公开(公告)号：US20100067799A1

公开(公告)日：2010-03-18

申请号：US12212222

申请日：2008-09-17

申请人： Guangcan Liu ,  Zhouchen Lin ,  Xiaoou Tang

发明人： Guangcan Liu ,  Zhouchen Lin ,  Xiaoou Tang

IPC分类号： G06K9/46

CPC分类号： G06K9/4647

摘要： A “globally invariant Radon feature transform,” or “GIRFT,” generates feature descriptors that are both globally affine invariant and illumination invariant. These feature descriptors effectively handle intra-class variations resulting from geometric transformations and illumination changes to provide robust texture classification. In general, GIRFT considers images globally to extract global features that are less sensitive to large variations of material in local regions. Geometric affine transformation invariance and illumination invariance is achieved by converting original pixel represented images into Radon-pixel images by using a Radon Transform. Canonical projection of the Radon-pixel image into a quotient space is then performed using Radon-pixel pairs to produce affine invariant feature descriptors. Illumination invariance of the resulting feature descriptors is then achieved by defining an illumination invariant distance metric on the feature space of each feature descriptor.

摘要翻译： “全局不变的氡特征变换”或“GIRFT”产生全局仿射不变和照明不变的特征描述符。 这些特征描述符有效地处理由几何变换和照明变化产生的类内变化，以提供鲁棒的纹理分类。 一般来说，GIRFT在全球范围内考虑图像，以提取对本地区域的大量材料较不敏感的全局特征。 通过使用Radon变换将原始像素表示的图像转换为氡像素图像来实现几何仿射变换不变性和照度不变性。 然后使用氡 - 像素对执行氡像素图像到商空间的规范投影，以产生仿射不变特征描述符。 然后通过在每个特征描述符的特征空间上定义照明不变距离度量来实现所得特征描述符的照明不变性。

公开(公告)号：US07646894B2

公开(公告)日：2010-01-12

申请号：US11276112

申请日：2006-02-14

申请人： Qiong Yang ,  Xiaoou Tang

发明人： Qiong Yang ,  Xiaoou Tang

IPC分类号： G06K9/62 ,  G06Y7/00 ,  G06K9/00

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。

公开(公告)号：US07536030B2

公开(公告)日：2009-05-19

申请号：US11290135

申请日：2005-11-30

申请人： Qiang Wang ,  Weiwei Zhang ,  Xiaoou Tang ,  Heung-Yeung Shum

发明人： Qiang Wang ,  Weiwei Zhang ,  Xiaoou Tang ,  Heung-Yeung Shum

IPC分类号： G06K9/00

CPC分类号： G06K9/00208 ,  G06K9/00241

摘要： Systems and methods are described for real-time Bayesian 3D pose tracking. In one implementation, exemplary systems and methods formulate key-frame based differential pose tracking in a probabilistic graphical model. An exemplary system receives live captured video as input and tracks a video object's 3D pose in real-time based on the graphical model. An exemplary Bayesian inter-frame motion inference technique simultaneously performs online point matching and pose estimation. This provides robust pose tracking because the relative pose estimate for a current frame is simultaneously estimated from two independent sources, from a key-frame pool and from the video frame preceding the current frame. Then, an exemplary online Bayesian frame fusion technique infers the current pose from the two independent sources, providing stable and drift-free tracking, even during agile motion, occlusion, scale change, and drastic illumination change of the tracked object.

摘要翻译： 描述了实时贝叶斯3D姿态跟踪的系统和方法。 在一个实现中，示例性系统和方法在概率图形模型中制定基于关键帧的差分姿态跟踪。 示例性系统基于图形模型实时地接收实时捕获的视频作为输入并实时跟踪视频对象的3D姿态。 示例性的贝叶斯帧间运动推理技术同时执行在线点匹配和姿态估计。 这提供了鲁棒的姿势跟踪，因为当前帧的相对姿态估计是从两个独立的来源，从关键帧池和当前帧之前的视频帧同时估计的。 然后，示例性的在线贝叶斯帧融合技术从两个独立的来源推测出当前姿态，即使在敏捷运动，闭塞，比例变化和跟踪对象的剧烈照明改变期间也能提供稳定和无漂移的跟踪。

公开(公告)号：US07439989B2

公开(公告)日：2008-10-21

申请号：US11276204

申请日：2006-02-17

申请人： Zhouchen Lin ,  Junfeng He ,  Xiaoou Tang

发明人： Zhouchen Lin ,  Junfeng He ,  Xiaoou Tang

IPC分类号： G06T15/00

CPC分类号： G06T1/0042 ,  G06T2201/0052 ,  H04N19/60 ,  H04N19/86

摘要： Systems and methods for detecting doctored JPEG images are described. In one aspect, a JPEG image is evaluated to determine if the JPEG image comprises double quantization effects of double quantized Discrete Cosine Transform coefficients. In response to results of these evaluation operations, the systems and methods determine whether the JPEG image has been doctored and identify any doctored portion.

摘要翻译： 描述用于检测编码的JPEG图像的系统和方法。 在一个方面，评估JPEG图像以确定JPEG图像是否包括双量化离散余弦变换系数的双量化效应。 响应于这些评估操作的结果，系统和方法确定JPEG图像是否被编辑并识别任何编辑部分。

公开(公告)号：US07382928B2

公开(公告)日：2008-06-03

申请号：US10913895

申请日：2004-08-06

申请人： Kam Fai Wong ,  Xiaoou Tang ,  Ying Fai Ho ,  Kwok Yiu Leung ,  Hoi Chung Chu

发明人： Kam Fai Wong ,  Xiaoou Tang ,  Ying Fai Ho ,  Kwok Yiu Leung ,  Hoi Chung Chu

IPC分类号： G06K9/00

CPC分类号： H04N5/3675 ,  G06T5/002 ,  G06T5/007 ,  G06T5/20 ,  G06T2207/10024 ,  H04N1/60 ,  H04N9/646 ,  H04N9/68 ,  H04N9/69 ,  H04N9/73

摘要： Disclosed is an automatic digital image enhancement method, including: determining the defects existing in the image based on pixel values, such as pixel luminance values and pixel color values, of the input digital image, and making corrections based on the determination results. The method can process images of digital photos, digital videos and make enhancement on exposure and color of the images. The method disclosed can also improve quickly and apparently the visual quality and appearance of images.

摘要翻译： 公开了一种自动数字图像增强方法，包括：基于输入数字图像的诸如像素亮度值和像素颜色值的像素值确定图像中存在的缺陷，并且基于确定结果进行校正。 该方法可以处理数字照片，数字视频的图像，并对图像的曝光和颜色进行增强。 所公开的方法还可以快速且明显地提高图像的视觉质量和外观。

公开(公告)号：US20080068386A1

公开(公告)日：2008-03-20

申请号：US11532052

申请日：2006-09-14

申请人： Zhouchen Lin ,  Lifeng Wang ,  Tian Fang ,  Xu Yang ,  Xuan Yu ,  Jian Wang ,  Xiaoou Tang

发明人： Zhouchen Lin ,  Lifeng Wang ,  Tian Fang ,  Xu Yang ,  Xuan Yu ,  Jian Wang ,  Xiaoou Tang

IPC分类号： G06T13/00

CPC分类号： G06T13/60 ,  G06T2210/56

摘要： Real-time rendering of realistic rain is described. In one aspect, image samples of real rain and associated information are automatically modeled in real-time to generate synthetic rain particles in view of respective scene radiances of target video content frames. The synthetic rain particles are rendered in real-time using pre-computed radiance transfer with uniform random distribution across respective frames of the target video content.

摘要翻译： 描述了实时降雨的实时渲染。 在一个方面，根据目标视频内容帧的相应场景辐射，实时地自动建模真实雨和相关信息的图像样本以产生合成雨粒子。 使用预先计算的辐射传输，通过目标视频内容的各个帧上的​​均匀随机分布实时地渲染合成雨粒子。

公开(公告)号：US20070147677A1

公开(公告)日：2007-06-28

申请号：US11275265

申请日：2005-12-21

申请人： Yasuyuki Matsushita ,  Xiaoou Tang ,  Francois Alter

发明人： Yasuyuki Matsushita ,  Xiaoou Tang ,  Francois Alter

IPC分类号： G06K9/62

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。

公开(公告)号：US20060262973A1

公开(公告)日：2006-11-23

申请号：US11132865

申请日：2005-05-19

申请人： Zhouchen Lin ,  Rongrong Wang ,  Xiaoou Tang ,  Heung-Yeung Shum

发明人： Zhouchen Lin ,  Rongrong Wang ,  Xiaoou Tang ,  Heung-Yeung Shum

IPC分类号： G06K9/62 ,  G06K9/00 ,  G06K15/00

CPC分类号： G06K9/00 ,  G06K9/00899

摘要： Embodiments of the invention determine whether an image has been altered. Sets of patches are selected in the image, and corresponding inverse response functions are provided to a support vector machine (SVM). The support vector machine is trained with exemplary normal and abnormal inverse response functions. Once trained, the support vector machine analyzes inverse response functions corresponding to a suspected image. The support vector machine determines if the inverse response functions are normal or abnormal by analyzing a set of features. In one embodiment, features include measures for monotonic characteristics, fluctuation characteristics, and divergence characteristics of the red, green, and blue components of a tuple. Each tuple of inverse response functions is associated with a set of patches selected in the image.