Progressive cut: interactive object segmentation
    1.
    发明申请
    Progressive cut: interactive object segmentation 审中-公开
    渐进切割:交互式对象分割

    公开(公告)号:US20080136820A1

    公开(公告)日:2008-06-12

    申请号:US11897224

    申请日:2007-08-29

    IPC分类号: G06T11/20 G06F3/041

    摘要: Progressive cut interactive object segmentation is described. In one implementation, a system analyzes strokes input by the user during iterative image segmentation in order to model the user's intention for refining segmentation. In the user intention model, the color of each stroke indicates the user's expectation of pixel label change to foreground or background, the location of the stroke indicates the user's region of interest, and the position of the stroke relative to a previous segmentation boundary indicates a segmentation error that the user intends to refine. Overexpansion of pixel label change is controlled by penalizing change outside the user's region of interest while overshrinkage is controlled by modeling the image as an eroded graph. In each iteration, energy consisting of a color term, a contrast term, and a user intention term is minimized to obtain a segmentation map.

    摘要翻译: 描述了渐进切割交互式对象分割。 在一个实现中,系统分析用户在迭代图像分割期间输入的笔画,以便模拟用户的细化细分意图。 在用户意图模型中,每个笔画的颜色表示用户对于前景或背景的像素标签变化的期望,笔画的位置指示用户感兴趣的区域,并且笔画相对于先前分割边界的位置指示 用户打算细化的分段错误。 像素标签变化的过度扩展是通过对用户感兴趣区域之外的变化进行惩罚来控制的,而通过将图像建模为受侵蚀图来控制超损耗。 在每次迭代中,将由颜色项,对比度项和用户意图项组成的能量最小化以获得分割图。

    Salience preserving image fusion
    2.
    发明授权
    Salience preserving image fusion 失效
    保守图像融合

    公开(公告)号:US07636098B2

    公开(公告)日:2009-12-22

    申请号:US11536513

    申请日:2006-09-28

    IPC分类号: G09G5/00

    CPC分类号: G06T5/50 G06T2207/20221

    摘要: Salience-preserving image fusion is described. In one aspect, multi-channel images are fused into a single image. The fusing operations are based on importance-weighted gradients. The importance weighted gradients are measured using respective salience maps for each channel in the multi-channel images.

    摘要翻译: 描述了保守的图像融合。 在一个方面,多通道图像被融合成单个图像。 定影操作基于重要度加权梯度。 重要度加权梯度是使用多通道图像中每个通道的各个显着性图来测量的。

    Salience Preserving Image Fusion
    3.
    发明申请
    Salience Preserving Image Fusion 失效
    显着保存图像融合

    公开(公告)号:US20080080787A1

    公开(公告)日:2008-04-03

    申请号:US11536513

    申请日:2006-09-28

    IPC分类号: G06K9/36

    CPC分类号: G06T5/50 G06T2207/20221

    摘要: Salience-preserving image fusion is described. In one aspect, multi-channel images are fused into a single image. The fusing operations are based on importance-weighted gradients. The importance weighted gradients are measured using respective salience maps for each channel in the multi-channel images.

    摘要翻译: 描述了保守的图像融合。 在一个方面,多通道图像被融合成单个图像。 定影操作基于重要度加权梯度。 重要度加权梯度是使用多通道图像中每个通道的各个显着性图来测量的。

    Directed Graph Embedding
    4.
    发明申请
    Directed Graph Embedding 审中-公开
    定向图嵌入

    公开(公告)号:US20100121792A1

    公开(公告)日:2010-05-13

    申请号:US12521985

    申请日:2008-01-07

    IPC分类号: G06N5/02 G06F15/18 G06F7/548

    CPC分类号: G06F16/9024

    摘要: Directed graph embedding is described. In one implementation, a system explores the link structure of a directed graph and embeds the vertices of the directed graph into a vector space while preserving affinities that are present among vertices of the directed graph. Such an embedded vector space facilitates general data analysis of the information in the directed graph. Optimal embedding can be achieved by measuring local affinities among vertices via transition probabilities between the vertices, based on a stationary distribution of Markov random walks through the directed graph. For classifying linked web pages represented by a directed graph, the system can train a support vector machine (SVM) classifier, which can operate in a user-selectable number of dimensions.

    摘要翻译: 描述了定向图嵌入。 在一个实现中,系统探索有向图的链接结构,并将有向图的顶点嵌入到向量空间中,同时保留存在于有向图的顶点之间的亲和度。 这样的嵌入向量空间有助于对有向图中的信息的一般数据分析。 基于通过有向图的马尔科夫随机游走的平稳分布,可以通过顶点之间的转移概率来测量顶点之间的局部亲和度来实现最佳嵌入。 为了对由有向图表示的链接的网页进行分类,系统可以训练支持向量机(SVM)分类器,其可以以用户可选择的维数操作。

    Bayesian Competitive Model Integrated With a Generative Classifier for Unspecific Person Verification
    5.
    发明申请
    Bayesian Competitive Model Integrated With a Generative Classifier for Unspecific Person Verification 有权
    贝叶斯竞争模型与用于非特定人员验证的生成分类器集成

    公开(公告)号:US20070189611A1

    公开(公告)日:2007-08-16

    申请号:US11276112

    申请日:2006-02-14

    IPC分类号: G06K9/62

    CPC分类号: G06K9/6278 G06K9/6292

    摘要: A Bayesian competitive model integrated with a generative classifier for unspecific person verification is described. In one aspect, a competitive measure for verification of an unspecific person is calculated using a discriminative classifier. The discriminative classifier is based on a Bayesian competitive model that is adaptable to unknown new classes. The Bayesian competitive model is integrated with a generative verification in view of a set of confidence criteria to make a decision regarding verification of the unspecific person.

    摘要翻译: 描述了与非特定人员验证的生成分类器集成的贝叶斯竞争模型。 一方面,使用歧视性分类器来计算非特异性人的验证的竞争措施。 歧视性分类器基于贝叶斯竞争模型,适用于未知的新类。 贝叶斯竞争模式与生成验证相结合,鉴于一套信心标准,对非特定人员的验证作出决定。

    Occlusion Handling in Stero Imaging
    6.
    发明申请
    Occlusion Handling in Stero Imaging 有权
    Stero成像中的闭塞处理

    公开(公告)号:US20070086646A1

    公开(公告)日:2007-04-19

    申请号:US11462342

    申请日:2006-08-03

    IPC分类号: G06K9/00

    摘要: The handling of occlusions in stereo imaging is disclosed. In one implementation, an association between a discontinuity in one stereo image and an occlusion in a second stereo image is utilized. In such an implementation, the first and second stereo images are segmented. A mapping of a discontinuity within the second stereo image is used to form at least part of a boundary of an occlusion in the first stereo image. The mapped discontinuity is found at a boundary between two segments in the second stereo image, and once mapped, divides a segment in the first stereo image into two patches. An energy calculation is made in an iterative manner, alternating with changes to a solution with the disparities and occlusions of the patches. Upon minimization, disparities and occlusions at the patch and pixel level are available.

    摘要翻译: 公开了立体成像中遮挡物的处理。 在一个实现中,利用一个立体图像中的不连续性和第二立体图像中的遮挡之间的关联。 在这种实现中,分割第一和第二立体图像。 使用第二立体图像内的不连续性的映射来形成第一立体图像中的遮挡的边界的至少一部分。 映射的不连续性位于第二立体图像中的两个段之间的边界处,一旦映射,则将第一立体图像中的段划分成两个补丁。 以迭代的方式进行能量计算,并与补丁的差异和闭塞的解决方案的变化交替进行。 在最小化时,可以使用补丁和像素级别的差异和遮挡。

    Image-based face search
    7.
    发明授权
    Image-based face search 有权
    基于图像的脸部搜索

    公开(公告)号:US07684651B2

    公开(公告)日:2010-03-23

    申请号:US11466750

    申请日:2006-08-23

    IPC分类号: G06K9/60

    CPC分类号: G06F17/30247

    摘要: A search includes comparing a query image provided by a user to a plurality of stored images of faces stored in a stored image database, and determining a similarity of the query image to the plurality of stored images. One or more resultant images of faces, selected from among the stored images, are displayed to the user based on the determined similarity of the stored images to the query image provided by the user. The resultant images are displayed based at least in part on one or more facial features.

    摘要翻译: 搜索包括将由用户提供的查询图像与存储在存储的图像数据库中的多个存储的面部图像进行比较,以及确定查询图像与多个存储图像的相似性。 基于所确定的存储的图像与由用户提供的查询图像的相似度,向用户显示从所存储的图像中选择的一个或多个所得到的面部图像。 所得图像至少部分地基于一个或多个面部特征显示。

    Learning object cutout from a single example
    8.
    发明授权
    Learning object cutout from a single example 有权
    从一个例子学习对象剪切

    公开(公告)号:US08644600B2

    公开(公告)日:2014-02-04

    申请号:US11810595

    申请日:2007-06-05

    IPC分类号: G06K9/00

    摘要: Systems and methods are described for learning visual object cutout from a single example. In one implementation, an exemplary system determines the color context near each block in a model image to create an appearance model. The system also learns color sequences that occur across visual edges in the model image to create an edge profile model. The exemplary system then infers segmentation boundaries in unknown images based on the appearance model and edge profile model. In one implementation, the exemplary system minimizes the energy in a graph-cut model where the appearance model is used for data energy and the edge profile is used to modulate edges. The system is not limited to images with nearly identical foregrounds or backgrounds. Some variations in scale, rotation, and viewpoint are allowed.

    摘要翻译: 描述了从单个示例中学习视觉对象切割的系统和方法。 在一个实现中,示例性系统确定模型图像中每个块附近的颜色上下文以创建外观模型。 该系统还学习在模型图像中跨视觉边缘发生的颜色序列,以创建边缘轮廓模型。 然后,示例性系统基于外观模型和边缘轮廓模型来推断未知图像中的分割边界。 在一个实现中,示例性系统最小化图形切割模型中的能量,其中外观模型用于数据能量,并且边缘轮廓用于调制边缘。 该系统不限于具有几乎相同的前景或背景的图像。 允许在比例尺,旋转角度和视角上有一些变化。

    Active Segmentation for Groups of Images
    9.
    发明申请
    Active Segmentation for Groups of Images 有权
    主动分割图像组

    公开(公告)号:US20120093411A1

    公开(公告)日:2012-04-19

    申请号:US13276881

    申请日:2011-10-19

    IPC分类号: G06K9/34

    摘要: Systems and methods of segmenting images are disclosed. The similarity of images in a set of images is compared. A group of images is selected from the set of images. The images in the group of images are selected based on compared similarities among the images. An informative image is selected from the group of images. User-defined semantic information of the informative image is received. The group of images is modeled as a graph. Each image in the group of images denotes a node in the graph. Edges of the graph denote a foreground or background relationship between images. One or more images in the group of images may be automatically segmented by propagating semantic information of the informative image to images in the group having a graph node corresponding to the informative image. Segmentation results can be refined according to user provided image semantics.

    摘要翻译: 公开了分割图像的系统和方法。 比较一组图像中图像的相似度。 从该组图像中选择一组图像。 基于图像中的相似度来选择图像组中的图像。 从图像组中选择信息图像。 收到信息图像的用户定义语义信息。 图像组被建模为图形。 图像组中的每个图像表示图中的一个节点。 图形的边缘表示图像之间的前景或背景关系。 图像组中的一个或多个图像可以通过将信息图像的语义信息传播到具有对应于信息图像的图形节点的组中的图像来自动分割。 分割结果可以根据用户提供的图像语义进行细化。

    Modeling micro-structure for feature extraction
    10.
    发明授权
    Modeling micro-structure for feature extraction 有权
    特征提取的微观结构建模

    公开(公告)号:US07991230B2

    公开(公告)日:2011-08-02

    申请号:US11466332

    申请日:2006-08-22

    IPC分类号: G06K9/00

    摘要: Exemplary systems and methods use micro-structure modeling of an image for extracting image features. The micro-structure in an image is modeled as a Markov Random Field, and the model parameters are learned from training images. Micro-patterns adaptively designed from the modeled micro-structure capture spatial contexts of the image. In one implementation, a series of micro-patterns based on the modeled micro-structure can be automatically designed for each block of the image, providing improved feature extraction and recognition because of adaptability to various images, various pixel attributes, and various sites within an image.

    摘要翻译: 示例性系统和方法使用用于提取图像特征的图像的微结构建模。 图像中的微结构被建模为马尔科夫随机场,并且从训练图像中学习模型参数。 微模式通过模拟的微观结构自适应地捕获图像的空间上下文。 在一个实现中,可以为图像的每个块自动设计一系列基于建模的微结构的微图案,从而提供改进的特征提取和识别,因为对各种图像的适应性,各种像素属性和 图片。