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1. 发明授权

US07982738B2 Interactive montages of sprites for indexing and summarizing video 有权
标题翻译：用于索引和总结视频的精灵互动蒙太奇
公开(公告)号：US07982738B2
公开(公告)日：2011-07-19
申请号：US11004760
申请日：2004-12-01
申请人： Nebojsa Jojic , Chris Pal
发明人： Nebojsa Jojic , Chris Pal
IPC分类号： G06T13/00
CPC分类号： G06F17/30811 , G06F17/30843 , G06F17/30852 , G11B27/034 , G11B27/28 , G11B27/34 , H04N21/44008 , H04N21/44029 , H04N21/45452 , H04N21/8193 , H04N21/8549 , Y10S345/95
摘要： A “Video Browser” provides interactive browsing of unique events occurring within an overall video recording. In particular, the Video Browser processes the video to generate a set of video sprites representing unique events occurring within the overall period of the video. These unique events include, for example, motion events, security events, or other predefined event types, occurring within all or part of the total period covered by the video. Once the video has been processed to identify the sprites, the sprites are then arranged over a background image extracted from the video to create an interactive static video montage. The interactive video montage illustrates all events occurring within the video in a single static frame. User selection of sprites within the montage causes either playback of a portion of the video in which the selected sprites were identified, or concurrent playback of the selected sprites within a dynamic video montage.
摘要翻译： “视频浏览器”提供了在整个视频录制中发生的独特事件的交互式浏览。特别地，视频浏览器处理视频以生成表示在视频的整个周期内发生的唯一事件的一组视频精灵。这些独特的事件包括例如运动事件，安全事件或其他预定事件类型，发生在视频所涵盖的整个周期的全部或部分内。一旦视频被处理以识别精灵，则将精灵布置在从视频提取的背景图像上，以创建交互式静态视频蒙太奇。交互式视频蒙太奇在单个静态帧中说明视频内发生的所有事件。蒙太奇内的精灵的用户选择导致播放所选择的精灵被识别的视频的一部分，或动态视频蒙太奇中所选精灵的并发回放。

2. 发明授权

US07978906B2 Capturing long-range correlations in patch models 有权
标题翻译：在补丁模型中捕获长距离相关性
公开(公告)号：US07978906B2
公开(公告)日：2011-07-12
申请号：US11763136
申请日：2007-06-14
申请人： Nebojsa Jojic , Vincent Cheung
发明人： Nebojsa Jojic , Vincent Cheung
IPC分类号： G06K9/62
CPC分类号： G06K9/469 , G06K9/6255
摘要： Systems and methodologies for modeling data in accordance with one or more embodiments disclosed herein are operable to receive input data, create data patches from the input data, obtain long-range correlations between the data patches, and model the input data as a patch model based at least in part on the data patches and the long-range correlations. Various learning algorithms are additionally provided for refining the patch model created in accordance with one or more embodiments disclosed herein. Further, systems and methodologies for synthesizing a patch model created in accordance with various aspects of the present invention with a set of test data to perform a transformation represented by the patch model on the test data are provided.
摘要翻译：根据本文公开的一个或多个实施例的用于建模数据的系统和方法可操作用于接收输入数据，从输入数据创建数据补丁，获得数据补丁之间的长距离相关性，并将输入数据建模为基于补丁模型至少部分地基于数据补丁和长距离相关性。另外提供了各种学习算法，用于细化根据本文公开的一个或多个实施例创建的贴片模型。此外，提供了用于根据本发明的各个方面创建的用于合成测试数据的一组测试数据来进行由测试数据上的补丁模型表示的变换的补丁模型的系统和方法。

3. 发明授权

US07702489B2 Method and apparatus for fast machine learning using probability maps and fourier transforms 有权
标题翻译：使用概率图和傅立叶变换进行快速机器学习的方法和装置
公开(公告)号：US07702489B2
公开(公告)日：2010-04-20
申请号：US10286142
申请日：2002-11-01
申请人： Nebojsa Jojic , Brendan J. Frey
发明人： Nebojsa Jojic , Brendan J. Frey
IPC分类号： G06F7/60 , G06F17/10
CPC分类号： G06K9/6255
摘要： The present invention provides a method of constructing recognition models. Under the method, a set of probabilities is estimated for values of a hidden variable. A Fourier transform is determined for the set of probabilities and is used to determine a Fourier transform of an estimated prototype pattern. The inverse Fourier transform is then determined for the Fourier transform of the estimated prototype pattern to form an estimated prototype pattern.
摘要翻译：本发明提供一种构建识别模型的方法。在该方法下，对一个隐含变量的值估计一组概率。对于概率集合确定傅里叶变换，并且用于确定估计原型图案的傅里叶变换。然后，对于所估计的原型图案的傅立叶变换来确定逆傅立叶变换以形成估计的原型图案。

4. 发明授权

US07657102B2 System and method for fast on-line learning of transformed hidden Markov models 有权
标题翻译：用于快速在线学习变换隐马尔科夫模型的系统和方法
公开(公告)号：US07657102B2
公开(公告)日：2010-02-02
申请号：US10649382
申请日：2003-08-27
申请人： Nebojsa Jojic , Nemanja Petrovic
发明人： Nebojsa Jojic , Nemanja Petrovic
IPC分类号： G06K9/62 , G10L15/06
CPC分类号： G11B27/28 , G06K9/00711 , G06K9/6297
摘要： A fast variational on-line learning technique for training a transformed hidden Markov model. A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, once the model has been initialized, an expectation-maximization (“EM”) algorithm is used to learn the one or more object class models, so that the video sequence has high marginal probability under the model. In the expectation step (the “E-Step”), the model parameters are assumed to be correct, and for an input image, probabilistic inference is used to fill in the values of the unobserved or hidden variables, e.g., the object class and appearance. In one embodiment of the invention, a Viterbi algorithm and a latent image is employed for this purpose. In the maximization step (the “M-Step”), the model parameters are adjusted using the values of the unobserved variables calculated in the previous E-step.
摘要翻译：一种快速变化的在线学习技术，用于训练变换后的隐马尔可夫模型。提供了简化的一般模型和相关联的估计算法用于对诸如视频序列的视觉数据进行建模。具体来说，一旦模型被初始化，使用期望最大化（“EM”）算法来学习一个或多个对象类模型，使得视频序列在该模型下具有高边际概率。在期望步骤（“E步骤”）中，假设模型参数是正确的，对于输入图像，使用概率推断来填充未观察或隐藏变量的值，例如对象类和出现。在本发明的一个实施例中，为此目的采用维特比算法和潜像。在最大化步骤（“M步骤”）中，使用在先前E步骤中计算的未观察到的变量的值来调整模型参数。

5. 发明授权

US07522749B2 Simultaneous optical flow estimation and image segmentation 有权
标题翻译：同时光流估计和图像分割
公开(公告)号：US07522749B2
公开(公告)日：2009-04-21
申请号：US11193273
申请日：2005-07-30
申请人： Charles Zitnick, III , Sing Bing Kang , Nebojsa Jojic
发明人： Charles Zitnick, III , Sing Bing Kang , Nebojsa Jojic
IPC分类号： G06K9/00
CPC分类号： G06K9/34 , G06K9/38 , G06K9/4652 , G06K9/6219
摘要： A technique for estimating the optical flow between images of a scene and a segmentation of the images is presented. This involves first establishing an initial segmentation of the images and an initial optical flow estimate for each segment of each images and its neighboring image or images. A refined optical flow estimate is computed for each segment of each image from the initial segmentation of that image and the initial optical flow of the segments of that image. Next, the segmentation of each image is refined from the last-computed optical flow estimates for each segment of the image. This process can continue in an iterative manner by further refining the optical flow estimates for the images using their respective last-computed segmentation, followed by further refining the segmentation of each image using their respective last-computed optical flow estimates, until a prescribed number of iterations have been completed.
摘要翻译：提出了一种用于估计场景图像和图像分割之间的光流的技术。这包括首先建立图像的初始分割和每个图像及其相邻图像或图像的每个片段的初始光学流量估计。从该图像的初始分割和该图像的片段的初始光流中计算每个图像的每个片段的精细光学流量估计。接下来，从图像的每个片段的最后计算的光学流量估计来细化每个图像的分割。该过程可以通过使用其各自的最后计算的分割进一步细化图像的光流估计，然后使用其各自的最后计算的光流估计进一步细化每个图像的分割，直到规定数量的迭代已经完成。

6. 发明申请

US20080310755A1 CAPTURING LONG-RANGE CORRELATIONS IN PATCH MODELS 有权
标题翻译：捕捉模式中的长距离关系
公开(公告)号：US20080310755A1
公开(公告)日：2008-12-18
申请号：US11763136
申请日：2007-06-14
申请人： Nebojsa Jojic , Vincent Cheung
发明人： Nebojsa Jojic , Vincent Cheung
IPC分类号： G06K9/64
CPC分类号： G06K9/469 , G06K9/6255
摘要： Systems and methodologies for modeling data in accordance with one or more embodiments disclosed herein are operable to receive input data, create data patches from the input data, obtain long-range correlations between the data patches, and model the input data as a patch model based at least in part on the data patches and the long-range correlations. Various learning algorithms are additionally provided for refining the patch model created in accordance with one or more embodiments disclosed herein. Further, systems and methodologies for synthesizing a patch model created in accordance with various aspects of the present invention with a set of test data to perform a transformation represented by the patch model on the test data are provided.
摘要翻译：根据本文公开的一个或多个实施例的用于建模数据的系统和方法可操作以接收输入数据，从输入数据创建数据补丁，获得数据补丁之间的长距离相关性，并将输入数据建模为基于补丁模型至少部分地基于数据补丁和长距离相关性。另外提供了各种学习算法，用于细化根据本文公开的一个或多个实施例创建的贴片模型。此外，提供了用于根据本发明的各个方面创建的用于合成测试数据的一组测试数据来进行由测试数据上的补丁模型表示的变换的补丁模型的系统和方法。

7. 发明申请

US20080172215A1 T-CELL EPIOTOPE PREDICTION 有权
标题翻译： T细胞EPIOTOPE预测
公开(公告)号：US20080172215A1
公开(公告)日：2008-07-17
申请号：US11963081
申请日：2007-12-21
申请人： David E. Heckerman , Carl M. Kadie , Jennifer Listgarten , Noah Aaron Zaitlen , Nebojsa Jojic
发明人： David E. Heckerman , Carl M. Kadie , Jennifer Listgarten , Noah Aaron Zaitlen , Nebojsa Jojic
IPC分类号： G06G7/60
CPC分类号： G06F19/24 , G06F19/16 , G06F19/18
摘要： Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.
摘要翻译：本文描述了表位预测模型。作为示例，用于预测与表位相关的表位信息的系统可以包括分类模型（例如逻辑回归模型）。经训练的分类模型可以说明性地操作地对所接收的蛋白质数据执行一个矿物物流功能，并且包含一个或多个隐藏的二进制变量和移位变量，其在被处理时表示一个或多个所需表位的识别（例如，预测）。分类模型可以被配置为通过处理包括表位，MHC，MHC超类型和布尔组合的各种特征的数据来预测表位信息。

8. 发明申请

US20080021686A1 CLUSTER MODELING, AND LEARNING CLUSTER SPECIFIC PARAMETERS OF AN ADAPTIVE DOUBLE THREADING MODEL 有权
标题翻译：自适应双线性模型的聚类建模和学习聚类特定参数
公开(公告)号：US20080021686A1
公开(公告)日：2008-01-24
申请号：US11770684
申请日：2007-06-28
申请人： Nebojsa Jojic , David Heckerman , Manuel Jesus Reyes Gomez
发明人： Nebojsa Jojic , David Heckerman , Manuel Jesus Reyes Gomez
IPC分类号： G06G7/48
CPC分类号： G06F19/24 , G06F19/16 , G06G7/48
摘要： Cluster models are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a probability distribution and a hidden variable that represents a cluster of protein sequences, and/or a set of hidden variables representing learned supertypes. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.
摘要翻译：这里描述了群集模型。作为示例，用于预测与蛋白质和配体的结合相关的结合信息的系统可以包括训练的结合模型和预测组分。经训练的绑定模型可以包括概率分布和表示蛋白质序列簇的隐藏变量，和/或表示学习超类型的一组隐藏变量。预测组件可以被配置为通过使用关于蛋白质序列，配体序列和训练的结合模型的信息来预测结合信息。

9. 发明申请

US20080016369A1 Click Passwords 失效
标题翻译：点击密码
公开(公告)号：US20080016369A1
公开(公告)日：2008-01-17
申请号：US11775159
申请日：2007-07-09
申请人： Darko Kirovski , Nebojsa Jojic , Paul Roberts
发明人： Darko Kirovski , Nebojsa Jojic , Paul Roberts
IPC分类号： H04K1/00
CPC分类号： G06F21/36
摘要： Methods, systems, devices and/or storage media for passwords. An exemplary method tiles an image, associates an index with each tile and optionally determines offsets for select tiles. Further, the tiling optionally relies on probability and/or entropy. An exemplary password system includes an image; a grid associated with the image, the grid composed of polygons; an index associated with each polygon; and an offset associated with each polygon wherein password identification relies on one or more indices and one or more offsets.
摘要翻译：用于密码的方法，系统，设备和/或存储介质。贴图图像的示例性方法，将索引与每个瓦片相关联，并且可选地确定选择瓦片的偏移量。此外，平铺可选地依赖于概率和/或熵。示例性密码系统包括图像; 与图像相关联的网格，网格由多边形组成; 与每个多边形相关联的索引; 以及与每个多边形相关联的偏移量，其中密码识别依赖于一个或多个索引和一个或多个偏移量。

10. 发明申请

US20070192033A1 Molecular interaction predictors 审中-公开
标题翻译：分子相互作用预测因子
公开(公告)号：US20070192033A1
公开(公告)日：2007-08-16
申请号：US11356196
申请日：2006-02-16
申请人： Nebojsa Jojic , Manuel Gomez
发明人： Nebojsa Jojic , Manuel Gomez
IPC分类号： G06F19/00
CPC分类号： G16B40/00 , G16B15/00
摘要： Adaptive threading models for predicting an interaction between two or more molecules such as proteins are provided. The adaptive threading models have one or more learnable parameters that can be learned from all or some of the available data. The available data can include data relating to known interactions between the two or more molecules, the composition of the molecules and the geometry of the molecular complex.
摘要翻译：提供了用于预测两个或多个分子（例如蛋白质）之间的相互作用的自适应穿线模型。自适应线程模型具有一个或多个可学习的参数，可从全部或部分可用数据中学习。可用的数据可以包括与两个或更多个分子之间的已知相互作用，分子的组成和分子复合物的几何形状有关的数据。

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