利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

6 条记录 (耗时 0.012 秒)

    Binary optical spectrum analyzer
    3.
    发明授权
    Binary optical spectrum analyzer 失效
    二元光谱分析仪

    公开(公告)号：US5461475A

    公开(公告)日：1995-10-24

    申请号：US191056

    申请日：1994-02-02

    申请人： Jeremy Lerner ,  Taiwei Lu ,  Shing-Hong F. Lin ,  Andrew Kostrzewski ,  Hung Chou

    发明人： Jeremy Lerner ,  Taiwei Lu ,  Shing-Hong F. Lin ,  Andrew Kostrzewski ,  Hung Chou

    IPC分类号： G01J3/28 ,  G01J3/00

    CPC分类号： G01J3/28 ,  G01J3/0229

    摘要： Apparatus for analyzing a spectral signature, including: a light source; a spatial light modulator connected to the light source, the spatial light modulator modulating light from the light source in accordance with spatial features of the spectral signature; an optic system upon which modulated light from the spatial light modulator is incident, the optic system filtering the modulated light; a hologram illuminated with filtered, modulated light from the optic system, the hologram outputting an optical identification of the spectral signature; and a detector upon which the optical identification is incident, the detector detecting the optical identification.

    摘要翻译： 用于分析光谱特征的装置，包括：光源; 连接到光源的空间光调制器，空间光调制器，根据光谱特征的空间特征调制来自光源的光; 光学系统，其上来自空间光调制器的调制光入射，光学系统对调制光进行滤波; 用来自光学系统的经滤波的调制光照射的全息图，全息图输出光谱特征的光学识别; 以及检测器，其上进行光学识别，检测器检测光学识别。

    Three dimensional imaging system
    4.
    发明授权
    Three dimensional imaging system 失效
    三维成像系统

    公开(公告)号：US06252623B1

    公开(公告)日：2001-06-26

    申请号：US09080135

    申请日：1998-05-15

    申请人： Taiwei Lu ,  Jianzhong Zhang

    发明人： Taiwei Lu ,  Jianzhong Zhang

    IPC分类号： H04N718

    CPC分类号： G01B11/25

    摘要： A method and apparatus for imaging three dimensional objects is described which has a source of illumination that is projected through a color grating onto the object to be imaged. A camera captures an image from the object which reflects the pattern imposed by the grating, and a series of mathematical operations are then performed on the data from the captured image to deduce three dimensional information about the object. The grating includes a repetitive pattern of parallel colored bars disposed a predetermined distance from each other, and includes an opaque area intermediate each of the colored bars to enhance the accuracy of the image by reducing cross-talk between the color bars of the captured image. One exposure of the object can provide information sufficient to calculate the 3-D profile of the object, making the system especially useful for imaging moving or living objects.

    摘要翻译： 描述了一种用于成像三维物体的方法和装置，其具有通过彩色光栅投影到待成像对象上的照明源。 相机拍摄来自对象的图像，该图像反映由光栅施加的图案，然后对来自拍摄图像的数据执行一系列数学运算，以推导出关于对象的三维信息。 光栅包括以彼此间隔预定距离设置的平行彩色条的重复图案，并且包括每个彩色条之间的不透明区域，以通过减少拍摄图像的色条之间的串扰来提高图像的准确度。 对象的一次曝光可以提供足以计算对象的三维轮廓的信息，使得该系统对于移动或活动物体的成像特别有用。

    Hybrid neural network and multiple fiber probe for in-depth 3-D mapping
    5.
    发明授权
    Hybrid neural network and multiple fiber probe for in-depth 3-D mapping 失效
    混合神经网络和多光纤探头，用于深入的3-D映射

    公开(公告)号：US5660181A

    公开(公告)日：1997-08-26

    申请号：US354317

    申请日：1994-12-12

    申请人： Zonh-Zen Ho ,  Taiwei Lu

    发明人： Zonh-Zen Ho ,  Taiwei Lu

    IPC分类号： A61B5/00 ,  A61B6/02

    CPC分类号： A61B5/7264 ,  A61B1/00193 ,  A61B1/042 ,  A61B1/043 ,  A61B1/07 ,  A61B5/0059 ,  A61B5/7267 ,  Y10S128/925

    摘要： An apparatus for in-depth three dimensional tumor mapping including (A) a light source; (B) a multi-fiber bundle including at least one illumination fiber and at least two receiving fibers, the at least one illumination fiber being connected to the light source; (C) a spectrometer connected to the at least two receiving fibers; and (D) a hybrid neural network connected to the spectrometer, said hybrid neural network including a principle component analysis processor and a neural network classifier.

    摘要翻译： 一种用于深入三维肿瘤图谱的装置，包括（A）光源; （B）包括至少一个照明光纤和至少两个接收光纤的多纤维束，所述至少一个照明光纤连接到所述光源; （C）连接到所述至少两个接收光纤的光谱仪; 和（D）连接到光谱仪的混合神经网络，所述混合神经网络包括主成分分析处理器和神经网络分类器。

    Method and apparatus for image recognition using invariant feature signals
    6.
    发明授权
    Method and apparatus for image recognition using invariant feature signals 失效

    公开(公告)号：US5497430A

    公开(公告)日：1996-03-05

    申请号：US335455

    申请日：1994-11-07

    申请人： Lev S. Sadovnik ,  Taiwei Lu

    发明人： Lev S. Sadovnik ,  Taiwei Lu

    IPC分类号： G06K9/00 ,  G06K9/52 ,  G06K9/46

    CPC分类号： G06K9/522 ,  G06K9/00221

    摘要： A method of operating an image recognition system including providing a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; providing a display including an indicator; initializing the indicator to an initialized state; obtaining an image of a structure; digitizing the image so as to obtain a plurality of input intensity cells and define an input object space; transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation-invariant feature signals from the input object space according to a set of relationships I.sub.k =.intg..sub..OMEGA. .intg.I(x,y)h[k,I(x,y)]dxdy, where I.sub.k is the set of n scale-, position- and rotation-invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, .OMEGA. is an area of integration of input intensity cells, and h[k,I(x,y)] is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons; transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality of output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; transforming the set of structure recognition output signals to a structure classification signal; and transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.