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    • 3. 发明授权
    • Automatic geometric image transformations using embedded signals
    • 使用嵌入式信号的自动几何图像变换
    • US5949055A
    • 1999-09-07
    • US956839
    • 1997-10-23
    • David J. FleetDavid J. HeegerTodd A. CassDavid L. Hecht
    • David J. FleetDavid J. HeegerTodd A. CassDavid L. Hecht
    • G06T1/00H04N1/32G06K7/12
    • G06T1/0028G06T1/0064H04N1/32203H04N1/32208H04N1/32229H04N1/32309G06T2201/0051G06T2201/0083G06T2201/0202G06T2201/0601H04N2201/3233H04N2201/327
    • An acquired (e.g., scanned) image contains an imperceptible periodic signal component (e.g., a sinusoid), decoding of which can be used to automatically determine a linear geometric relationship between the acquired image and the original image in which the signal was embedded, without having the original image available during the decoding process. This known geometric relationship allows for linear geometric properties of the acquired image, such as alignment and scaling, to be automatically matched with those of the original image so that the acquired image may be automatically oriented and scaled to the size of the original image. The embedded periodic signals produce a distinct pattern of local peak power concentrations in a spatial frequency amplitude spectrum of the acquired image. Using geometric constraint information about the embedded signals when the signals were originally embedded in the image, the locations and spatial frequencies of the signals are decoded from the image, providing a linear mapping between the peak power concentrations of the acquired and original image spatial frequency amplitude spectra. This linear mapping can be used to compute the linear geometric relationship between the two images. In an illustrated embodiment, the acquired image contains a set of sinusoidal signals that act as a grid. Decoding of the sinusoids does not require the original image, only information about the predetermined geometric relationship of the embedded sinusoids.
    • 获取的(例如,扫描的)图像包含不可察觉的周期性信号分量(例如,正弦曲线),其解码可用于自动确定所获取的图像与其中嵌入信号的原始图像之间的线性几何关系,而没有 在解码过程中可以获得原始图像。 这种已知的几何关系允许所获取的图像的线性几何属性(例如对准和缩放)自动与原始图像的几何属性匹配,使得所获取的图像可以被自动定向并且缩放到原始图像的尺寸。 嵌入的周期信号在所获取的图像的空间频率幅度谱中产生局部峰值功率浓度的不同图案。 当信号最初嵌入到图像中时,使用关于嵌入信号的几何约束信息,从图像中解码信号的位置和空间频率,从而提供所获取的和原始图像空间频率幅度的峰值功率浓度之间的线性映射 光谱。 该线性映射可用于计算两个图像之间的线性几何关系。 在所示实施例中,所获取的图像包含用作网格的一组正弦信号。 正弦曲线的解码不需要原始图像,只有关于嵌入的正弦曲线的预定几何关系的信息。
    • 5. 发明授权
    • Visual motion analysis method for detecting arbitrary numbers of moving objects in image sequences
    • 用于检测图像序列中移动物体的任意数目的视觉运动分析方法
    • US06954544B2
    • 2005-10-11
    • US10155815
    • 2002-05-23
    • Allan D. JepsonDavid J. FleetMichael J. Black
    • Allan D. JepsonDavid J. FleetMichael J. Black
    • G06T7/20G06K9/00
    • G06K9/32G06T7/215G06T7/251G06T2207/10016G06T2207/30196
    • A visual motion analysis method that uses multiple layered global motion models to both detect and reliably track an arbitrary number of moving objects appearing in image sequences. Each global model includes a background layer and one or more foreground “polybones”, each foreground polybone including a parametric shape model, an appearance model, and a motion model describing an associated moving object. Each polybone includes an exclusive spatial support region and a probabilistic boundary region, and is assigned an explicit depth ordering. Multiple global models having different numbers of layers, depth orderings, motions, etc., corresponding to detected objects are generated, refined using, for example, an EM algorithm, and then ranked/compared. Initial guesses for the model parameters are drawn from a proposal distribution over the set of potential (likely) models. Bayesian model selection is used to compare/rank the different models, and models having relatively high posterior probability are retained for subsequent analysis.
    • 一种视觉运动分析方法,其使用多层全局运动模型来检测和可靠地跟踪出现在图像序列中的任意数量的移动物体。 每个全局模型包括背景层和一个或多个前景“多边形”,每个前景多边形包括参数形状模型,外观模型和描述相关联的运动对象的运动模型。 每个多骨架包括独占空间支持区域和概率边界区域,并被分配明确的深度排序。 使用例如EM算法进行精细化,然后进行排序/比较,生成与被检测对象对应的具有不同层数,深度顺序,动作等的多个全局模型。 模型参数的初始猜测是从一组潜在(可能)模型中的提案分布中得出的。 贝叶斯模型选择用于比较/排列不同模型,并且保留具有较高后验概率的模型用于后续分析。