CONTENT ADAPTIVE DETECTION OF IMAGES WITH STAND-OUT OBJECT
    1.
    发明申请
    CONTENT ADAPTIVE DETECTION OF IMAGES WITH STAND-OUT OBJECT 有权
    内容自适应检测图像与标准对象

    公开(公告)号:US20100321513A1

    公开(公告)日:2010-12-23

    申请号:US12486487

    申请日:2009-06-17

    IPC分类号: H04N5/228 G06K9/48 G06K9/34

    CPC分类号: G06K9/00

    摘要: Content adaptive detection of images having stand-out objects involves block variance-based detection and determining if an object includes a stand-out object. The images with a stand-out object are further processed to isolate an object of interest. The images without a detected stand-out object are further processed with a transition map-based detection method which includes generating a transition map. If an object portrait is determined from the transition map, then the image is further processed to isolate the object of interest.

    摘要翻译: 具有突出对象的图像的内容自适应检测涉及基于块方差的检测,并且确定对象是否包括独立对象。 具有突出对象的图像被进一步处理以隔离感兴趣的对象。 使用基于过渡映射的检测方法进一步处理没有检测到的突出对象的图像,该方法包括生成转换图。 如果从转换图确定对象画像,则进一步处理图像以隔离感兴趣的对象。

    ORIENTATION-BASED APPROACH FOR FORMING A DEMOSAICED IMAGE, AND FOR COLOR CORRECTING AND ZOOMING THE DEMOSAICED IMAGE
    2.
    发明申请
    ORIENTATION-BASED APPROACH FOR FORMING A DEMOSAICED IMAGE, AND FOR COLOR CORRECTING AND ZOOMING THE DEMOSAICED IMAGE 有权
    基于方向的方法,用于形成一个降解图像,并用于颜色校正和变焦图像

    公开(公告)号:US20100253817A1

    公开(公告)日:2010-10-07

    申请号:US12418207

    申请日:2009-04-03

    IPC分类号: H04N5/335 G06K9/00

    摘要: A method and apparatus for forming a demosaiced image from a color-filter-array (“CFA”) image is provided. The CFA image comprises a first set of pixels colored according to a first (e.g., a green) color channel, a second set of pixels colored according to a second (e.g., a red) color channel and a third set of pixels colored according to a third (e.g., blue) color channel. The method may include obtaining an orientation map, which includes, for each pixel of the color-filter-array image, an indicator of orientation of an edge bounding such pixel. The method may further include interpolating the first color channel at the second and third sets of pixels as a function of the orientation map so as to form a fourth set of pixels. The method may also include interpolating the second color channel at the first and third sets of pixels as a function of the orientation map and the fourth set of pixels; and interpolating the third color channel at the first and second sets of pixels as a function of the orientation map and the fourth set of pixels.

    摘要翻译: 提供了一种用于从彩色滤光片阵列(“CFA”)图像形成去马赛克图像的方法和装置。 CFA图像包括根据第一(例如,绿色)颜色通道着色的第一组像素,根据第二(例如,红色)颜色通道着色的第二组像素,以及根据第 第三(例如蓝色)彩色通道。 该方法可以包括获得方位图,其包括对于彩色滤波器阵列图像的每个像素的边界边界的方向的指示符。 该方法可以进一步包括根据取向图来插值第二和第三组像素处的第一颜色通道,以便形成第四组像素。 该方法还可以包括根据取向图和第四组像素来内插第一和第三组像素处的第二颜色通道; 以及作为所述取向图和所述第四组像素的函数,在所述第一和第二像素组处插入所述第三颜色通道。

    VIDEO SYSTEM WITH BLOCKING ARTIFACT FILTERING
    3.
    发明申请
    VIDEO SYSTEM WITH BLOCKING ARTIFACT FILTERING 失效
    具有阻塞文艺过滤的视频系统

    公开(公告)号:US20100111435A1

    公开(公告)日:2010-05-06

    申请号:US12266551

    申请日:2008-11-06

    IPC分类号: G06K9/40

    摘要: A video system includes: analyzing video data, having a block; performing a transition change detection for determining a spatial intensity transition within the block; performing a block-wise similarity measurement on the block in the video data for identifying a blocking artifact; and filtering with a two dimensional cross filter every pixel in the block for removing the blocking artifact.

    摘要翻译: 视频系统包括:分析具有块的视频数据; 执行用于确定所述块内的空间强度转换的转变变化检测; 对所述视频数据中的块进行块式相似度测量,以识别块伪影; 并使用块中的每个像素对二维交叉滤波进行滤波,以消除块伪影。

    Block based codec friendly edge detection and transform selection
    4.
    发明申请
    Block based codec friendly edge detection and transform selection 有权
    基于块的编解码器友好边缘检测和变换选择

    公开(公告)号:US20090262800A1

    公开(公告)日:2009-10-22

    申请号:US12148582

    申请日:2008-04-18

    IPC分类号: H04N7/30

    摘要: Low complexity edge detection and DCT type selection method to improve the visual quality of H.264/AVC encoded video sequence is described. Encoding-generated information is reused to detect an edge macroblock. Variance and Mean Absolute Difference (MAD) of one macroblock shows a certain relationship that is able to be used to differentiate the edge macroblock and the non-edge macroblock. Also, the variance difference of neighbor macroblocks provides a hint for edge existence. Then, a block-based edge detection method uses this information. To determine the DCT type for each block, the detected edges are differentiated as visual obvious edge, texture-like edge, soft edge and strong edge. 8×8 DCT is used for texture-like edges and the 4×4 DCT is used for all the other edges. The result is an efficient and accurate edge detection and transform selection method.

    摘要翻译: 描述了低复杂度边缘检测和DCT类型选择方法,以提高H.264 / AVC编码视频序列的视觉质量。 编码生成的信息被重新用于检测边缘宏块。 一个宏块的方差和平均绝对差(MAD)表示能够用于区分边缘宏块和非边缘宏块的一定关系。 此外,相邻宏块的方差差异提供了边缘存在的提示。 然后,基于块的边缘检测方法使用该信息。 为了确定每个块的DCT类型,检测到的边缘被区分为视觉明显的边缘,纹理样边缘,软边缘和强边。 8x8 DCT用于纹理样边缘,4x4 DCT用于所有其他边。 结果是一种高效准确的边缘检测和变换选择方法。

    CODING TOOL SELECTION IN VIDEO CODING BASED ON HUMAN VISUAL TOLERANCE
    5.
    发明申请
    CODING TOOL SELECTION IN VIDEO CODING BASED ON HUMAN VISUAL TOLERANCE 审中-公开
    基于人类视觉容忍度的视频编码中的编码工具选择

    公开(公告)号:US20090074058A1

    公开(公告)日:2009-03-19

    申请号:US11855888

    申请日:2007-09-14

    IPC分类号: H04N7/12

    摘要: In one embodiment, a coding mode selection method is provided to improve the visual quality of an encoded video sequence. The coding mode is selected based on a human visual tolerance level. Picture data may be received for a video coding process. The picture data is then analyzed to determine human visual tolerance adjustment information. For example, parameters of a cost equation may be adjusted based on the human visual tolerance level, which may be a tolerance that is based on a distortion bound that the human visual system can tolerate. The picture data may be analyzed in places that are considered visually sensitive areas, such as trailing suspicious areas, stripping suspicious areas, picture boundary areas, and/or blocking suspicious areas. Depending on what kind of visually sensitive area is found in the picture data, a parameter in a cost equation may be adjusted based on different visual tolerance thresholds. The coding mode is then determined based on the cost.

    摘要翻译: 在一个实施例中,提供编码模式选择方法以提高编码视频序列的视觉质量。 基于人的视觉容忍度来选择编码模式。 可以接收用于视频编码处理的图像数据。 然后分析图像数据以确定人类视觉容差调整信息。 例如,成本方程的参数可以基于人的视觉容忍度来调整,其可以是基于人类视觉系统可以容忍的失真约束的公差。 可以在被认为是视觉敏感区域的地方分析图像数据,例如可疑区域,剥离可疑区域,图像边界区域和/或阻止可疑区域。 根据在图像数据中找到什么样的视觉敏感区域,可以基于不同的视觉容差阈值来调整成本方程中的参数。 然后根据成本确定编码模式。

    Method and apparatus for stain separation using vector analysis
    6.
    发明授权
    Method and apparatus for stain separation using vector analysis 有权
    使用向量分析进行染色分离的方法和装置

    公开(公告)号:US09036889B2

    公开(公告)日:2015-05-19

    申请号:US13549019

    申请日:2012-07-13

    IPC分类号: G06K9/00 G06T7/00 G06T7/40

    摘要: A computer-implemented method and apparatus for stain separation of a pathology image using stain vector analysis comprising converting an original image into an optical domain image, performing stain vector analysis on the optical domain image to obtain one or more stain vectors, deconvoluting the vectors adaptively to produce one or more separated stain images.

    摘要翻译: 一种用于使用染色载体分析对病理图像进行染色分离的计算机实现的方法和装置,包括将原始图像转换为光域图像,对光域图像执行染色矢量分析以获得一个或多个染色载体,自适应地卷积载体 以产生一个或多个分离的污点图像。

    System and method for effectively performing a scene representation procedure
    7.
    发明授权
    System and method for effectively performing a scene representation procedure 有权
    用于有效执行场景表示过程的系统和方法

    公开(公告)号:US08873833B2

    公开(公告)日:2014-10-28

    申请号:US13398948

    申请日:2012-02-17

    IPC分类号: G06K9/00

    CPC分类号: G06K9/00718 G06K9/469

    摘要: A system for performing a scene representation procedure includes an image manager that processes source images from a given scene to define subscenes in the source images. The image manager creates an image understanding graph for each of the source images, and also creates a scene representation graph for each of the source images based upon the corresponding subscenes and certain image characteristics. The image manager further generates an integrated scene representation to represent all of the source images with a single representation. A processor of an electronic device controls the image manager to perform the scene representation procedure.

    摘要翻译: 用于执行场景表示过程的系统包括图像管理器,其处理来自给定场景的源图像以在源图像中定义子信号。 图像管理器为每个源图像创建图像理解图,并且还基于相应的子像素和某些图像特征为每个源图像创建场景表示图。 图像管理器进一步生成集成的场景表示以用单个表示来表示所有源图像。 电子设备的处理器控制图像管理器执行场景表示过程。

    Region description and modeling for image subscene recognition
    8.
    发明授权
    Region description and modeling for image subscene recognition 有权
    图像二次识别的区域描述和建模

    公开(公告)号:US08705866B2

    公开(公告)日:2014-04-22

    申请号:US12962647

    申请日:2010-12-07

    摘要: A method and apparatus is described here that categorizes images by extracting regions and describing the regions with a 16-dimensional subscene feature vector, which is a concatenation of color, texture, and spatial feature vectors. By comparing the spatial feature vectors in images with similarly-obtained feature vectors in a Gaussian mixture based model pool (obtained in a subscene modeling phase), the images may be categorized (in a subscene recognition phase) with probabilities relating to each region or subscene. Higher probabilities are likelier correlations. The device may be a single or multiple core CPU, or parallelized vector processor for characterizing many images. The images may be photographs, videos, or video stills, without restriction. When used real-time, the method may be used for visual searching or sorting.

    摘要翻译: 这里描述了一种方法和装置,其通过提取区域并且用具有颜色,纹理和空间特征向量的级联的16维子像素特征向量来描述区域来对图像进行分类。 通过将图像中的空间特征向量与基于高斯混合的模型池(在亚型建模阶段中获得)中的类似获得的特征向量进行比较,可以将图像分类(在子序列识别阶段),其具有与每个区域或子网相关的概率 。 更高的概率是可能的相关性。 该设备可以是用于表征许多图像的单个或多个核心CPU或并行化矢量处理器。 图像可以是照片,视频或视频静止图像,没有限制。 当实时使用时,该方法可用于视觉搜索或排序。

    System and method for “Bokeh-Aji” shot detection and region of interest isolation
    9.
    发明授权
    System and method for “Bokeh-Aji” shot detection and region of interest isolation 有权
    散景 - Aji镜头检测和感兴趣区域分离的系统和方法

    公开(公告)号:US08346005B2

    公开(公告)日:2013-01-01

    申请号:US12566445

    申请日:2009-09-24

    IPC分类号: G06K9/40

    摘要: A “Bokeh-Aji” image is one in which the region of interest is in focus and the background is out of focus. Detection of “Bokeh-Aji” type images and then isolation to the region of interest area in a low complexity way without any human intervention is beneficial. A set of tools for performing this task include SAD and high pass filtering based in-focus/out-of-focus area separation, in-focus/out-of-focus block distribution based “Bokeh-Aji” shot detection and region of interest isolation. By effectively integrating these tools together, the “Bokeh-Aji” images are successfully identified, and the region of interest area is successfully isolated.

    摘要翻译: 散景 - 阿奇图像是感兴趣的区域在焦点中并且背景偏离焦点的图像。 检测散景 - Aji型图像,然后以低复杂度方式分离到感兴趣区域,而无需人为干预是有益的。 用于执行此任务的一组工具包括基于焦点/离焦区域分离的SAD和高通滤波,基于焦点/焦点外块分布的Bokeh-Aji镜头检测和感兴趣区域隔离。 通过有效地将这些工具集成在一起,成功地识别了Bokeh-Aji图像,并且成功地隔离了感兴趣区域。

    Color and intensity based meaningful object of interest detection
    10.
    发明授权
    Color and intensity based meaningful object of interest detection 有权
    基于颜色和强度的有意义的检测对象

    公开(公告)号:US08331684B2

    公开(公告)日:2012-12-11

    申请号:US12723438

    申请日:2010-03-12

    IPC分类号: G06K9/46

    CPC分类号: G06K9/00664 G06K9/3241

    摘要: An apparatus and method for detecting “Object Portraits” (photographs or images with a stand-out object of interest or a set of stand-out objects of interest) is described. A set of tools has been developed for object of interest detection, including “Sunset-like” scene detection, pseudo-color saturation-based detection and object of interest isolation, block intensity based detection and object of interest isolation. By effectively integrating these tools together, the “Object Portrait” images and “Non-Object Portrait” images are successfully identified. Meaningful object of interest areas are thereby successfully isolated in a low complexity manner without human intervention.

    摘要翻译: 描述了用于检测对象肖像(具有引人注目的对象的感兴趣的对象或一组待排除的对象的照片或图像)的装置和方法。 已经开发了一组工具,用于感兴趣的检测对象,包括日落式场景检测,基于伪彩色饱和度检测和感兴趣对象隔离,基于块强度检测和感兴趣对象隔离。 通过有效地将这些工具集成在一起,成功识别对象肖像图像和非对象肖像图像。 因此,有意义的利益领域的对象在没有人为干预的情况下以低复杂度方式被成功隔离。