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    • 4. 发明申请
    • PROCESSING AN INPUT SIGNAL USING A CORRECTION FUNCTION BASED ON TRAINING PAIRS
    • 使用基于训练对的校正功能处理输入信号
    • US20080103717A1
    • 2008-05-01
    • US11554183
    • 2006-10-30
    • Yacov Hel-OrDoron Shaked
    • Yacov Hel-OrDoron Shaked
    • G06F19/00G06F17/40
    • G06T5/10G06T5/002G06T2207/20008G06T2207/20012G06T2207/20064
    • Provided are systems, methods and techniques for, inter alia, processing an input signal. In one representative embodiment, samples of an input signal that represents a physical quantity are obtained and then transformed from an original domain into a plurality of coefficients in a transform domain, using an over-complete transform, such that the plurality of coefficients is sufficient to redundantly reconstruct the input signal samples. The coefficients are then modified independently of each other by applying a correction function, thereby obtaining a set of corrected coefficients, and the set of corrected coefficients is transformed back to the original domain. According to this embodiment, the correction function was determined by using a set of training pairs, each training pair including an uncorrected signal and a corrected signal, and by reducing a specified aggregate measure of error between the uncorrected signal and the corrected signal in the original domain across the training pairs.
    • 提供了用于处理输入信号的系统,方法和技术。 在一个代表性实施例中,使用表示物理量的输入信号的采样,并使用过完整变换从原始域变换为变换域中的多个系数,使得多个系数足以 冗余重建输入信号样本。 然后通过应用校正函数来独立地修改系数,从而获得一组校正后的系数,并将该组校正系数转换回原始域。 根据本实施例,通过使用一组训练对,每个训练对包括未校正的信号和校正的信号,并且通过减少未校正的信号与原始信号中的校正信号之间的误差的规定的总体测量来确定校正功能 域间训练对。
    • 5. 发明授权
    • Approximated invariant method for pattern detection
    • 近似不变式的模式检测方法
    • US06337927B1
    • 2002-01-08
    • US09326120
    • 1999-06-04
    • Michael EladYacov Hel-OrRenato Kresch
    • Michael EladYacov Hel-OrRenato Kresch
    • G06K962
    • G06K9/6284G06K9/6235
    • A system and a method for classifying input vectors into one of two classes, a target class and a non-target class, utilize iterative rejection stages to first label the input vectors that belong in the non-target class in order to identify the remaining non-labeled input vectors as belonging in the target class. The system and method may be used in a number applications, such as face detection, where the members of the two classes can be represented in a vector form. The operation of the system can be divided into an off-line (training) procedure and an on-line (actual classification) procedure. During the off-line procedure, projection vectors and their corresponding threshold values that will be used during the on-line procedure are computed using a training set of sample non-targets and sample targets. Each projection vector facilitates identification of a large portion of the sample non-targets as belonging in the non-target class for a given set of sample targets and sample non-targets. During the on-line procedure, an input vector is successively projected onto each computed projection vector and compared with a pair of corresponding threshold values to determine whether the input vector is a non-target. If the input vector is not determined to be a non-target during the successive projection and thresholding, the input vector is classified as a target.
    • 将输入向量分类为目标类和非目标类中的一个类别的系统和方法利用迭代拒绝阶段来首先标记属于非目标类中的输入向量,以便识别剩余的非目标类别 标注的输入向量属于目标类。 系统和方法可以用于诸如面部检测的数字应用中,其中可以以向量形式表示两个类的成员。 系统的操作可以分为离线(训练)程序和在线(实际分类)程序。 在离线过程中,使用样本非目标和样本目标的训练集计算在线过程期间将使用的投影向量及其相应的阈值。 每个投影向量有助于将样本非目标的大部分识别为对于给定的一组样本目标和样本非目标属于非目标类。 在在线过程期间,将输入向量连续地投影到每个计算的投影向量上,并与一对相应的阈值进行比较,以确定输入向量是否是非目标。 如果在连续投影和阈值处理期间输入向量未被确定为非目标,则将输入向量分类为目标。
    • 6. 发明授权
    • Processing an input signal using a correction function based on training pairs
    • 使用基于训练对的校正功能处理输入信号
    • US07930145B2
    • 2011-04-19
    • US11554183
    • 2006-10-30
    • Yacov Hel-OrDoron Shaked
    • Yacov Hel-OrDoron Shaked
    • G06F17/40G06F17/00
    • G06T5/10G06T5/002G06T2207/20008G06T2207/20012G06T2207/20064
    • A method of processing, by a computer, an input signal including obtaining input signal samples that represents a physical quantity. The method includes transforming the samples from an original domain into a plurality of coefficients in a transform domain, using an over-complete transform, such that the plurality of coefficients is sufficient to redundantly reconstruct the input signal samples. The method also includes modifying the coefficients independently of each other by applying a correction function, obtaining a set of corrected coefficients. The method also includes transforming the set of corrected coefficients back to the original domain. In the method, the correction function is determined by using a set of training pairs, each training pair including an uncorrected signal and a corrected signal, and by reducing a specified aggregate measure of error between the uncorrected signal and the corrected signal in the original domain across the training pairs.
    • 一种通过计算机处理包括获得表示物理量的输入信号样本的输入信号的方法。 该方法包括使用过完整变换将样本从原始域变换为变换域中的多个系数,使得多个系数足以冗余重建输入信号样本。 该方法还包括通过应用校正函数来彼此独立地修改系数,获得一组经校正的系数。 该方法还包括将该组校正系数转换回原始域。 在该方法中,通过使用一组训练对,每个训练对包括未校正的信号和校正的信号,以及通过减少未校正的信号和原始域中的经校正的信号之间的指定的误差集合来确定校正功能 穿过训练对。
    • 8. 发明授权
    • Image demosaicing method utilizing directional smoothing
    • US06618503B2
    • 2003-09-09
    • US10095198
    • 2002-03-11
    • Yacov Hel-orDaniel Keren
    • Yacov Hel-orDaniel Keren
    • G06K900
    • G06T3/4015H04N9/045
    • A method for operating a data processing system to generate a full color image from a partially sampled version of the image. The full color image includes a first two-dimensional array of vectors having components representing the intensity of a pixel in the full color image in a corresponding spectral band at a location determined by the location of the vector in the first two-dimensional array. The method generates the first two-dimensional array from a two-dimensional array of scalars. Each scalar determines one of the first, second, or third intensity values at a corresponding location in the two-dimensional array of vectors. The method determines the remaining ones of the first, second, and third intensity values. The method starts by assigning a value to each one of the components of the vectors in the first two-dimensional array of vectors that is not determined by one of the scalars. A luminance image and first and second chrominance images are then generated from the first two-dimensional array of vectors. The chrominance images are filtered with an isotropic low-pass spatial filter to generate filtered chrominance images. The two-dimensional array of vectors is then regenerated from the luminance image and the first and second filtered chrominance images. The scalars that are originally given by the sensors are reset in the regenerated two-dimensional array of vectors. The decomposition, filtering, resetting, and regenerating steps are iterated to provide the final full-color image. In the preferred embodiment, the luminance image is also filtered. However, the filtering of the luminance image utilizes a low-pass spatial filter having an anisotropy that varies with location in the luminance image.