure is used and the classification value is set as the NCC value.
    • 使用归一化互相关(NCC)测量对对象进行分类,以比较在不均匀照明条件下获取的两个图像。 输入模式被分类以分配暂定分类标签和值。 将输入模式分配给具有最大分类值的径向基函数网络中的输出节点。 如果输入模式和与节点相关联的图像(称为节点图像)都具有均匀的照明,则节点图像被接受,并且将概率设置在用户指定的阈值以上。 如果测试图像或节点图像不均匀,则不接受节点图像,并且将分类值保持为分类器分配的值。 如果测试图像和节点图像都不一致,则使用NCC测量,并将分类值设置为NCC值。
    • 7. 发明申请
    • METHOD OF DETERMINING MOTION-RELATED FEATURES AND METHOD OF PERFORMING MOTION CLASSIFICATION
    • 确定运动相关特征的方法和执行运动分类的方法
    • WO2008139399A2
    • 2008-11-20
    • PCT/IB2008051843
    • 2008-05-09
    • PHILIPS INTELLECTUAL PROPERTYKONINKL PHILIPS ELECTRONICS NVPIETQUIN OLIVIERPHILOMIN VASANTH
    • PIETQUIN OLIVIERPHILOMIN VASANTH
    • G06K9/00
    • G06K9/00335G06T7/246G06T2207/10016G06T2207/30196
    • The invention relates to a method of determining motion-related features (F m ) pertaining to the motion of an object (1), which method comprises obtaining a sequence of images (f 1 , f 2 ,..., f n ) of the object (1), processing at least parts of the images (f 1 , f 2 ,..., f n ) to extract a number of first-order features from the images (f 1 , f 2 ,..., f n ), computing a number of statistical values pertaining to the first-order features, combining the statistical values into a number of histograms (H LC , H G ), and determining the motion- related features (F m ) for the object (1) based on the histograms (H LC , H G ). Furthermore, the invention relates to a method for performing motion classification for the motion of an object (1) captured in a sequence of images (f 1 , f 2 ,..., f n ). Such a motion classification method comprises determining motion-related features (F m ) for the images (f 1 , f 2 ,..., f n ) using the described method of determining motion-related features (F m ), and using the motion-related features (F m ) to classify the motion of the object (1). The invention also relates to a system (3) for determining motion-related features (F m ) pertaining to an object (1) in an image (f 1 ), and to a system (4) for performing motion classification for the motion of an object ( 1 ).
    • 本发明涉及一种确定与物体(1)的运动相关的运动相关特征(F m)的方法,该方法包括获得图像序列(f 1> (1)的SUB>,f 2 2,...,f N n N),处理至少部分图像(f 1> ,f 2,...,f n n),以从图像中提取多个一阶特征(f 1,...,f 计算与第一阶特征相关的统计值的数量,将统计值组合成多个直方图(H< 2>,...,f& 并且基于直方图(H')确定对象(1)的运动相关特征(F m m), LC> H,H G)。 此外,本发明涉及一种用于对在图像序列(f 1,...,2,...)捕获的对象(1)的运动进行运动分类的方法。 ),f n n n)。 这样的运动分类方法包括:针对图像(f 1>,f 2,...,...)确定运动相关特征(F m) 使用所描述的确定运动相关特征(F m m)的方法,并且使用运动相关特征(F m m) 对物体(1)的运动进行分类。 本发明还涉及一种用于确定与图像(f 1> 1)中的对象(1)相关的运动相关特征(F m> m)的系统(3),以及 涉及用于对物体(1)的运动执行运动分类的系统(4)。