公开(公告)号：US07706571B2

公开(公告)日：2010-04-27

申请号：US11250181

申请日：2005-10-13

Applicant: Subhodev Das ,  Manoj Aggarwal ,  Harpreet Singh Sawhney ,  Rakesh Kumar ,  Supun Samarasekera

Inventor： Subhodev Das ,  Manoj Aggarwal ,  Harpreet Singh Sawhney ,  Rakesh Kumar ,  Supun Samarasekera

IPC: G06K9/00

CPC classification number: G06K9/32 ,  G06K9/00369 ,  G06T7/251 ,  G06T7/277 ,  G06T2207/10016 ,  G06T2207/30196 ,  G06T2207/30232

Abstract: Method for tracking an object recorded within a selected frame of a sequence of frames of video data, using a plurality of layers, where at least one object layer of the plurality of layers represents the object includes initializing layer ownership probabilities for pixels of the selected frame using a non-parametric motion model, estimating a set of motion parameters of the plurality of layers for the selected frame using a parametric maximization algorithm and tracking the object. The non-parametric motion model is optical flow and includes warping the mixing probabilities, the appearances of the plurality of layers, and the observed pixel data from the pixels of the preceding frame to the pixels of the selected frame to initialize the layer ownership probabilities for the pixels of the selected frame.

Abstract translation: 用于使用多个层来跟踪记录在视频数据帧序列的所选帧内的对象的方法，其中多个层中的至少一个对象层表示对象包括初始化所选帧的像素的层所有权概率 使用非参数运动模型，使用参数最大化算法估计所选择的帧的所述多个层的运动参数的集合并跟踪所述对象。 非参数运动模型是光流，包括混合概率，多层的出现以及从前一帧的像素到所选帧的像素的观察像素数据的变形，以初始化层所有权概率 所选帧的像素。

公开(公告)号：US08243991B2

公开(公告)日：2012-08-14

申请号：US12486316

申请日：2009-06-17

Applicant: Subhodev Das ,  Yi Tan ,  Ming-Yee Chiu ,  Andrew Coppock ,  Feng Han

Inventor： Subhodev Das ,  Yi Tan ,  Ming-Yee Chiu ,  Andrew Coppock ,  Feng Han

IPC: G06K9/00

CPC classification number: G06K9/3241 ,  G06K9/4642 ,  G06K9/6857

Abstract: A system and method for detecting a target in imagery is disclosed. At least one image region exhibiting changes in at least intensity is detected from among at least a pair of aligned images. A distribution of changes in at least intensity inside the at least one image region is determined using an unsupervised learning method. The distribution of changes in at least intensity is used to identify pixels experiencing changes of interest. At least one target from the identified pixels is identified using a supervised learning method. The distribution of changes in at least intensity is a joint hue and intensity histogram when the pair of images pertain to color imagery. The distribution of changes in at least intensity is an intensity histogram when the pair of images pertain to grey-level imagery.

Abstract translation: 公开了一种用于检测图像中的目标的系统和方法。 从至少一对对准的图像中检测至少一个表现出至少强度变化的图像区域。 使用无监督学习方法确定至少一个图像区域内的至少强度的变化的分布。 使用至少强度变化的分布来识别经历感兴趣变化的像素。 使用监督学习方法来识别来自所识别的像素的至少一个目标。 至少强度变化的分布是当一对图像属于彩色图像时的联合色调和强度直方图。 至少强度变化的分布是当一对图像属于灰度图像时的强度直方图。

公开(公告)号：US20100092036A1

公开(公告)日：2010-04-15

申请号：US12486316

申请日：2009-06-17

Applicant: Subhodev Das ,  Yi Tan ,  Ming-Yee Chiu ,  Andrew Coppock ,  Feng Han

Inventor： Subhodev Das ,  Yi Tan ,  Ming-Yee Chiu ,  Andrew Coppock ,  Feng Han

IPC: G06K9/00

CPC classification number: G06K9/3241 ,  G06K9/4642 ,  G06K9/6857

Abstract: A system and method for detecting a target in imagery is disclosed. At least one image region exhibiting changes in at least intensity is detected from among at least a pair of aligned images. A distribution of changes in at least intensity inside the at least one image region is determined using an unsupervised learning method. The distribution of changes in at least intensity is used to identify pixels experiencing changes of interest. At least one target from the identified pixels is identified using a supervised learning method. The distribution of changes in at least intensity is a joint hue and intensity histogram when the pair of images pertain to color imagery. The distribution of changes in at least intensity is an intensity histogram when the pair of images pertain to grey-level imagery.

Abstract translation: 公开了一种用于检测图像中的目标的系统和方法。 从至少一对对准的图像中检测至少一个表现出至少强度变化的图像区域。 使用无监督学习方法确定至少一个图像区域内的至少强度的变化的分布。 使用至少强度变化的分布来识别经历感兴趣变化的像素。 使用监督学习方法来识别来自所识别的像素的至少一个目标。 至少强度变化的分布是当一对图像属于彩色图像时的联合色调和强度直方图。 至少强度变化的分布是当一对图像属于灰度图像时的强度直方图。

公开(公告)号：US5953076A

公开(公告)日：1999-09-14

申请号：US662089

申请日：1996-06-12

Applicant: Brian Astle ,  Subhodev Das

Inventor： Brian Astle ,  Subhodev Das

IPC: H04N5/265 ,  G06T7/20 ,  H04N5/14 ,  H04N5/272 ,  H04N9/74

CPC classification number: H04N5/2723 ,  G06T7/2033 ,  H04N5/272 ,  H04N5/145

Abstract: A system and method for realtime occlusion processing for seamlessly and realistically blending an inserted image such as an advertisement into a region of a live broadcast image without obscuring the action of the live image. The average color and intensity of a synthetic reference image containing at least some of the region to be replaced is compared to the average color and intensity of the current live broadcast image to determine the difference between the two images. The resulting difference image obtained from processing the current image and synthetic, reference image determines areas of the intended insertion region within the current image which are obscured by live action. The processor then generates an occlusion mask based on the difference image and only those pixels that are unoccluded within the intended insertion region are allowed to be inserted into the live broadcast.

Abstract translation: 一种用于实时闭塞处理的系统和方法，用于将诸如广告的插入图像无缝地和实际地混合到实况广播图像的区域中，而不会模糊实时图像的动作。 将包含要替换的区域中的至少一些的合成参考图像的平均颜色和强度与当前实时广播图像的平均颜色和强度进行比较，以确定两个图像之间的差异。 由处理当前图像和合成参考图像得到的差分图像确定当前图像内的预期插入区域的区域，这些区域被实时动作遮蔽。 然后处理器基于差分图像生成遮挡掩模，并且仅允许在预期插入区域内未被占用的那些像素插入到实况广播中。

公开(公告)号：US5808695A

公开(公告)日：1998-09-15

申请号：US580892

申请日：1995-12-29

Applicant: Roy J. Rosser ,  Subhodev Das ,  Yi Tan

Inventor： Roy J. Rosser ,  Subhodev Das ,  Yi Tan

IPC: H04N5/265 ,  G06T1/00 ,  G06T3/00 ,  G06T7/20 ,  H04N5/14 ,  H04N5/272 ,  H04N7/18 ,  H04N9/74 ,  H04N5/76

CPC classification number: H04N5/272 ,  G06T7/204 ,  H04N5/2723 ,  G06T2207/10016 ,  G06T2207/30228 ,  H04N5/145

Abstract: A method for tracking motion from field to field in a sequence of related video broadcast images. The method uses template correlation to follow a set of predetermined landmarks within a scene in order to provide position information of objects in the current image. The current image object position information is compared to position information of the same objects within a reference array data table. The comparison is accomplished through the use of warp equations that map points in the current image to points in the reference array. Motion is tracked according to a velocity prediction scheme utilizing a weighted formula that emphasizes the weight of landmarks that are closer to their predicted position.

Abstract translation: 一种在相关视频广播图像序列中跟踪从场到场的运动的方法。 该方法使用模板相关性来跟随场景内的一组预定的界标，以便提供当前图像中的对象的位置信息。 将当前图像对象位置信息与参考阵列数据表中的相同对象的位置信息进行比较。 比较通过使用将当前图像中的点与参考阵列中的点进行映射的翘曲方程来实现。 根据速度预测方案跟踪运动，利用加权公式强调更靠近其预测位置的地标的重量。

公开(公告)号：US5627915A

公开(公告)日：1997-05-06

申请号：US381088

申请日：1995-01-31

Applicant: Roy Rosser ,  Subhodev Das ,  Yi Tan ,  Peter von Kaenel

Inventor： Roy Rosser ,  Subhodev Das ,  Yi Tan ,  Peter von Kaenel

IPC: G06K9/68

CPC classification number: H04N5/2723 ,  G06K9/6282

Abstract: A system for inserting images into live video fields includes a method for rapidly and efficiently identifying landmarks and objects. Initially a first template, having a first pattern similar to one of the distinctive features of the object, is passed over the video field and compared to it in order to preliminarily identify at least one possible distinctive feature as a candidate. A second template is then created by taking one of the major elements of the distinctive feature candidate and extending that element all the way across the second template and then comparing it to the distinctive feature candidate. This eliminates one or more possible falsely identified features. A third template is then created having a pattern formed from another major element of said distinctive feature and extending it all the way across the third template. The third template is then likewise passed over the distinctive feature candidate and compared therewith in order to eliminate still further falsely identified features. The method is continued until all possible false alarm candidates have been eliminated. The process is then repeated in order to preliminarily identify two or three landmarks of the target object. The locations of those objects are then compared to a geometric model to further verify if the object has been correctly identified. The methodology can be tested against a video taped program to determine if it accurately identifies objects.

Abstract translation: 用于将图像插入实况视频场的系统包括用于快速有效地识别地标和物体的方法。 最初，具有与对象的一个​​特征相似的第一模式的第一模板在视频场上传递并与之进行比较，以便初步地将至少一个可能的特征特征识别为候选。 然后通过获取特征特征候选的主要元素之一并将该元素一直延伸到第二模板，然后将其与特征特征候选进行比较来创建第二模板。 这消除了一个或多个可能的错误识别的特征。 然后创建具有由所述独特特征的另一主要元素形成的图案并将其一直延伸穿过第三模板的第三模板。 然后，第三个模板同样通过该特征候选，并与之进行比较，以便消除更进一步的错误识别的特征。 该方法继续进行，直到所有可能的假警报候选者都被消除。 然后重复该过程，以便初步识别目标对象的两个或三个地标。 然后将这些对象的位置与几何模型进行比较，以进一步验证对象是否已被正确识别。 该方法可以针对视频录制程序进行测试，以确定它是否准确识别对象。