公开(公告)号：US20080181507A1

公开(公告)日：2008-07-31

申请号：US12011705

申请日：2008-01-28

申请人： Chandan Gope ,  Amit Agarwal ,  Vaidhi Nathan ,  Alexander Bovyrin ,  Ilya Popov ,  Lev Afraimovich

发明人： Chandan Gope ,  Amit Agarwal ,  Vaidhi Nathan ,  Alexander Bovyrin ,  Ilya Popov ,  Lev Afraimovich

IPC分类号： G06K9/46 ,  G06K9/03 ,  G06K9/40 ,  G06K9/36

CPC分类号： H04N5/272 ,  G06T5/002 ,  G06T7/11 ,  G06T7/194 ,  G06T11/60 ,  G06T2207/20012 ,  H04N5/144

摘要： In an embodiment, an image is received having a first portion and one or more other portions. The one or more other portions are replaced with one or more other images. The replacing of the one or more portions results in an image including the first portion and the one or more other images. In an embodiment, the background of an image is replaced with another background. In an embodiment, the foreground is extracted by identifying the background based on an image of the background without any foreground. In an embodiment, the foreground is extracted by identifying portions of the image that have characteristics that are expected to be associated with the background and characteristics that are expected to be associated with foreground. In an embodiment any of the images can be still images. In an embodiment, any of the images are video images.

摘要翻译： 在一个实施例中，接收具有第一部分和一个或多个其它部分的图像。 一个或多个其它部分被一个或多个其他图像替换。 更换一个或多个部分导致包括第一部分和一个或多个其他图像的图像。 在一个实施例中，图像的背景被替换为另一背景。 在一个实施例中，通过基于背景的图像识别背景来提取前景，而没有任何前景。 在一个实施例中，通过识别具有预期与预期与前景相关联的背景和特征的特征的图像的部分来提取前景。 在一个实施例中，任何图像可以是静止图像。 在一个实施例中，任何图像是视频图像。

公开(公告)号：US20190251369A1

公开(公告)日：2019-08-15

申请号：US16272556

申请日：2019-02-11

申请人： Ilya Popov ,  Dmitry Yashunin ,  Semen Budenkov ,  Krishna Khadloya

发明人： Ilya Popov ,  Dmitry Yashunin ,  Semen Budenkov ,  Krishna Khadloya

IPC分类号： G06K9/00 ,  G06T5/20 ,  G06K9/62 ,  G06T5/00

CPC分类号： G06K9/00791 ,  G06K9/6267 ,  G06K2209/01 ,  G06K2209/15 ,  G06T5/002 ,  G06T5/20

摘要： A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.