公开(公告)号：US20150326845A1

公开(公告)日：2015-11-12

申请号：US14697804

申请日：2015-04-28

申请人： Lingyan LIANG ,  Ying ZHAO ,  Liyan LIU

发明人： Lingyan LIANG ,  Ying ZHAO ,  Liyan LIU

IPC分类号： H04N13/00 ,  G06T7/00 ,  G06F3/01

CPC分类号： G06T7/0097 ,  G06F3/017 ,  G06T5/002 ,  G06T5/50 ,  G06T7/11 ,  G06T7/174 ,  G06T7/187 ,  G06T7/194 ,  G06T2200/04 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/30196 ,  H04N13/128

摘要： Disclosed is a method comprising steps of conducting image preprocessing so as to respectively obtain a candidate object region including a foreground image in a depth map and a color image; determining whether it is necessary to conduct a region growing process with respect to the candidate object region of the depth map; in the former case, conducting the region growing process with respect to the candidate object region of the depth map; and conducting, after the region growing process is conducted with respect to the candidate object region of the depth map, a depth value restoration process with respect to a candidate region.

摘要翻译： 公开了一种方法，包括进行图像预处理以分别获得包括深度图和彩色图像中的前景图像的候选对象区域的步骤; 确定是否需要针对深度图的候选对象区域进行区域增长处理; 在前一种情况下，对深度图的候选对象区域进行区域增长处理; 并且在相对于深度图的候选对象区域进行区域生长处理之后进行相对于候选区域的深度值恢复处理。

公开(公告)号：US20150055828A1

公开(公告)日：2015-02-26

申请号：US14461558

申请日：2014-08-18

申请人： Ying ZHAO ,  Liyan LIU ,  Lingyan LIANG

发明人： Ying ZHAO ,  Liyan LIU ,  Lingyan LIANG

IPC分类号： G06T7/20 ,  G06K9/00 ,  G01S17/50

CPC分类号： G06K9/00355 ,  G01S17/50 ,  G06T7/20 ,  G06T7/215 ,  G06T7/248 ,  G06T2207/10028 ,  G06T2207/20021 ,  G06T2207/30196

摘要： A moving object detection method includes acquiring two depth image frames including depth information, which are obtained by continuously taking images of a moving object, the two depth image frames including a present depth image frame and at least one past depth image frame; dividing each of the two depth image frames into a plurality of blocks; calculating differences between numbers of pixels positioned in respective different depth areas in each of the plurality of blocks in the present depth image frame, and numbers of pixels positioned in respective different depth areas in each of the corresponding plurality of blocks in each of the at least one past depth image frame, which correspond to the plurality of blocks in the present depth image frame; and detecting a moving block in the present depth image frame based on the calculated difference and constituting the detected moving object.

摘要翻译： 移动物体检测方法包括获取包括深度信息的两个深度图像帧，所述深度信息通过连续拍摄移动物体的图像，所述两个深度图像帧包括当前深度图像帧和至少一个过去深度图像帧; 将两个深度图像帧中的每一个划分为多个块; 计算位于本深度图像帧中的多个块中的每一个中的相应不同深度区域中的像素的数量之间的差异，以及位于每个至少在每个的相应多个块中的相应不同深度区域中的像素的数量 一个过去的深度图像帧，其对应于当前深度图像帧中的多个块; 并且基于所计算的差异检测当前深度图像帧中的移动块，并构成检测到的移动对象。

公开(公告)号：US20160117832A1

公开(公告)日：2016-04-28

申请号：US14918680

申请日：2015-10-21

申请人： Liyan LIU ,  Ying ZHAO

发明人： Liyan LIU ,  Ying ZHAO

IPC分类号： G06T7/00 ,  G06T7/40 ,  G06T7/20

CPC分类号： G06T7/174 ,  G06T7/194 ,  G06T7/215 ,  G06T2207/10028

摘要： A method and an apparatus for separating a foreground image are disclosed. The method includes obtaining an input image and depth information of the input image; roughly dividing the input image to obtain a rough foreground region based on the depth information of the input image; obtaining motion information of the input image, and generating a three-color image from the rough foreground region based on the motion information; and separating the foreground image from the generated three-color image. According to the method, the three-color image is generated based on the depth information and the motion information, thus a foreground object can be accurately separated from the three-color image.

摘要翻译： 公开了一种用于分离前景图像的方法和装置。 该方法包括获得输入图像和输入图像的深度信息; 基于输入图像的深度信息粗略地划分输入图像以获得粗糙的前景区域; 获取所述输入图像的运动信息，以及基于所述运动信息从所述粗糙前景区域生成三色图像; 以及从生成的三色图像分离前景图像。 根据该方法，基于深度信息和运动信息生成三色图像，因此可以将前景对象与三色图像精确地分离。

公开(公告)号：US20170032183A1

公开(公告)日：2017-02-02

申请号：US15211924

申请日：2016-07-15

申请人： Chao CHEN ,  Liyan LIU ,  Ying ZHAO

发明人： Chao CHEN ,  Liyan LIU ,  Ying ZHAO

IPC分类号： G06K9/00 ,  G06T7/00 ,  H04N5/225

CPC分类号： G06K9/00335 ,  G06K9/00375 ,  G06K9/48

摘要： An image processing method, device and non-transitory computer-readable recording medium are disclosed. The image processing method includes obtaining an object image including an operating body, extracting the operating body from the object image, and calculating, by processing circuitry, an orientation of the operating body in the object image. The method further includes determining, by the processing circuitry, whether the operating body is of an operator that operates an apparatus based on the orientation of the operation body.

摘要翻译： 公开了一种图像处理方法，装置和非暂时计算机可读记录介质。 图像处理方法包括获得包括操作体的对象图像，从对象图像提取操作体，以及通过处理电路计算操作体在对象图像中的取向。 该方法还包括由处理电路确定操作体是否是基于操作体的取向操作设备的操作者。

公开(公告)号：US20160133027A1

公开(公告)日：2016-05-12

申请号：US14930009

申请日：2015-11-02

申请人： Ying ZHAO ,  Gang WANG ,  Liyan LIU

发明人： Ying ZHAO ,  Gang WANG ,  Liyan LIU

IPC分类号： G06T7/00 ,  G06K9/46 ,  G06K9/52 ,  G06T7/40

CPC分类号： G06T7/11 ,  G06K9/0014 ,  G06K9/3233 ,  G06T7/194 ,  G06T2207/10004 ,  G06T2207/10028

摘要： A method and an apparatus for separating a foreground image are disclosed. The method includes obtaining an input image, and color information and depth information of the input image; roughly dividing, based on the depth information of the input image, the input image to obtain an initial three-color image; reducing or expanding, based on the color information of the input image, an unknown region in the initial three-color image to obtain an optimized three-color image; and separating the foreground image from the optimized three-color image. According to the method, the initial three-color image can be optimized based on the color information of the input image, so that a more accurate three-color image can be obtained; thus the foreground image can be accurately separated from the three-color image.

摘要翻译： 公开了一种用于分离前景图像的方法和装置。 该方法包括获得输入图像，输入图像的颜色信息和深度信息; 基于输入图像的深度信息粗略地划分输入图像以获得初始三色图像; 基于输入图像的颜色信息，减少或扩大初始三色图像中的未知区域以获得优化的三色图像; 并且将前景图像与优化的三色图像分离。 根据该方法，可以基于输入图像的颜色信息优化初始三色图像，从而可以获得更准确的三色图像; 因此可以将前景图像与三色图像精确地分离。

公开(公告)号：US20150243038A1

公开(公告)日：2015-08-27

申请号：US14629650

申请日：2015-02-24

申请人： Ying ZHAO

发明人： Ying ZHAO

IPC分类号： G06T7/00 ,  G06T7/20 ,  G06K9/00 ,  G06T1/00

CPC分类号： G06T7/254 ,  G06K9/00348 ,  G06K9/4671 ,  G06T7/246 ,  G06T2207/10021 ,  G06T2207/10028 ,  G06T2207/30196

摘要： A method and an apparatus for expressing a motion object are disclosed. The method includes obtaining a stereo image in which the motion object has been captured, the stereo image including a depth image; extracting a key point from the motion object in the stereo image; determining, based on statistical information relating to three-dimensional motion of pixels within a first predetermined region surrounding the key point, a dominant direction of the key point; determining, based on the dominant direction of the key point, motion vectors of pixels within a second predetermined region surrounding the key point to obtain rotation invariant motion vectors; and extracting, based on the determined motion vectors of the pixels within the second predetermined region surrounding the key point, a feature describing the key point. The present invention can extract features of motion object that are irrelevant to a viewing angle of a camera.

摘要翻译： 公开了一种用于表达运动对象的方法和装置。 该方法包括获取其中已经捕获了运动对象的立体图像，立体图像包括深度图像; 从立体图像中的运动对象提取关键点; 基于关于所述关键点周围的第一预定区域内的像素的三维运动的统计信息，确定所述关键点的主导方向; 基于关键点的主要方向，确定围绕所述关键点的第二预定区域内的像素的运动矢量以获得旋转不变运动矢量; 以及基于所确定的围绕所述关键点的所述第二预定区域内的像素的运动矢量来提取描述所述关键点的特征。 本发明可以提取与摄像机的视角无关的运动对象的特征。

公开(公告)号：US20110295783A1

公开(公告)日：2011-12-01

申请号：US13204713

申请日：2011-08-07

申请人： Ying ZHAO ,  Charles C. ZHOU ,  Chetan KOTAK

发明人： Ying ZHAO ,  Charles C. ZHOU ,  Chetan KOTAK

IPC分类号： G06F15/18

CPC分类号： G06F17/30702

摘要： The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules.

摘要翻译： 本发明公开了多域异常检测系统和方法的各种实施例。 在本发明的一个实施例中，多域异常检测系统使用每个域的通用学习过程，基于每个域的数据观察为每个域创建“正常数据简档”，其中可以使用每个域的正常数据简档 确定和计算每个域的特定于异常的数据。 然后，可以在使用一个或多个融合规则的跨域融合数据分析中一起分析每个域的域特异性异常数据。 融合规则可能涉及到来自多个域的域特异性异常数据的比较，以导出用于特定跨域分析任务的多域异常得分计。 多域异常检测系统及其相关方法还可以利用每个域的域特异性异常指标，使用融合规则导出跨域异常指标。

公开(公告)号：US20140088883A1

公开(公告)日：2014-03-27

申请号：US14093520

申请日：2013-12-02

申请人： Charles C. ZHOU ,  Ying ZHAO

发明人： Charles C. ZHOU ,  Ying ZHAO

IPC分类号： G06F19/24

CPC分类号： G06F19/24 ,  G06F19/12 ,  G06F19/18

摘要： Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.

摘要翻译： 在本发明的各种实施例中公开了用于基于期望的代谢性质或生物学行为选择微生物的知识模式搜索和分析的方法和系统。 在本发明的一个实施方案中，用于选择目的特异性微生物的计算机实现的方法首先通过将每个微生物的产甲烷，氢，电，另一代谢性质和/或其他生物学行为与遗传和化学指纹 代谢和能量生成途径。 然后，基于微生物的编制概况，计算机实现的方法使用在计算机系统上执行的机器学习和模式识别将微生物组合成路径特征，并且随后产生称为“发现特征”的预期以获得期望的代谢性质 或至少一种微生物的所需生物学行为。 此外，可以计算轮廓匹配分数，以指示生物废物或废水中一种或多种微生物用于可再生能源产生的有用性。

公开(公告)号：US20120143800A1

公开(公告)日：2012-06-07

申请号：US13371469

申请日：2012-02-12

申请人： Charles C. ZHOU ,  Ying ZHAO

发明人： Charles C. ZHOU ,  Ying ZHAO

IPC分类号： G06F15/18

CPC分类号： G06F19/24 ,  G06F19/18

摘要： Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.

摘要翻译： 在本发明的各种实施例中公开了用于基于期望的代谢性质或生物学行为选择微生物的知识模式搜索和分析的方法和系统。 在本发明的一个实施方案中，用于选择目的特异性微生物的计算机实现的方法首先通过将每个微生物的产甲烷，氢，电，另一代谢性质和/或其他生物学行为与遗传和化学指纹 代谢和能量生成途径。 然后，基于微生物的编制概况，计算机实现的方法使用在计算机系统上执行的机器学习和模式识别将微生物组合成路径特征，并且随后产生称为“发现特征”的预期以获得期望的代谢性质 或至少一种微生物的所需生物学行为。 此外，可以计算轮廓匹配分数，以指示生物废物或废水中一种或多种微生物用于可再生能源产生的有用性。