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31. 发明申请

US20070133878A1 Method for constructing covariance matrices from data features 有权
标题翻译：从数据特征构造协方差矩阵的方法
公开(公告)号：US20070133878A1
公开(公告)日：2007-06-14
申请号：US11305427
申请日：2005-12-14
申请人： Fatih Porikli , Oncel Tuzel
发明人： Fatih Porikli , Oncel Tuzel
IPC分类号： G06K9/46
CPC分类号： G06K9/4642 , G06K9/6215
摘要： A method constructs descriptors for a set of data samples and determines a distance score between pairs of subsets selected from the set of data samples. A d-dimensional feature vector is extracted for each sample in each subset of samples. The feature vector includes indices to the corresponding sample and properties of the sample. The feature vectors of each subset of samples are combined into a d×d dimensional covariance matrix. The covariance matrix is a descriptor of the corresponding subset of samples. Then, a distance score is determined between the two subsets of samples using the descriptors to measure a similarity between the descriptors.
摘要翻译：一种方法构建一组数据样本的描述符，并确定从该组数据样本中选择的子集对之间的距离分数。在每个样本子集中为每个样本提取d维特征向量。特征向量包括相应样本的索引和样本的属性。样本的每个子集的特征向量被组合成dxd维协方差矩阵。协方差矩阵是相应样本子集的描述符。然后，使用描述符在两个样本子集之间确定距离分数，以测量描述符之间的相似性。

32. 发明申请

US20050213816A1 Image segmentation by base point selection and wavefront propagation 失效
标题翻译：通过基点选择和波前传播进行图像分割
公开(公告)号：US20050213816A1
公开(公告)日：2005-09-29
申请号：US10810921
申请日：2004-03-26
申请人： Fatih Porikli
发明人： Fatih Porikli
IPC分类号： G06K9/00 , G06T5/00
CPC分类号： G06K9/38 , G06K9/342 , G06T7/12 , G06T7/149 , G06T2207/20156 , G06T2207/20161
摘要： A method partitions an image into segments. The image includes pixel points. A set of base points is selected in the image. A wavefront is initialized and emitted from each base point. Each wavefront is propagated according to a speed function until a termination condition is satisfied to determine a corresponding final wavefront. The image is then segmented according to each final wavefront.
摘要翻译：一种方法将图像分割成段。图像包括像素点。在图像中选择一组基点。从每个基点初始化和发射波阵面。每个波前根据速度函数进行传播，直到满足终止条件以确定相应的最终波阵面。然后根据每个最终的波前将图像分段。

33. 发明申请

US20050102107A1 Method for determining similarities between data sequences using cross-correlation matrices and deformation functions 失效
标题翻译：使用互相关矩阵和变形函数确定数据序列之间的相似性的方法
公开(公告)号：US20050102107A1
公开(公告)日：2005-05-12
申请号：US10703961
申请日：2003-11-07
申请人： Fatih Porikli
发明人： Fatih Porikli
IPC分类号： G01N33/48 , G01N33/50 , G06F19/00 , G06K9/62 , G06K9/64
CPC分类号： G06K9/6212 , G06K9/6215
摘要： A method compares a first data sequence with a second data sequence. A cross-correlation matrix of distances between all possible pairs of values of the first and second data sequences is constructed. A minimum cost path between diagonally opposing corners of the cross-correlation matrix is determined. The minimum cost path is projected onto a transfer function that maps between the first data sequence and the second data sequence. Then, the minimum cost path is projected onto a deformation function that corresponds to a similarity between the first data sequence and the second data sequence.
摘要翻译：一种方法将第一数据序列与第二数据序列进行比较。构建第一和第二数据序列的所有可能的值对之间的距离的互相关矩阵。确定互相关矩阵的斜对角之间的最小成本路径。最小成本路径被投影到在第一数据序列和第二数据序列之间映射的传递函数上。然后，将最小成本路径投影到对应于第一数据序列与第二数据序列之间的相似度的变形函数上。

34. 发明授权

US08935308B2 Method for recovering low-rank matrices and subspaces from data in high-dimensional matrices 有权
标题翻译：从高维矩阵数据中恢复低阶矩阵和子空间的方法
公开(公告)号：US08935308B2
公开(公告)日：2015-01-13
申请号：US13355335
申请日：2012-01-20
申请人： Fatih Porikli , Xianbiao Shu
发明人： Fatih Porikli , Xianbiao Shu
IPC分类号： G06F7/00
CPC分类号： G06K9/6249
摘要： A method recovers an uncorrupted low-rank matrix, noise in corrupted data and a subspace from the data in a form of a high-dimensional matrix. An objective function minimizes the noise to solve for the low-rank matrix and the subspace without estimating the rank of the low-rank matrix. The method uses group sparsity and the subspace is orthogonal. Random subsampling of the data can recover subspace bases and their coefficients from a much smaller matrix to improve performance. Convergence efficiency can also be improved by applying an augmented Lagrange multiplier, and an alternating stepwise coordinate descent. The Lagrange function is solved by an alternating direction method.
摘要翻译：一种方法从高维矩阵的形式中恢复未破坏的低秩矩阵，损坏数据中的噪声和数据中的子空间。目标函数最小化了解低级矩阵和子空间的噪声，而不估计低秩矩阵的秩。该方法使用组稀疏，子空间正交。数据的随机子采样可以从更小的矩阵中恢复子空间基础及其系数，以提高性能。还可以通过应用增强的拉格朗日乘数和交替的逐步坐标下降来提高收敛效率。拉格朗日函数通过交替方向法求解。

35. 发明申请

US20140093160A1 3D Object Tracking in Multiple 2D Sequences 有权
标题翻译：多维2D序列中的3D对象跟踪
公开(公告)号：US20140093160A1
公开(公告)日：2014-04-03
申请号：US13632500
申请日：2012-10-01
申请人： Fatih Porikli , Feng Li
发明人： Fatih Porikli , Feng Li
IPC分类号： G06K9/46
CPC分类号： G06T7/277 , G06T2207/10076 , G06T2207/10124 , G06T2207/10136 , G06T2207/20076 , G06T2207/20081 , G06T2207/30096
摘要： A tumor is tracked in multiple sequences of images acquired concurrently from different viewpoints. Features are extracted in each set of current images using a window. A regression function, subject to motion constraints, is applied to the features to obtain 3D motion parameters, which are applied to the tumor as observed in the images to obtain a 3D location of the object. Then, the shape of the 3D object at the 3D location is projected onto each image to update the location of the window for the next set of images to be processed.
摘要翻译：以不同观点同时获得的多个图像序列跟踪肿瘤。使用窗口在每组当前图像中提取特征。受到运动约束的回归函数被应用于特征以获得3D运动参数，其被应用于在图像中观察到的肿瘤以获得对象的3D位置。然后，将3D位置处的3D对象的形状投影到每个图像上，以更新要处理的下一组图像的窗口的位置。

36. 发明申请

US20130236090A1 Learning Dictionaries with Clustered Atoms 有权
标题翻译：用集群原子学习字典
公开(公告)号：US20130236090A1
公开(公告)日：2013-09-12
申请号：US13418217
申请日：2012-03-12
申请人： Fatih Porikli , Nikhil Rao
发明人： Fatih Porikli , Nikhil Rao
IPC分类号： G06T9/00
CPC分类号： H04N19/97
摘要： A dictionary of atoms for coding data is learned by first selecting samples from a set of samples. Similar atoms in the dictionary are clustered, and if a cluster has multiple atoms, the atoms in that cluster are merged into a single atom. The samples can be acquired online.
摘要翻译：通过首先从一组样本中选择样本来学习用于编码数据的原子词典。字典中的类似原子是聚类的，如果一个簇具有多个原子，那么该簇中的原子将被合并为单个原子。样品可以在线获取。

37. 发明申请

US20130156340A1 Image Filtering by Sparse Reconstruction on Affinity Net 有权
标题翻译：通过稀疏重建在亲和网上的图像过滤
公开(公告)号：US20130156340A1
公开(公告)日：2013-06-20
申请号：US13330795
申请日：2011-12-20
申请人： Fatih Porikli , Rajagopalan Sundaresan
发明人： Fatih Porikli , Rajagopalan Sundaresan
IPC分类号： G06K9/40
CPC分类号： G06T5/002 , G06T2207/10044 , G06T2207/20072 , G06T2207/20076 , G06T2207/20081
摘要： A method reduces multiplicative and additive noise in image pixels by clustering similar patches of the pixels into clusters. The clusters form nodes in an affinity net of nodes and vertices. From each cluster, a dictionary is learned by a sparse combination of corresponding atoms in the dictionaries. The patches are aggregated collaboratively using the dictionaries to construct a denoised image.
摘要翻译：一种方法通过将相似的像素块聚类成群集来减少图像像素中的乘法和加性噪声。簇在节点和顶点的亲和网中形成节点。从每个集群中，通过字典中相应原子的稀疏组合来学习字典。使用字典协作地聚合补丁以构建去噪的图像。

38. 发明申请

US20130156300A1 Multi-Class Classification Method 审中-公开
标题翻译：多类分类方法
公开(公告)号：US20130156300A1
公开(公告)日：2013-06-20
申请号：US13330905
申请日：2011-12-20
申请人： Fatih Porikli , Yuejie Chi
发明人： Fatih Porikli , Yuejie Chi
IPC分类号： G06K9/62
CPC分类号： G06K9/6227 , G06K9/6249
摘要： A test sample is classified by determining a nearest subspace residual from subspaces learned from multiple different classes of training samples, and a collaborative residual from a collaborative representation of a dictionary constructed from all of the test samples. The residuals are used to determine a regularized residual. The subspaces, the dictionary and the regularized residual are inputted into a classifier, wherein the classifier includes a collaborative representation classifier and a nearest subspace classifier, and a label is assigned to the test sample using the classifier, and wherein the regularization parameter balances a trade-off between the collaborative representation classifier the nearest subspace classifier.
摘要翻译：通过确定从多个不同类别的训练样本中学习的子空间的最近子空间残差以及来自所有测试样本构造的字典的协作表示的协作残差来分类测试样本。残差用于确定正则化残差。将子空间，字典和正则化残差输入到分类器中，其中分类器包括协作表示分类器和最近的子空间分类器，并且使用分类器将标签分配给测试样本，并且其中正则化参数平衡交易在合作表示分类器之间的最近的子空间分类器。

39. 发明授权

US08429102B2 Data driven frequency mapping for kernels used in support vector machines 有权
标题翻译：用于支持向量机的内核的数据驱动频率映射
公开(公告)号：US08429102B2
公开(公告)日：2013-04-23
申请号：US13076749
申请日：2011-03-31
申请人： Fatih Porikli , Huseyin Ozkan
发明人： Fatih Porikli , Huseyin Ozkan
IPC分类号： G06F15/18 , G06F17/00 , G06N5/00
CPC分类号： G06N99/005
摘要： Frequency features to be used for binary classification of data using a linear classifier are selected by determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data. A mapping function is constructed for each hypothesis. The mapping functions are applied to the training data to generate frequency features, and a subset of the frequency are selecting iteratively. The linear function is then trained using the subset of frequency features and labels of the training data.
摘要翻译：使用线性分类器对数据的二进制分类使用的频率特征是通过使用d维标记的训练数据确定d维空间中的一组假设来选择的。为每个假设构建映射函数。将映射函数应用于训练数据以产生频率特征，并且频率的子集被迭代地选择。然后使用训练数据的频率特征和标签的子集来训练线性函数。

40. 发明授权

US08401282B2 Method for training multi-class classifiers with active selection and binary feedback 有权
标题翻译：用于训练具有主动选择和二进制反馈的多类分类器的方法
公开(公告)号：US08401282B2
公开(公告)日：2013-03-19
申请号：US12732225
申请日：2010-03-26
申请人： Fatih Porikli , Ajay Joshi
发明人： Fatih Porikli , Ajay Joshi
IPC分类号： G06K9/62 , G06K9/00 , G06K9/54 , G06K9/60
CPC分类号： G06K9/6253 , G06F17/30265 , G06F17/3028 , G06K9/622
摘要： A multi-class classifier is trained by selecting a query image from a set of active images based on a membership probability determined by the classifier, wherein the active images are unlabeled. A sample image is selected from a set of training image based on the membership probability of the query image, wherein the training images are labeled. The query image and the sample images are displayed to a user on an output device. A response from the user is obtained with an input device, wherein the response is a yes-match or a no-match. The query image with the label of the sample image is added to the training set if the yes-match is obtained, and otherwise repeating the selecting, displaying, and obtaining steps until a predetermined number of no-match is reached to obtain the multi-class classifier.
摘要翻译：通过基于由分类器确定的成员概率从一组活动图像中选择查询图像来训练多类分类器，其中活动图像是未标记的。基于查询图像的成员概率从一组训练图像中选择样本图像，其中训练图像被标记。查询图像和样本图像在输出设备上显示给用户。使用输入设备获得来自用户的响应，其中响应是是匹配或不匹配。如果获得了是匹配，则具有样本图像的标签的查询图像被添加到训练集合，否则重复选择，显示和获得步骤，直到达到预定数量的不匹配，类分类器。

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