Laplacian principal components analysis (LPCA)
    41.
    发明授权
    Laplacian principal components analysis (LPCA) 有权
    拉普拉斯主成分分析(LPCA)

    公开(公告)号:US08064697B2

    公开(公告)日:2011-11-22

    申请号:US11871764

    申请日:2007-10-12

    IPC分类号: G06K9/00 G06T7/00

    CPC分类号: G06K9/6248

    摘要: Systems and methods perform Laplacian Principal Components Analysis (LPCA). In one implementation, an exemplary system receives multidimensional data and reduces dimensionality of the data by locally optimizing a scatter of each local sample of the data. The optimization includes summing weighted distances between low dimensional representations of the data and a mean. The weights of the distances can be determined by a coding length of each local data sample. The system can globally align the locally optimized weighted scatters of the local samples and provide a global projection matrix. The LPCA improves performance of such applications as face recognition and manifold learning.

    摘要翻译: 系统和方法执行拉普拉斯主成分分析(LPCA)。 在一个实现中,示例性系统通过局部优化数据的每个局部采样的散射来接收多维数据并且降低数据的维度。 优化包括对数据的低维表示和平均值之间的加权距离求和。 距离的权重可以通过每个本地数据样本的编码长度来确定。 该系统可以对局部采样的局部优化加权散射进行全局对齐,并提供全局投影矩阵。 LPCA可以改善诸如面部识别和歧管学习等应用的性能。

    Active segmentation for groups of images
    42.
    发明授权
    Active segmentation for groups of images 有权
    主动分割图像组

    公开(公告)号:US08045800B2

    公开(公告)日:2011-10-25

    申请号:US12025703

    申请日:2008-02-04

    IPC分类号: G06K9/34

    摘要: Systems and methods of segmenting images are disclosed herein. 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 as a graph 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 relationship between images or a background relationship between images. One or more images in the group of images are automatically segmented by propagating the semantic information of the informative image to images in the group of images having a corresponding graph node that is related to a graph node corresponding to the informative image. Segmentation results can be refined according to user provided image semantics.

    摘要翻译: 本文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。 作为图形的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。 图的边缘表示图像之间的前景关系或图像之间的背景关系。 通过将信息图像的语义信息传播到具有与对应于信息图像的图形节点相关联的对应图形节点的图像组中的图像来自动分割图像组中的一个或多个图像。 分割结果可以根据用户提供的图像语义进行细化。

    Salient object detection
    43.
    发明授权
    Salient object detection 有权
    突出物体检测

    公开(公告)号:US07940985B2

    公开(公告)日:2011-05-10

    申请号:US11759192

    申请日:2007-06-06

    IPC分类号: G06K9/34

    CPC分类号: G06K9/3233 G06K9/4638

    摘要: Methods for detecting a salient object in an input image are described. For this, the salient object in an image may be defined using a set of local, regional, and global features including multi-scale contrast, center-surround histogram, and color spatial distribution. These features are optimally combined through conditional random field learning. The learned conditional random field is then used to locate the salient object in the image. The methods can also use image segmentation, where the salient object is separated from the image background.

    摘要翻译: 描述用于检测输入图像中的突出物体的方法。 为此,可以使用一组局部,区域和全局特征来定义图像中的显着对象,包括多尺度对比度,中心环绕直方图和颜色空间分布。 这些特征通过条件随机场学习进行最佳组合。 然后使用学习的条件随机字段来定位图像中的显着对象。 该方法还可以使用图像分割,其中显着对象与图像背景分离。

    Image-Based Face Search
    44.
    发明申请
    Image-Based Face Search 有权
    基于图像的脸部搜索

    公开(公告)号:US20100135584A1

    公开(公告)日:2010-06-03

    申请号:US12699274

    申请日:2010-02-03

    IPC分类号: G06K9/68 G06F17/30

    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。

    Determining intensity similarity in low-light conditions using the Poisson-quantization noise model
    45.
    发明授权
    Determining intensity similarity in low-light conditions using the Poisson-quantization noise model 失效
    使用泊松量化噪声模型确定低光条件下的强度相似度

    公开(公告)号:US07711047B2

    公开(公告)日:2010-05-04

    申请号:US11275265

    申请日:2005-12-21

    IPC分类号: H04N7/18

    CPC分类号: G06K9/38 G06K9/40

    摘要: A Poisson-quantization noise model for modeling noise in low-light conditions is described. In one aspect, image information is received. A Poisson-quantization noise model is then generated from a Poisson noise model and a quantization noise model. Poisson-quantization noise is then estimated in the image information using the Poisson-quantization noise model.

    摘要翻译: 描述了用于在低光条件下建模噪声的泊松量化噪声模型。 一方面,接收图像信息。 然后从泊松噪声模型和量化噪声模型生成泊松量化噪声模型。 然后使用泊松量化噪声模型在图像信息中估计泊松量化噪声。

    TENSOR LINEAR LAPLACIAN DISCRIMINATION FOR FEATURE EXTRACTION
    47.
    发明申请
    TENSOR LINEAR LAPLACIAN DISCRIMINATION FOR FEATURE EXTRACTION 有权
    用于特征提取的传感器线性拉普拉斯分析

    公开(公告)号:US20100076723A1

    公开(公告)日:2010-03-25

    申请号:US12235927

    申请日:2008-09-23

    CPC分类号: G06F17/30598 G06K9/6234

    摘要: Tensor linear Laplacian discrimination for feature extraction is disclosed. One embodiment comprises generating a contextual distance based sample weight and class weight, calculating a within-class scatter using the at least one sample weight and a between-class scatter for multiple classes of data samples in a sample set using the class weight, performing a mode-k matrix unfolding on scatters and generating at least one orthogonal projection matrix.

    摘要翻译: 公开了用于特征提取的张量线性拉普拉斯判别。 一个实施例包括生成基于上下文距离的样本权重和类权重,使用所述至少一个样本权重来计算类内散度,以及使用类权重在样本集合中的多类数据样本之间进行类间散射,执行 mode-k矩阵在散射上展开并生成至少一个正交投影矩阵。

    Salience preserving image fusion
    48.
    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。

    Producing animated scenes from still images
    49.
    发明授权
    Producing animated scenes from still images 失效
    从静态图像生成动画场景

    公开(公告)号:US07609271B2

    公开(公告)日:2009-10-27

    申请号:US11428195

    申请日:2006-06-30

    IPC分类号: G06T13/00

    摘要: A strategy is described for producing an animated scene from multiple high resolution still images. The strategy involves: creating a graph based on an analysis of similarity among the plural still images; performing partial temporal order recovery to define a partial ordering among the plural still images; and extracting an output sequence from the plural still images using second-order Markov Chain analysis, using the partial ordering as a reference. The strategy can perform the above-described analysis with respect to multiple independent animated regions (IARs) within the still images. Further, the strategy can decompose any IAR with a significant amount of motion into multiple semi-independent animated regions (SIARs). The SIARs are defined to be weakly interdependent.

    摘要翻译: 描述了从多个高分辨率静止图像生成动画场景的策略。 该策略涉及:基于对多个静止图像之间的相似性的分析来创建图; 执行部分时间顺序恢复以限定所述多个静止图像中的部分排序; 以及使用部分排序作为参考,使用二阶马尔可夫链分析从多个静止图像中提取输出序列。 该策略可以针对静止图像内的多个独立动画区域(IAR)执行上述分析。 此外,该策略可以将具有大量运动的任何IAR分解成多个半独立动画区域(SIAR)。 SIAR被定义为弱相互依赖。

    Hybrid Graph Model For Unsupervised Object Segmentation
    50.
    发明申请
    Hybrid Graph Model For Unsupervised Object Segmentation 有权
    用于无监督对象分割的混合图模型

    公开(公告)号:US20090080774A1

    公开(公告)日:2009-03-26

    申请号:US11860428

    申请日:2007-09-24

    IPC分类号: G06K9/34

    摘要: This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background.

    摘要翻译: 本公开描述了用于无人监督的对象分割的集成框架。 通过以集成的方式使用算法将自上而下的约束和自下而上的对象形状约束集成在一起,进行类无监督对象分割。 该算法描述了对象部分和超像素之间的关系。 该过程通过对象部分形成对象形状,并将像素图像监视到超像素中,该算法与约束相结合。 本公开描述了从混合图计算掩模图,将图像分割成前景对象和背景,以及从背景显示前景对象。