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21. 发明申请

US20080063300A1 Image registration using joint spatial gradient maximization 有权
标题翻译：使用联合空间梯度最大化的图像配准
公开(公告)号：US20080063300A1
公开(公告)日：2008-03-13
申请号：US11519136
申请日：2006-09-11
申请人： Fatih M. Porikli , Xue Mei
发明人： Fatih M. Porikli , Xue Mei
IPC分类号： G06K9/32 , G06K9/00
CPC分类号： G06K9/6206
摘要： A method registers images by first determining spatial gradient magnitudes for each pixel in each image to provide a corresponding energy image. Each energy image is transformed according to motion parameters associated with the energy image. An average sum of the transformed energy images is maximized. The motion parameters are then updated according to the maximized average sum. The above steps are repeated until a termination condition is reached, and then the images can be registered with each other according to the updated motion parameters.
摘要翻译： A方法通过首先确定每个图像中的每个像素的空间梯度大小来记录图像以提供对应的能量图像。每个能量图像根据与能量图像相关的运动参数进行变换。变换能量图像的平均总和最大化。然后根据最大化的平均和来更新运动参数。重复上述步骤直到达到终止条件，然后可以根据更新的运动参数将图像相互对准。

22. 发明申请

US20080063285A1 Detecting Moving Objects in Video by Classifying on Riemannian Manifolds 有权
标题翻译：通过黎曼流形分类检测视频中的移动物体
公开(公告)号：US20080063285A1
公开(公告)日：2008-03-13
申请号：US11763699
申请日：2007-06-15
申请人： Fatih M. Porikli , Oncel C. Tuzel
发明人： Fatih M. Porikli , Oncel C. Tuzel
IPC分类号： G06K9/46
CPC分类号： G06K9/00369 , G06K9/4642 , G06K9/6257
摘要： A method constructs a classifier from training data and detects moving objects in test data using the trained classifier. High-level features are generated from low-level features extracted from training data. The high level features are positive definite matrices on an analytical manifold. A subset of the high-level features is selected, and an intrinsic mean matrix is determined. Each high-level feature is mapped to a feature vector on a tangent space of the analytical manifold using the intrinsic mean matrix. An untrained classifier is trained with the feature vectors to obtain a trained classifier. Test high-level features are similarly generated from test low-level features. The test high-level features are classified using the trained classifier to detect moving objects in the test data.
摘要翻译：一种方法从训练数据构建分类器，并使用经过训练的分类器检测测试数据中的移动对象。高级功能是从训练数据中提取的低级功能产生的。高级特征是分析歧管上的正定矩阵。选择高级特征的子集，并确定固有均值矩阵。每个高级特征使用内在平均矩阵映射到分析歧管的切线空间上的特征向量。使用特征向量训练未训练的分类器以获得训练有素的分类器。测试高级功能类似地从测试低级功能生成。测试高级功能使用训练有素的分类器进行分类，以检测测试数据中的移动对象。

23. 发明授权

US07142602B2 Method for segmenting 3D objects from compressed videos 失效
标题翻译：从压缩视频分割3D对象的方法
公开(公告)号：US07142602B2
公开(公告)日：2006-11-28
申请号：US10442417
申请日：2003-05-21
申请人： Fatih M. Porikli , Huifang Sun , Ajay Divakaran
发明人： Fatih M. Porikli , Huifang Sun , Ajay Divakaran
IPC分类号： H04B1/66
CPC分类号： G06K9/34 , G06T7/11 , G06T7/187 , G06T2207/10016 , G06T2207/20048 , G06T2207/20101 , H04N19/48 , H04N19/87
摘要： A method segments a video into objects, without user assistance. An MPEG compressed video is converted to a structure called a pseudo spatial/temporal data using DCT coefficients and motion vectors. The compressed video is first parsed and the pseudo spatial/temporal data are formed. Seeds macro-blocks are identified using, e.g., the DCT coefficients and changes in the motion vector of macro-blocks.A video volume is “grown” around each seed macro-block using the DCT coefficients and motion distance criteria. Self-descriptors are assigned to the volume, and mutual descriptors are assigned to pairs of similar volumes. These descriptors capture motion and spatial information of the volumes. Similarity scores are determined for each possible pair-wise combination of volumes. The pair of volumes that gives the largest score is combined iteratively. In the combining stage, volumes are classified and represented in a multi-resolution coarse-to-fine hierarchy of video objects.
摘要翻译：一种方法是将视频分割成对象，而无需用户帮助。使用DCT系数和运动矢量将MPEG压缩视频转换成称为伪空间/时间数据的结构。首先解压缩视频并形成伪空间/时间数据。使用例如DCT系数和宏块的运动矢量的变化来识别种子宏块。使用DCT系数和运动距离标准，在每个种子宏块周围“生长”视频量。自描述符被分配给卷，并且相互描述符被分配给相似卷的对。这些描述符捕获卷的运动和空间信息。确定每个可能的成对组合的相似度分数。给出最大分数的一对卷被迭代地组合。在组合阶段，卷被分类并以视频对象的多分辨率粗到精细层级来表示。

24. 发明授权

US06859554B2 Method for segmenting multi-resolution video objects 失效
标题翻译：分割多分辨率视频对象的方法
公开(公告)号：US06859554B2
公开(公告)日：2005-02-22
申请号：US09826333
申请日：2001-04-04
申请人： Fatih M. Porikli , Yao Wang
发明人： Fatih M. Porikli , Yao Wang
IPC分类号： G06T5/00 , H04N7/26 , G06K9/34
CPC分类号： G06K9/342 , G06T7/11 , G06T7/187 , G06T7/215 , G06T9/001 , G06T9/20 , G06T2207/10016 , G06T2207/20016 , G06T2207/20156
摘要： A method for segmenting video objects in a video sequence that is composed of frames including pixels first assigns a feature vector to each pixel of the video. Next, selected pixels are identified as marker pixels. Pixels adjacent to each marker pixel are assembled into a corresponding a volume of pixels if the distance between the feature vector of the marker pixel and the feature vector of the adjacent pixels is less than a first predetermined threshold. After all pixels have been assembled into volumes, a first score and descriptors are assigned to each volume. At this point, each volume represents a segmented video object. The volumes are then sorted a high-to-low order according to the first scores, and further processed in the high-to-low order. Second scores, dependent on the descriptors of pairs of volumes are determined. The volumes are iteratively combined if the second score passes a second threshold to generate a video object in a resolution video object tree that completes when the combined volume or video object is the entire video.
摘要翻译：用于分割由包括像素的帧组成的视频序列中的视频对象的方法首先将特征向量分配给视频的每个像素。接下来，将所选择的像素识别为标记像素。如果标记像素的特征向量与相邻像素的特征向量之间的距离小于第一预定阈值，则与每个标记像素相邻的像素被组合成相应的一个像素体积。所有像素已经组装成卷之后，第一个分数和描述符被分配给每个卷。此时，每个卷代表一个分段的视频对象。然后根据第一分数将卷从高到低的顺序排列，并以高到低的顺序进一步处理。确定依赖于成对体积描述符的第二分。如果第二分数通过第二阈值以在组合的音量或视频对象是整个视频时完成的分辨率视频对象树中的视频对象生成，则体积被迭代地组合。

25. 发明授权

US08385632B2 System and method for adapting generic classifiers for object detection in particular scenes using incremental training 有权
标题翻译：用于使用增量训练适应特定场景中的物体检测的通用分类器的系统和方法
公开(公告)号：US08385632B2
公开(公告)日：2013-02-26
申请号：US12791786
申请日：2010-06-01
申请人： Fatih M. Porikli
发明人： Fatih M. Porikli
IPC分类号： G06K9/00
CPC分类号： G06K9/00771 , G06K9/6263 , G06K9/6269 , G06N99/005
摘要： A generic classifier is adapted to detect an object in a particular scene, wherein the particular scene was unknown when the classifier was trained with generic training data. A camera acquires a video of frames of the particular scene. A model of the particular scene model is constructed using the frames in the video. The classifier is applied to the model to select negative examples, and new negative examples are added to the training data while removing another set of existing negative examples from the training data based on an uncertainty measure. Selected positive examples are also added to the training data and the classifier is retrained until a desired accuracy level is reached to obtain a scene specific classifier.
摘要翻译：通用分类器适于检测特定场景中的对象，其中当分类器用通用训练数据训练时，特定场景是未知的。相机获取特定场景的帧的视频。使用视频中的帧构建特定场景模型的模型。将分类器应用于模型以选择负面示例，并且将新的否定示例添加到训练数据中，同时基于不确定性度量从训练数据中移除另一组现有的负面示例。选择的正例也被添加到训练数据中，分类器被重新训练直到达到期望的精度水平以获得场景特定的分类器。

26. 发明授权

US08218869B2 Image segmentation using spatial random walks 有权
标题翻译：使用空间随机游走的图像分割
公开(公告)号：US08218869B2
公开(公告)日：2012-07-10
申请号：US12413593
申请日：2009-03-29
申请人： Fatih M. Porikli , Rui Li
发明人： Fatih M. Porikli , Rui Li
IPC分类号： G06K9/34
CPC分类号： G06T7/143 , G06T7/11 , G06T2207/20076 , G06T2207/30096
摘要： The embodiments of the invention describe a method for segmenting an image. We perform an initial segmentation of the image to produce a previous segmented region and segment iteratively the image using a spatial random walk based on a shape prior of the previous segmented region to produce a next segmented region. We compare the next segmented region with the previous segmented region, and repeat the segmenting and the comparing until the previous and next segmented regions converge. After that, we select the next segmented region as a final segmented region.
摘要翻译：本发明的实施例描述了一种用于分割图像的方法。我们执行图像的初始分割以产生先前的分割区域，并且使用基于先前分割区域之前的形状的空间随机游走来迭代地分割图像以产生下一分割区域。我们将下一个分段区域与先前的分段区域进行比较，并重复分段和比较直到前一个和下一个分段区域收敛。之后，我们选择下一个分段区域作为最终分段区域。

27. 发明授权

US08174374B2 Method and system for coding digital information in lane markings using an optical sensor 有权
标题翻译：使用光学传感器对通道标记中的数字信息进行编码的方法和系统
公开(公告)号：US08174374B2
公开(公告)日：2012-05-08
申请号：US12495419
申请日：2009-06-30
申请人： Raymond Yim , Masashi Saito , Fatih M. Porikli , Jinyun Zhang
发明人： Raymond Yim , Masashi Saito , Fatih M. Porikli , Jinyun Zhang
IPC分类号： G08B21/00
CPC分类号： G06K9/00798 , G01C21/3602 , G05D1/0234 , G05D2201/0213 , G06K19/06009
摘要： A road surface includes lane marking that store digital information. Images of the road surface and lane markings are acquired by a camera. The digital information is decoded from the images, analyzed so that a feedback signal can be generated according to the decoded digital information.
摘要翻译：路面包括存储数字信息的车道标记。路面和车道标记的图像由相机获取。从图像中解码数字信息，进行分析，使得可以根据解码的数字信息生成反馈信号。

28. 发明申请

US20110241927A1 Method for Detecting Small Targets in Radar Images Using Needle Based Hypotheses Verification 失效
标题翻译：使用针假设检测雷达图像中小目标的方法
公开(公告)号：US20110241927A1
公开(公告)日：2011-10-06
申请号：US12753643
申请日：2010-04-02
申请人： Fatih M. Porikli
发明人： Fatih M. Porikli
IPC分类号： G01S13/00
CPC分类号： G01S7/2923 , G01S13/89
摘要： A method detects a target in a sequence of radar images, wherein each image is partitioned into a grid of cells, and wherein each cell has a corresponding position in an image coordinate system associated with a location in a world coordinate system. For each most recent image in a sliding temporal window of images, intensities of each cell are determined, and the subset of the cells having highest intensities is stored as a set of current needles. A set of hypotheses, obtained by using a state transition model and corresponding maximum limits, is determined for the current set of needles and appended to a set of queues. The hypotheses for the previous sets of needles to the corresponding set of queues are updated, and a maximum likelihood in the set of queues are selected to detect the location of targets.
摘要翻译：一种方法检测雷达图像序列中的目标，其中每个图像被划分为单元格格，并且其中每个单元在与世界坐标系中的位置相关联的图像坐标系中具有相应的位置。对于图像的滑动时间窗口中的每个最新图像，确定每个单元的强度，并且具有最高强度的单元的子集被存储为一组当前针。通过使用状态转换模型和对应的最大限制获得的一组假设是针对当前针组确定的并附加到一组队列。更新针对相应队列的先前针组的假设，并且选择队列集合中的最大似然度来检测目标的位置。

29. 发明授权

US07899253B2 Detecting moving objects in video by classifying on riemannian manifolds 有权
标题翻译：通过对黎曼流形进行分类来检测视频中的移动物体
公开(公告)号：US07899253B2
公开(公告)日：2011-03-01
申请号：US11763699
申请日：2007-06-15
申请人： Fatih M. Porikli , Oncel C. Tuzel
发明人： Fatih M. Porikli , Oncel C. Tuzel
IPC分类号： G06K9/46
CPC分类号： G06K9/00369 , G06K9/4642 , G06K9/6257
摘要： A method constructs a classifier from training data and detects moving objects in test data using the trained classifier. High-level features are generated from low-level features extracted from training data. The high level features are positive definite matrices on an analytical manifold. A subset of the high-level features is selected, and an intrinsic mean matrix is determined. Each high-level feature is mapped to a feature vector on a tangent space of the analytical manifold using the intrinsic mean matrix. An untrained classifier is trained with the feature vectors to obtain a trained classifier. Test high-level features are similarly generated from test low-level features. The test high-level features are classified using the trained classifier to detect moving objects in the test data.
摘要翻译：一种方法从训练数据构建分类器，并使用经过训练的分类器检测测试数据中的移动对象。高级功能是从训练数据中提取的低级功能产生的。高级特征是分析歧管上的正定矩阵。选择高级特征的子集，并确定固有均值矩阵。每个高级特征使用内在平均矩阵映射到分析歧管的切线空间上的特征向量。使用特征向量训练未训练的分类器以获得训练有素的分类器。测试高级功能类似地从测试低级功能生成。测试高级功能使用训练有素的分类器进行分类，以检测测试数据中的移动对象。

30. 发明申请

US20100246997A1 Object Tracking With Regressing Particles 失效
标题翻译：回归粒子对象跟踪
公开(公告)号：US20100246997A1
公开(公告)日：2010-09-30
申请号：US12413628
申请日：2009-03-30
申请人： Fatih M. Porikli , Pan Pan
发明人： Fatih M. Porikli , Pan Pan
IPC分类号： G06K9/36
CPC分类号： G06T7/251
摘要： Embodiments of the invention provide a method and a system for tracking an object from a training image to a target image. The training image and the target image are elements of a sequence of images. The object in the training image is represented by an object state. First, a set of particles is acquired, wherein each particle in the set of particles is associated with a weight, such that the particle represents the object state with a probability equal to the weight. Next, a regression function is applied to each particle in the set of particles based on a target image to determine a set of moved particles and the object state is updated according to the set of moved particles, such that the object state represents the object in the target image.
摘要翻译：本发明的实施例提供了一种用于将训练图像跟踪到目标图像的方法和系统。训练图像和目标图像是图像序列的元素。训练图像中的对象由对象状态表示。首先，获取一组颗粒，其中该组颗粒中的每个颗粒与重量相关联，使得颗粒以等于重量的概率表示物体状态。接下来，基于目标图像将回归函数应用于粒子集合中的每个粒子，以确定一组移动的粒子，并且根据移动的粒子的集合更新对象状态，使得对象状态表示对象目标图像。

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