CN106096474A - 用于成像的系统和方法 - Google Patents
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Abstract
用于成像的系统和方法。一种成像设备具有光场成像器和处理器。光场成像器包括微透镜阵列和光场传感器,光场传感器被定位在靠近微透镜阵列,具有多个像素并且记录来自穿过微透镜阵列的光的光学目标的光场数据。处理器被配置成:从光场传感器接收光学目标的光场数据,估计光场数据中的信噪比以及光学目标的深度,基于信噪比和深度选择子孔径图像的子集,组合子孔径图像的选择的子集,并在子孔径图像的组合的子集上执行图像分析。
Description
对相关申请的交叉引用
本申请要求2015年4月21日提交的编号62/150352的用于成像的系统和方法的美国专利申请的益处,在此通过引用将其整体并入。
本申请涉及2014年12月10日提交的编号14/566464的用于条形码成像的美国专利申请(以及2015年6月11日公开的编号2015/0161427的美国专利公开),其要求享有2013年12月10日提交的编号61/914256的用于条形码识别的美国专利申请的益处。在此通过引用将前述专利申请和专利公开中的每一个其整体并入。
技术领域
本发明一般涉及光场成像的系统和方法,并且更具体地涉及减少光场成像的计算复杂性的系统和方法。
背景技术
除其它事项外,由于机械聚焦系统缺乏对机械冲击的健壮性,标记扫描仪往往具有固定的聚焦光学器件。结果是扫描仪具有有限的场深度,而将对象定位在该场深度内的责任在用户;做出给定扫描仪的各种子模型以解决不同的扫描范围。
另外,用于条形码读取器的可接受的处理时间非常短,因为它们用在高通过量设置中,诸如杂货店结账柜台。因此,需要开发允许更大的扫描场深度的条形码读取器,同时(a)对机械冲击仍然是健壮的和/或(b)向用户提供快速响应。
为了去除这些限制,有兴趣为扫描使用光场相机。光场相机使用光学路径中的微透镜阵列来捕获光射线的集,可以在软件中组合光射线的集以产生以不同距离聚焦的图像。光场成像系统允许用户捕获四维(4D)图像,四维图像提供可以由典型的成像系统所提供的额外的成像信息。例如,光场成像阵列系统可以提供空间和角度光射线信息二者。
与光场扫描相关联的缺点是:相对于更传统的二维图像传感器,重聚焦操作的计算复杂性高。例如,典型地,当利用光场成像系统时,图像数据被分析,被分割成部件,并且创建几个重聚焦的图像,其中每个对应的图像作为整体聚焦在图像内的不同深度上。为了这样做,在几个重聚焦的图像上最大化目标函数(例如锐度),以便确定提供最高对比度的图像,并且对应于场景中条形码的深度。此更高的计算复杂性可引起图像捕获和符号解码(例如,解码捕获的图像中的条形码)之间不可接受的长延迟。
对此方法的替代方案是分析傅里叶域中的场景,但是这是缓慢的,因为大矩阵必须被变换到傅里叶域中。在一些应用中,这些方法的处理时间可以比所希望的慢。
发明内容
在本发明的一个方面,一种成像设备包括:包括微透镜阵列和光场传感器的光场成像器,该光场传感器被定位在靠近微透镜阵列,具有多个像素并且记录来自穿过微透镜阵列的光的光学目标的光场数据;以及处理器,被配置成:从光场传感器接收光学目标的光场数据,估计光场数据中的信噪比以及光学目标的深度,基于信噪比和深度选择子孔径图像的子集,组合子孔径图像的选择的子集,并在子孔径图像的组合的子集上执行图像分析。
在实施例中,光场数据包括多个子孔径图像。
在实施例中,使用传感器特性来执行估计信噪比。
在实施例中,传感器特征包括传感器噪声水平、增益、曝光时间或其任何组合。
在另一个实施例中,根据像素强度确定传感器特征。
在实施例中,从光场数据直接地确定估计光学目标距光场成像器的深度。
在另一个实施例中,通过两个子孔径图像的配准估计光学目标距光场成像器的深度。
在实施例中,子孔径图像的最优子集的预定的表格以不同的噪声比和理论光学目标距光场成像器的深度。
在实施例中,从预定的表格选择子孔径图像的子集。
在另一个实施例中,组合子孔径图像的子集包括一起量化的移位和添加子孔径图像的子集。
在实施例中,成像设备是标记扫描仪。
在实施例中,图像分析是面部识别。
在实施例中,图像分析是虹膜识别。
在本发明的另一个方面,一种方法包括:用光场传感器捕获光学目标的光场数据;估计光场数据中的信噪比以及光学目标的深度;基于信噪比和深度选择子孔径图像的子集;组合子孔径图像的选择的子集;并在子孔径图像的组合的子集上执行图像分析。
在实施例中,光场数据包括多个子孔径图像。
在另一个实施例中,使用传感器噪声水平、增益、曝光时间或其任意组合的传感器特性来执行估计信噪比。
在另一个实施例中,根据光场数据直接地确定估计光学目标距光场成像器的深度。
在又另一个实施例中,通过两个子孔径图像的配准估计光学目标距光场成像器的深度。
在实施例中,从预定的表格选择子孔径图像的子集,预定的表格包括以不同的噪声比和理论光学目标距光场成像器的深度的子孔径图像的最优子集。
在实施例中,组合子孔径图像的子集包括一起量化的移位和添加子孔径图像的子集。
附图说明
现在将通过示例的方式、参照附图来描述本发明,其中:
图1a是光场成像器的示意性视图;
图1b是光场成像器的计算机系统的示意性视图;
图2a是使用组合所有视图的传统基线方法的1D条形码的第一图像;
图2b是使用有限数量的视图的1D条形码的第二图像;
图2c是没有不具有量化的重聚焦参数的传统基线方法的2D条形码第三图像;
图2d是具有量化的重聚焦参数的2D条形码的第四图像;
图3是随着真正移位变化而与量化误差成比例地四舍五入减少图像对比度的图形表示;
图4是使用减少光场成像器的计算复杂性的方法的重聚焦的图像的动态范围性能的图形表示;以及
图5是用于减少光场成像器的计算复杂性的方法的框图。
具体实施方式
虽然以下描述针对标记扫描仪和可解码标记的分辨率的领域,但本领域技术人员将认识到:本文描述的装置和方法一般适用于其它成像应用。从而,标记扫描仪的使用是示范性的,并且本发明不应当限于此。编号14/566464的美国专利申请公开一种找到图像帧内的条形码并确定从扫描仪到目标对象的距离的方法。以下的公开改进了用于组合多条射线以产生图像的现有方法。
在工业成像应用中,捕获大量的成像以用于非审美的用途,如果用标准方法执行图像处理,这将招致高计算成本。然而,虽然在消费者摄影中往往希望高质量的图像,但是图像质量对于模式识别系统是二进制的:图像质量或者高于支持可靠识别的预定阈值水平或者低于该预定阈值水平。一旦超过该阈值,就浪费改善图像质量的任何额外努力。诸如标记扫描仪之类的模式识别系统需要光场处理,光场处理以最小的计算成本产生满意的图像质量。诸如在图1的实施例中所示的光场相机1之类的光场相机同时捕获多个场景“视图”;并且例如在基于微透镜的光场成像器的情况下,这些“视图”也被称为子孔径图像(SAI),并且对应于由穿过主透镜的孔径的特定区域的光所形成的图像。参见下面详细讨论的图1a和1b。
处理的计算复杂性与组合的视图数量成比例,并且与视图到视图配准的精度成比例。虽然可以始终以图像质量为代价减少计算努力,但经由幼稚的或贪婪的算法选择SAI是次优的,因为选择的SAI可能不是计算上最简单的组合。从而,通过确定在重聚焦时将组合的SAI的最优数量和选择,光场渲染(即重聚焦)的计算复杂性可以大大减小。
在实施例中,可以通过做出较差质量的图像而减小光场渲染的计算复杂性。换句话说,一般的目标之一是使用最小量的计算生成正好高于可解码性的阈值的图像,而不是生成美观的图像。由于光场渲染的计算复杂性与组合的角视图的数量成比例,所以可以通过使用最小数量的这样的视图(SAI)并且选择关于所需多少计算的最小数量来获得快速重聚焦。
在图1a和1b中所示的实施例中,光场成像器1包括主透镜10、微透镜阵列20、光场传感器30、存储器503和处理单元502(例如处理器)。光场传感器30与存储器503和/或处理单元502电通信。如图1a中所示,光场传感器30被定位在靠近微透镜阵列20,具有多个像素(未示出)并记录来自穿过微透镜阵列20的光40的光学目标的光场数据。
在图1b中所示的实施例中,示范性的计算机系统500实现公开的方法。所有组件无需用在各种实施例中。一个示范性的计算设备以计算机500的形式,并且包括处理单元502、存储器503、可移动储存器510和不可移动储存器512中的一个或多个。虽然示范性的计算设备被示为和描述为计算机500,但计算设备在不同的实施例中可以不同的形式。例如,如上所述,计算设备可以是光场成像器1,诸如LytroTM或其它商业可获得的光场相机,或者智能电话,平板电脑,或者包括与关于图1b所示出和描述的相同或相似的元件的其它计算设备。此外,虽然各种数据储存元件被图示为计算机500的部件,但储存器也可以或替代地包括经由诸如因特网之类的网络可访问的基于云的储存器。
存储器503可以包括易失性存储器514和非易失性存储器508。计算机500包括或有权访问计算环境,该计算环境包括各种各样的计算机可读介质,诸如易失性存储器514和非易失性存储器508、可移动储存器510和不可移动储存器512。计算机储存器包括随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM)和电可擦除可编程只读存储器(EEPROM)、快闪存储器或其它存储器技术、光盘只读存储器(CD-ROM)、数字多功能盘(DVD)或其它光盘储存器、磁带盒、磁带、磁盘储存器或其它磁储存器设备、或者能够存储计算机可读指令的任何其它介质。
计算机500可以包括或访问计算环境,该计算环境包括输入506、输出504和通信连接516。输出504可以包括也可以充当输入设备的显示设备,诸如触摸屏。输入506可以包括触摸屏、触摸板、鼠标、键盘、相机、一个或多个设备专用按钮、在计算机500内集成的或经由到计算机500的有线或无线数据连接耦合的一个或多个传感器、以及其它输入设备中的一个或多个。计算机可以在联网的环境中使用通信连接操作,以连接到一个或多个远程计算机,诸如数据库服务器。远程计算机可以包括个人计算机(PC)、服务器、路由器、网络PC、对等设备或其它公共网络节点等等。通信连接可以包括局域网(LAN)、广域网(WAN)、蜂窝、WiFi、蓝牙或其它网络。
存储在计算机可读介质上的计算机可读指令可由计算机500的处理单元502执行。硬盘驱动器、CD-ROM和RAM是制品的一些示例,该制品包括非暂时性计算机可读介质,诸如储存器设备。术语计算机可读介质和储存器设备不包括载波。例如,计算机程序518可以被包括在CD-ROM上并且从CD-ROM加载到硬盘驱动器,该计算机程序518能够提供通用技术来为数据访问执行访问控制检查和/或用于在基于组件对象模型(COM)的系统中的服务器之一上完成操作。计算机可读指令允许计算机500在具有多个用户和服务器的基于COM的计算机网络系统中提供通用访问控制。
基线复杂性
用于模式识别的常规光场处理涉及至少三个计算上密集的步骤:(1)从原始传感器图像内插SAI,(2)定位和估计目标区域的深度,以及(3)将SAI组合到重聚焦的图像。步骤(1)和(3)的复杂性与在重聚焦期间组合的SAI的数量成比例,因此最小化SAI的数量减少计算复杂性。在实施例中,为时间受限的应用利用基于对应的深度估计,因为可以和两个SAI一样少地使用基于对应的深度估计。另外,域专用的深度估计方法可以消除从传感器图像内插SAI的需要,诸如在编号14/566464的美国专利申请中所述,该申请已经通过引用而被并入,其详细描述用于1D条形码的域专用的深度估计,虽然本领域技术人员将理解:也可以使用其它已知的深度估计方法,诸如傅里叶域方法。
在常规的移位和添加重聚焦算法中,光场被表示为子孔径图像的集合(SAI),其像素中的每一个记录穿过主透镜孔径的具体区域的光。中央SAI L0.0由穿过主透镜孔径的中心的光组成,其中中央SAI像素被定位在每个微透镜后面的点的中心处。一般情况下,SAI被表示L(u,v),其中u和v分别表示相对于孔径/点中心的构成射线/像素的水平和垂直位移;umax表示微透镜点的半径,所以u,v≤umax。每个目标深度对应于唯一的值Δ(“移位”),使得在像素(x,y)处重聚焦的图像急剧地聚焦在该深度,当:
R(x,y)|=∫u∫vL(u,v)(x+uΔ,y+vΔ)dudv (1)
因为在u,v中微透镜采样一般是稀疏的,所以在R的聚焦区域外可出现混叠而不内插额外的视图。在实施例中,可以通过仅渲染目标区域来避免内插和集成额外的视图,其中不同的视图被添加为:
在实施例中,根据等式2用于重聚焦的图像中的每个像素的移位和添加的重聚焦算法的复杂性是:组合n个SAI涉及n个项的总和。如果Δ是整数,可以经由相应的SAI中的查找发现被加数,并且等式(2)是n-1个加法。当uΔ和vΔ不是整数时,可是重聚焦也涉及n-1个2D内插。从而,在实施例中,当Δ是整数时,移位和添加一个SAI到另一个比用非整数移位的2D内插约快4倍。
量化重聚焦参数
量化的缺点是:在求和之前误配准多个视图,会造成R的散焦。当Δ被四舍五入到最接近的整数时,量化误差以0.5个像素为界,并且模糊点的半径以R中的umax/2为界。
在使用1D和2D条形码扫描作为示例的实施例中,根据经验,当施加到光场图像时,诸如由LytroTM相机捕获的那些,其中umax=5,条形码解码器不支持重聚焦移位的整数量化。这是通过采用一距离重聚焦条形码200的光场图像所示的,其中最小的条映射到约1.5个像素。
例如如图3中所示,随着真正的移位在[0,1]的范围内连续地变化,四舍五入与量化误差成比例地减少对比度。整数量化可以将对比度减少到低于0.19的阈值,其中解码是不成功的。不过以半像素或三分之一像素增量量化移位使对比度边界超过该阈值,其中解码是成功的。当只有对应度估计贡献额外的误差时,可以选择间隔,使得量化加深度误差的统计期望的净效应产生具有足够对比度的结果。虽然半像素或三分之一像素量化步骤没有一起避免内插,但它们的确减少所需的内插的数量。当量化步骤是1/q时,如果u1=u2 mod q并且v1=v2 mod q时,可以组合任何两个SAI L(u1,v1)和L(u2,v2)而没有内插。使用半像素(即q=2)量化步骤并且将Δ四舍五入到Δq,等式2可以被重写为:
R(x,y)=R(0,0)(x,y)+
R(1,0)(x+Δq,y)+
R(0,1)(x,y+Δq)+
R(1,1)(x+Δq,y+Δq) (3)
其中R上的上标表示(u,v)模q的余项。
作为示例,
具有u和v上适当的边界。生成这些R避免内插,因为qΔq必然是整数,即在该总和中的SAI都具有关于彼此的整数移位。包含各种R的被加数的集被称为“整数移位类”R,由于每个构件可以经由整数移位与任何其它组合,并且例如被表示为等式4中的R(0,0)被加数。当q=2时,产生R所需的内插的数量减少到3,并且一般q2-1。另外,虽然R(1,1)在等式3中仍然需要双线性的内插,但R(0,1)和R(1,0)只需要线性内插。所以当q=2时,重聚焦从n-1个2D内插减少到2个1D和1个2D内插。
在基于微透镜的光场相机中,通过相同的孔径尺寸取场景的每个视图,所以组合额外的视图不改善锐度。组合多个视图维持与√n倍的f数相关联的锐度,但给出与全透镜孔径(在LytroTM相机的情况下是f/2)一致的SNR。但是,取决于光照条件,可不需要全孔径来产生可识别的图像;如果曝光时间足够长,中央SAI本身可能是可识别的,并且重聚焦是不必要的。在识别系统中重聚焦的目的是为了产生满足或超过阈值SNR的目标的图像-以尽可能少的时间。所以这部分描述如何动态地(即考虑图像曝光)发现产生可识别的图像所需的视图数量。
由于每个基于图像的识别算法将具有其自己的信号噪声比(SNR)要求,所以函数SNR(A)可被概括,并且量化使用A中的SAI以及关于该数量的阈值τ重聚焦的图像的质量,满足:SNR(A∪B)=SNR(A)+SNR(B)-SNR(A∩B),其中零偏差SNR({})=0。其中重聚焦目标将是可认识的最小阈值是否为SNR(·)>=τ。排序由以下反映:如果||(u1,v1)||<||(u2,v2)||,则SNR({L(u1,v1)})>SNR({L(u2,v2)})。
排序反映事实:由于微透镜晕映,SAI的信号分量随着||(u,v)||减小,而显著噪声源(来自暗电流与读出)仍然固定。在实施例中,在预处理期间避免原始图像中SAI的脱晕映,因为晕映SAI与||(u,v)||成比例地放大噪声。这保持暗噪声水平在SAI之间一致,使得其易于知道给定的图像是否是可解码的。在增加||(u,v)||的次序中分类SAI之后,在以下中反映最小值k:
在等式5中所示的实施例中,确定达到给定的SNR阈值所需的SAI的最小数量。在增加||(u,v)||的次序中分类SAI,并且然后求和它们的前k个,隐含充当贪婪的算法,其中添加的下一个SAI在未使用的SAI中是最亮的。然而,如上所述,具有最小处理复杂性的最优SAI是那些SAI,其中量化的移位参数Δq是整数,由于那些SAI不需要内插。
另一方面,如果例如Δq=1.5像素并且SNR({L(0,0)})如此接近于τ,以至于添加任何其它SAI将产生可识别的图像,贪婪的算法将次优地选择L(1,0)、L(-1,0)、L(0,1)或L(0,-1)之一,这取决于在分类期间约束(tie)如何破裂。由于所有的这些需要内插来实现量化的移位,所以计算复杂性高于将L(2,0)添加到L(0,0),这仅需要存储器查找,因为2Δq是整数。同样,当Δq被量化到像素的三分之一时,添加L(3,0)是最优的,因为它是其使用避免内插的最亮的非中心SAI。所以确定SAI的最优集以组合必须考虑SNR({L(0,0)})和Δq。形式上,优化问题被定义为选择集使得用这些SAI重聚焦提供比τ大的SNR,并且没有其它子集的L以较低的计算成本这样做。从而,w=SNR({L(0,0)}),并且表示我们期望的集Lw Δq。L的子集数量是SAI数量的指数,所以如果使用Dansereau的工具箱,给定121个SAI,有2121≈2.7*1036个子集,并且蛮力方法是完全不实用的。相反,在实施例中,易处理的算法观察关于整数移位类的Lw Δq的属性的最优子结构属性。
为了确定最优子结构引理:Lw Δq表示给重聚焦的图像提供SNR(Lw Δq)的SAI的最低成本的集。如果我们从Lw Δq去除都来自给定的整数移位类R的所有SAI,然后SAI的剩下的集Lw Δq\R必须是产生具有SNR(Lw Δq\R)的重聚焦的图像的最低成本的子集。
例如,假定有产生具有SNR(Lw Δq\R)的重聚焦的图像的较低成本集 在那种情况下,通过添加属性,集X∪(Lw Δq∩R)必须具有比Lw Δq低的成本,同时产生相同的SNR,并且Lw Δq是最低成本的前提是矛盾的。
根据最优子结构引理,Lw Δq可以通过动态编程来发现。设M(i,t)表示最大SNR,最大SNR可以在最多t毫秒的计算中仅使用前i个整数移位类来实现。时间量被表示为从传感器图像提取SAI并且将该SAI添加到其整数移位类中的另一个SAI所花的时间,作为ta,并且内插从第i个整数移位类Ri选择的SAI的相加的结果所需的时间量,作为ti。整数移位类通过增加ti来分类,并且从而当没有足够的时间来从第i个整数移位类内插和添加甚至单个SAI时,
如果ti+ta>t,M(i,t)=M(i-1,w) (6)
否则:
其中max()中的项表示来自第i个整数移位类的0,1,...,k个SAI的使用,其构件在||(u,v)||的升序中被分类。k是第i个整数移位类中SAI的数量和值[(t-ti)/ta]中的较小值,即可以在时间限制内组合的SAI的最大数量。
为了求解Lw Δq,对于I=1,条目填充有M,其中第一整数移位类包含L(u,v),其中u=0mod q并且v=0 mod q。可以在tmax覆盖t的最大值,其中M(1,tmax)>τ,并且可以使用等式6和7计算M的剩余值。如在动态编程的其它应用中,可以通过将ta和ti除以其最大公约数来加速M的值的计算。
在图4中所示的实施例中,在离LytroTM相机约15cm的距离处捕获13milUPC条形码的图像,同时在13个光圈增量中16个光圈曝光的范围内捕获的约48个图像。这些光圈范围从曝光不足的11个光圈到过度曝光的5个光圈。为了证明Lytro的f/2主孔径相对于提供相同场深度的小孔径的聚光优势,中央SAI L(0,0)与捕获的图像比较以看到可以在其上解码UPC-A条形码的曝光范围。在LytroTM相机上,中央SAI约对应于f/22孔径。
图4根据上述方法总结重聚焦的图像的动态范围性能。在低曝光范围上,中央SAI的第一个成功解码是3个曝光不足的1/3光圈,并且使用该方法的第一个成功解码是9个曝光不足的2/3光圈。这是低端解码动态范围的6个1/3光圈的增加,意味着可以用如小孔径方法的2-6.3=1:3%的光量来执行解码。在高曝光范围上,最后成功的基线解码是2个过度曝光的1/3光圈,并且图像的最后一次成功的解码是3个过度曝光的2/3光圈。这是1个1/3的增加,所以该方法可以处理21.3=250%的和以前一样的光。
图4的实施例还示出用于进行重聚焦的图像的SAI的数量。数值上,重聚焦的图像的46%仅仅是单个SAI。以低曝光水平,重聚焦的图像使用增加数量的SAI。由于晕映,当曝光水平减少达一个光圈时,SAI的数量增加一倍多。由于分布如此倾斜,在使用的图像(14)的平均数和中位数(2)之间有很大的差异,但是任一个表示相对于一直使用所有的121个SAI重聚焦的幼稚的光场的大的节约。
表格1.用于重聚焦的iMX25处理时间
表格1使用本文所述方法示出经验导出的定时信息,提供嵌入式识别系统的性能而无需高端现代处理器。在表格1中,根据本文所述方法的移位和添加重聚焦元件在飞思卡尔半导体iMX25开发板上来执行,该开发板具有含单个400MHz核的32位ARM926EJS处理器。移位和添加重聚焦的组件在不同大小的若干图像上被定时,并示出每个SAI操作每个像素的平均复杂性。因为iMX25不具有用于浮点运算的硬件支持,并且软件仿真是极其昂贵的,乘以和除以非整数数量近似最接近的有理数,例如0.66的乘法通过乘以2然后除以3来实现。
在基线实现方式中,移位和添加重聚焦涉及经由2D内插独立地配准非中央SAI中的每一个,并且然后添加到L(0,0)。这具有120个SAI*335ns/SAI=0.040ms每像素的复杂性,所以重聚焦256×256像素的灰度图像将需要大约2.6秒。其中具有给定的u值的所有SAI在v方向上的第二个1D内插之前经由1D内插被组合的更有效的基线将此减少到120个1D内插,花费~每个像素0.031ms或对于相同大小的图像2秒。
由于使用方法的实施例组合的SAI的集Lw Δq取决于w和量化的移位参数Δq二者,方法的计算复杂性取决于曝光水平和相对于主透镜的聚焦平面的目标位置。在理想情况下,当Lw Δq={L(0,0)}时,因为中央SAI足够充分曝光,该方法的重聚焦是非操作,并且不需要任何处理时间。在理想情况下,诸如LytroTM相机之类的光场相机仅仅像具有小孔径的传统相机一样行动。
在图2a-2d中所示的实施例中,图2a示出使用组合所有121个SAI的常规基线方法的1D条形码100的第一图像101。在产生第一图像101时的计算成本约是402ms。图2b示出通过组合仅仅6个视图而使用本文所述的方法产生的1D条形码100的第二图像102。通过处理有限数量的视图产生第二图像102的计算成本约是17ms。图2c示出使用不量化重聚焦参数(即没有移位量化)的常规基线方法产生的2D标记110的第三图像103。产生第三图像103的计算成本约是1280ms。图2d示出通过量化重聚焦参数而使用本文所述的方法产生的2D标记110的第四图像104。产生第四图像104的计算成本约是21ms。
如图5的实施例中所示,减少光场成像器1的计算复杂性的方法300包括:在块310处,用光场传感器30捕获光学目标的光场数据,诸如可解码的标记,其中光场数据包括多个子孔径图像。
方法300包括:在块315处,估计光场数据中的信噪比以及光学目标的深度。在实施例中,使用传感器噪声水平、增益、曝光时间或其任何组合的传感器特性来执行估计信噪比。在另一个实施例中,根据光场传感器30的像素强度来确定传感器特性。在实施例中,根据光场数据直接地确定估计光学目标距光场成像器的深度。在实施例中,通过配准两个子孔径图像估计光学目标距光场成像器的深度。
方法300包括:在块320处,基于信噪比和深度选择子孔径图像的子集。在实施例中,从预先确定的表格选择子孔径图像的子集,该预先确定的表格包括以不同的噪声比和理论光学目标距光场成像器的的深度的子孔径图像的最优子集。
方法300包括:在块325处,组合子孔径图像的选择的子集。在实施例中,组合子孔径图像的子集包括一起量化的移位和添加子孔径图像的子集。
方法300包括:在块330处,在子孔径图像的组合的子集上执行图像分析。在实施例中,图像分析识别和解码可解码的标记。在另一个实施例中,图像分析是面部识别。在又另一个实施例中,图像分析是虹膜识别。
因此,本文所述的方法最优地选择SAI的集,其在量化的移位和添加重聚焦之后产生具有相对于常规方法的尽可能少的计算的可识别的图像。利用对散焦的健壮性和识别算法的动态范围允许选择需被组合的最少数量的SAI,并且操纵用其将那些SAI配准到彼此的精度。共同地优化SAI的数量和配准的精度获得优于传统方法的高达99%的计算复杂性减少。
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2012年2月7日(Feng等人)提交的编号13/367978、用于采用基于弹性U形铰的激光扫描组件的激光扫描模块的美国专利申请;
2013年6月19日(Fitch等人)提交的编号29/458405、用于电子设备的美国专利申请;
2013年7月2日(London等人)提交的编号29/459620、用于电子设备外壳的美国专利申请;
2013年9月26日(Oberpriller等人)提交的编号29/468118、用于电子设备箱的美国专利申请;
2014年1月8日(Colavito等人)提交的编号14/150393、用于具有整体结构扫描仪的标记读取器的美国专利申请;
2014年3月7日(Feng等人)提交的编号14/200405、用于尺寸受限的应用的标记读取器的美国专利申请;
2014年4月1日(Van Horn等人)提交的编号14/231898、用于用手指运动触发的手工安装的标记读取设备的美国专利申请;
2014年4月2日(Oberpriller等人)提交的编号29/486759、用于成像终端的美国专利申请;
2014年4月21日(Showering)提交的编号14/257364、用于使用近场通信的对接系统和方法的美国专利申请;
2014年4月29日(Ackley等人)提交的编号14/264173、用于标记读取器的自动聚焦透镜系统的美国专利申请;
2014年5月14日(Jovanovski等人)提交的编号14/277337、用于多用途光学读取器的美国专利申请;
2014年5月21日(Liu等人)提交的编号14/283282、用于具有照明和聚焦控制的终端的美国专利申请;
2014年7月10日(Hejl)提交的编号14/327827、用于电子交易的移动电话适配器的美国专利申请;
2014年7月18日(Hejl)提交的编号14/334934、用于标记验证的系统和方法的美国专利申请;
2014年7月24日(Xian等人)提交的编号14/339708、用于激光扫描码符号读取系统的美国专利申请;
2014年7月25日(Rueblinger等人)提交的编号14/340627、用于轴心增强的柔性扫描元件的美国专利申请;
2014年7月30日(Good等人)提交的编号14/446391、用于具有光学签名捕获的多功能销售点装置的美国专利申请;
2014年8月6日(Todeschini)提交的编号14/452697、用于交互式标记读取器的美国专利申请;
2014年8月6日(Li等人)提交的编号14/453019、用于具有引导的对准的量尺寸系统的美国专利申请;
2014年8月19日(Todeschini等人)提交的编号14/462801、用于具有数据认知软件的移动计算设备的美国专利申请;
2014年9月10日(McCloskey等人)提交的编号14/483056、用于场条形码扫描仪的可变深度的美国专利申请;
2014年10月14日(Singel等人)提交的编号14/513808、用于识别存储设施中的库存物品的美国专利申请;
2014年10月21日(Laffargue等人)提交的编号14/519195、用于具有反馈的手持量尺寸系统的美国专利申请;
2014年10月21日(Thuries等人)提交的编号14/519179、用于具有多径干扰减轻的量尺寸系统的美国专利申请;
2014年10月21日(Ackley等人)提交的编号14/519211、用于量尺寸的系统和方法的美国专利申请;
2014年10月21日(Laffargue等人)提交的编号14/519233、用于具有数据质量指示的手持量尺寸器的美国专利申请;
2014年10月21日(Ackley等人)提交的编号14/519249、用于具有测量一致反馈的手持量尺寸系统的美国专利申请;
2014年10月29日(Braho等人)提交的编号14/527191、用于使用期望的响应中的通配符识别话音的方法和系统的美国专利申请;
2014年10月31日(Schoon等人)提交的编号14/529563、用于移动计算设备的适应性接口的美国专利申请;
2014年10月31日(Todeschini等人)提交的编号14/529857、用于具有安全特性的条形码读取器的美国专利申请;
2014年11月3日(Bian等人)提交的编号14/398542、用于具有在控制应用单元中使用的分离位置触发单元的便携式电子设备的美国专利申请;
2014年11月3日(Miller等人)提交的编号14/531154、用于通过检查指导检查器的美国专利申请;
2014年11月5日(Todeschini)提交的编号14/533319、用于使用具有嵌入式相机的可穿戴设备的条形码扫描系统的美国专利申请;
2014年11月7日(Braho等人)提交的编号14/535764、用于话音识别的连接的期望的响应的美国专利申请;
2014年12月12日(Todeschini)提交的编号14/568305、用于标记读取器的自动对比取景器的美国专利申请;
2014年12月17日(Goldsmith)提交的编号14/573022、用于动态诊断指标生成的美国专利申请;
2014年12月22日(Ackley等人)提交的编号14/578627、用于安全系统和方法的美国专利申请;
2014年12月23日(Bowles)提交的编号14/580262、用于热转印打印机的介质门的美国专利申请;
2015年1月6日(Payne)提交的编号14/590024、用于运载工具的架设和包装定位系统的美国专利申请;
2015年1月14日(Ackley)提交的编号14/596757、用于检测条形码打印误差的系统和方法的美国专利申请;
2015年1月21日(Chen等人)提交的编号14/416147、用于具有可变设置的光学读取装置的美国专利申请;
2015年2月5日(Oberpriller等人)提交的编号14/614706、用于支撑用户的手上的电子工具的设备的美国专利申请;
2015年2月5日(Morton等人)提交的编号14/614796、用于货物分配技术的美国专利申请;
2015年2月6日(Bidwell等人)提交的编号29/516892、用于台式电脑的美国专利申请;
2015年2月11日(Pecorari)提交的编号14/619093、用于训练话音识别系统的方法的美国专利申请;
2015年2月23日(Todeschini)提交的编号14/628708、用于确定结帐通道状态的设备、系统和方法的美国专利申请;
2015年2月25日(Gomez等人)提交的编号14/630841、用于包括成像组件的终端的美国专利申请;
2015年3月2日(Sevier)提交的编号14/635346、用于由编码的信息读取终端处理的可靠存储和转发数据的系统和方法的美国专利申请;
2015年3月2日(Zhou等人)提交的编号29/519017、用于扫描仪的美国专利申请;
2015年3月9日(Zhu等人)提交的编号14/405278、用于安全存储的设计模式的美国专利申请;
2015年3月18日(Kearney等人)提交的编号14/660970、用于具有组合照明的可解码标记读取终端的美国专利申请;
2015年3月18日(Soule等人)提交的编号14/661013、用于包括编程符号的设备的重新编程系统和方法的美国专利申请;
2015年3月19日(Van Horn等人)提交的编号14/662922、用于多功能销售点系统的美国专利申请;
2015年3月20日(Davis等人)提交的编号14/663638、用于具有可配置的点火开关行为的车载电脑的美国专利申请;
2015年3月20日(Todeschini)提交的编号14/664063、用于用智能设备扫描条形码、同时在智能设备显示器上连续运行和显示应用的方法及应用的美国专利申请;
2015年3月26日(Funyak等人)提交的编号14/669280、用于将网页的组件转换成语音提示的美国专利申请;
2015年3月31日(Bidwell)提交的编号14/674329、用于条形码扫描的瞄准器的美国专利申请;
2015年4月1日(Huck)提交的编号14/676109、用于标记读取器的美国专利申请;
2015年4月1日(Yeakley等人)提交的编号14/676327、用于安全设备的设备管理代理的美国专利申请;
2015年4月2日(Showering)提交的编号14/676898、用于被配置成集成运动传感设备输入的导航系统的美国专利申请;
2015年4月6日(Laffargue等人)提交的编号14/679275、用于量尺寸系统校准系统和方法的美国专利申请;
2015年4月7日(Bidwell等人)提交的编号29/523098、用于平板电脑的手柄的美国专利申请;
2015年4月9日(Murawski等人)提交的编号14/682615、用于移动设备的电源管理的系统和方法的美国专利申请;
2015年4月15日(Qu等人)提交的编号14/686822、用于多平台支持系统和方法的美国专利申请;
2015年4月15日(Kohtz等人)提交的编号14/687289、用于经由外围集线器通信的系统的美国专利申请;
2015年4月17日(Zhou等人)提交的编号29/524186、用于扫描仪的美国专利申请;
2015年4月24日(Sewell等人)提交的编号14/695364、用于药物管理系统的美国专利申请;
2015年4月24日(Kubler等人)提交的编号14/695923、用于安全的无监视的网络认证的美国专利申请;
2015年4月27日(Schulte等人)提交的编号29/525068、用于具有可移动扫描设备的平板电脑的美国专利申请;
2015年4月29日(Nahill等人)提交的编号14/699436、用于具有预测性诊断的符号读取系统的美国专利申请;
2015年5月1日(Todeschini等人)提交的编号14/702110、用于将条形码数据注入调节到智能设备上运行的应用的系统和方法的美国专利申请;
2015年5月4日(Young等人)提交的编号14/702979、用于跟踪电池情况的美国专利申请;
2015年5月5日(Charpentier等人)提交的编号14/704050、用于中间线性定位的美国专利申请;
2015年5月6日(Fitch等人)提交的编号14/705012、用于响应于车辆驾驶员的免提人机界面的美国专利申请;
2015年5月6日(Hussey等人)提交的编号14/705407、用于保护基于软件的网络连接的设备免于高级持续威胁的方法和系统的美国专利申请;
2015年5月8日(Chamberlin)提交的编号14/707037、用于使用车载电脑显示信息的系统和方法的美国专利申请;
2015年5月8日(Pape)提交的编号14/707123、用于应用独立的DEX/UCS接口的美国专利申请;
2015年5月8日(Smith等人)提交的编号14/707492、用于使用多个数据源读取光学标记的方法和装置的美国专利申请;
2015年5月13日(Smith)提交的编号14/710666、用于编码的信息读取终端的预付使用系统的美国专利申请;
2015年5月14日(Fitch等人)提交的编号29/526918、用于充电座的美国专利申请;
2015年5月19日(Venkatesha等人)提交的编号14/715672、用于增强现实使能的危险显示的美国专利申请;
2015年5月19日(Ackley)提交的编号14/715916、用于评估图像值的美国专利申请;
2015年5月27日(Showering等人)提交的编号14/722608、用于捕获图像信号中的文档的交互式用户界面的美国专利申请;
2015年5月27日(Oberpriller等人)提交的编号29/528165、用于柜台内条形码扫描仪的美国专利申请;
2015年5月28日(Wang等人)提交的编号14/724134、用于具有无线路径选择能力的电子设备的美国专利申请;
2015年5月29日(Barten)提交的编号14/724849、用于在标记读取设备中编程默认电缆接口软件的方法的美国专利申请;
2015年5月29日(Barber等人)提交的编号14/724908、用于具有成像组件的成像装置的美国专利申请;
编号14/725352、用于监测一个或多个便携式数据终端的装置和方法(Caballero的等人)的美国专利申请;
2015年5月29日(Fitch等人)提交的编号29/528590、用于电子设备的美国专利申请;
2015年6月2日(Fitch等人)提交的编号29/528890、用于移动电脑机壳的美国专利申请;
2015年6月2日(Caballero)提交的编号14/728397、用于使用虚拟接口对相关申请交叉引用的设备管理的美国专利申请;
2015年6月8日(Powilleit)提交的编号14/732870、用于数据采集模块和系统的美国专利申请;
2015年6月8日(Zhou等人)提交的编号29/529441、用于标记读取设备的美国专利申请;
2015年6月10日(Todeschini)提交的编号14/735717、用于具有与用户的神经系统的接口的标记读取系统的美国专利申请;
2015年6月12日(Amundsen等人)提交的编号14/738038、用于检测对象称重干扰的方法和系统的美国专利申请;
2015年6月16日(Bandringa)提交的编号14/740320、用于移动电子设备的触觉开关的美国专利申请;
2015年6月16日(Ackley等人)提交的编号14/740373、用于校准体积量尺寸器的美国专利申请;
2015年6月18日(Xian等人)提交的编号14/742818、用于采用数字增益控制的标记读取系统的美国专利申请;
2015年6月18日(Wang等人)提交的编号14/743257、用于无线网点便携式数据终端的美国专利申请;
2015年6月18日(Vargo等人)提交的编号29/530600、用于除尘器的美国专利申请;
2015年6月19日(Wang)提交的编号14/744633、用于包括具有共享全局快门电路的图像传感器阵列的成像装置的美国专利申请;
2015年6月19日(Todeschini等人)提交的编号14/744836、用于读取可解码的标记的基于云的系统的美国专利申请;
2015年6月19日(Todeschini等人)提交的编号14/745006、用于解码的消息数据的选择性输出的美国专利申请;
2015年6月23日(Thuries等人)提交的编号14/747197、用于光学图案投影仪的美国专利申请;
2015年6月23日(Jovanovski等人)提交的编号14/77490、用于双投影仪的三维扫描仪的美国专利申请;以及
2015年6月24日(Xie等人)提交的编号14/748446、用于具有用于无线充电和EAS去激活的多功能线圈的无绳标记读取器的美国专利申请。
Claims (15)
1.一种成像设备,包括:
光场成像器,包括:
微透镜阵列;以及
光场传感器,被定位在靠近微透镜阵列,具有多个像素并且记录来自穿过微透镜阵列的光的光学目标的光场数据;以及
处理器,被配置成:
从光场传感器接收光学目标的光场数据;
估计光场数据中的信噪比以及光学目标的深度;
基于信噪比和深度选择子孔径图像的子集;
组合子孔径图像的选择的子集;以及
在子孔径图像的组合的子集上执行图像分析。
2.根据权利要求1所述的成像设备,其中光场数据包括多个子孔径图像。
3.根据权利要求2所述的成像设备,其中使用传感器特性来执行估计信噪比。
4.根据权利要求3所述的成像设备,其中传感器特征包括传感器噪声水平、增益、曝光时间或其任何组合。
5.根据权利要求4所述的成像设备,其中根据像素强度确定传感器特征。
6.根据权利要求1所述的成像设备,其中根据光场数据直接地确定估计光学目标距光场成像器的深度。
7.根据权利要求1所述的成像设备,其中通过配准两个子孔径图像估计光学目标距光场成像器的深度。
8.根据权利要求1所述的成像设备,包括:以不同的噪声比和理论光学目标距光场成像器的深度的子孔径图像的最优子集的预定表格。
9.根据权利要求8所述的成像设备,其中从预定的表格选择子孔径图像的子集。
10.根据权利要求1所述的成像设备,其中组合子孔径图像的子集包括一起量化的移位和添加子孔径图像的子集。
11.根据权利要求1所述的成像设备,其中成像设备是标记扫描仪。
12.根据权利要求1所述的成像设备,其中图像分析是面部识别。
13.根据权利要求1所述的成像设备,其中图像分析是虹膜识别。
14.一种方法,包括:
用光场传感器捕获光学目标的光场数据;
估计光场数据中的信噪比以及光学目标的深度;
基于信噪比和深度选择子孔径图像的子集;
组合子孔径图像的选择的子集;以及
在子孔径图像的组合的子集上执行图像分析。
15.根据权利要求14所述的方法,其中光场数据包括多个子孔径图像。
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US20160316190A1 (en) | 2016-10-27 |
EP3086281A1 (en) | 2016-10-26 |
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