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    • 21. 发明申请
    • AUTOMATIC PARAMETER ESTIMATION FOR ADAPTIVE PIXEL-BASED FILTERING
    • 基于自适应像素滤波的自动参数估计
    • WO2008042029A2
    • 2008-04-10
    • PCT/US2007/014890
    • 2007-06-25
    • THOMSON LICENSINGBHAGAVATHY, SitaramLLACH, Joan
    • BHAGAVATHY, SitaramLLACH, Joan
    • G06T5/002G06T5/20G06T5/50G06T2207/10016G06T2207/20182
    • One particular automatic parameter estimation method and apparatus estimates low level filtering parameters from one or more user controlled high-level filtering parameters. The high level filtering parameters are strength and quality, where strength indicates how much noise reduction will be performed, and quality indicates a tolerance which controls the balance between filtering uniformity and loss of detail. The low level filtering parameters that can be estimated include the spatial neighborhood and/or temporal neighborhood size from which pixel candidates are selected, and thresholds used to verify the 'goodness' of the spatially or temporally predicted candidate pixels. More generally, a criterion for filtering digital image data is accessed (902), and a value is determined (904) for a parameter for use in filtering digital image data, the value being determined based on whether the value results in the criterion being satisfied for at least a portion of a digital image.
    • 一种特定的自动参数估计方法和装置根据一个或多个用户控制的高级滤波参数来估计低级滤波参数。 高级别的过滤参数是强度和质量,其中强度指示将执行多少噪声降低,并且质量指示控制过滤均匀性和细节丢失之间平衡的容差。 可以估计的低级滤波参数包括从其中选择像素候选的空间邻域和/或时间邻域大小,以及用于验证空间或时间预测的候选像素的“良好”的阈值。 更一般地,访问(902)用于过滤数字图像数据的标准,并且针对用于过滤数字图像数据的参数确定(904)值,该值基于该值是否导致标准被满足来确定 至少为数字图像的一部分。
    • 23. 发明申请
    • DETECTION OF FIELD LINES IN SPORTS VIDEOS
    • 运动场景线路检测
    • WO2010083021A1
    • 2010-07-22
    • PCT/US2010/000032
    • 2010-01-07
    • THOMSON LICENSINGJACOB, Mithun, GeorgeBHAGAVATHY, SitaramBARCON-PALAU, JesusLLACH, Joan
    • JACOB, Mithun, GeorgeBHAGAVATHY, SitaramBARCON-PALAU, JesusLLACH, Joan
    • H04N7/18
    • G06T7/13G06T7/181G06T2207/10016G06T2207/20016G06T2207/20061G06T2207/20164G06T2207/30228
    • A method for accurately and robustly detecting field lines in an image, such as a frame of a sports video, includes: convolving the image with a Laplacian operator to generate a Laplacian image emphasizing likely line pixels; removing non-playfield related pixels from the Laplacian image, including pixels lying outside of the playfield and pixels representing players within the playfield; contrast stretching the resultant Laplacian image; iteratively applying a Hough transform to the contrast-stretched Laplacian image to detect lines, wherein each iteration results in the detection of one or more field lines which are removed prior to the next iteration, thus reducing the complexity of the next iteration; identifying fragments along each field line detected; filling gaps in the field lines, wherein the gaps are analyzed so that only those gaps that are not likely due to occlusion by players or other non-line objects are filled; and providing a binary mask indicating the pixels representing field lines.
    • 一种用于准确且鲁棒地检测诸如运动视频的帧的图像中的场线的方法包括:使用拉普拉斯算子来卷积图像以生成强调可能线像素的拉普拉斯图像; 从拉普拉斯图像中去除非播放场相关像素,包括位于播放区域外的像素和表示播放区域内播放器的像素; 对比拉伸拉普拉斯图像; 迭代地将霍夫变换应用于对比拉伸拉普拉斯图像以检测线,其中每次迭代导致在下一次迭代之前被去除的一个或多个场线的检测,从而降低下次迭代的复杂度; 识别沿着每个场线的片段; 填充场线中的间隙,其中分析间隙,使得仅填充由于玩家或其他非线对象的遮挡而不可能的那些间隙; 并提供指示表示场线的像素的二进制掩码。
    • 25. 发明申请
    • ADAPTIVE PIXEL-BASED FILTERING
    • 自适应像素滤波
    • WO2008005007A1
    • 2008-01-10
    • PCT/US2006/025738
    • 2006-06-29
    • THOMSON LICENSINGBHAGAVATHY, SitaramLLACH, Joan
    • BHAGAVATHY, SitaramLLACH, Joan
    • H04N7/26H04N5/21
    • H04N5/21G06T5/002G06T5/20G06T5/50G06T2207/10016G06T2207/20182H04N19/105H04N19/117H04N19/162H04N19/182H04N19/537H04N19/593H04N19/86
    • In an implementation, a pixel is selected from a target digital image. Multiple candidate pixels, from one or more digital images, are evaluated based on values of the multiple candidate pixels. For the selected pixel, a corresponding set of pixels is determined from the multiple candidate pixels based on the evaluations of the multiple candidate pixels and on whether a predetermined threshold number of pixels have been included in the corresponding set. Further for the selected pixel, a substitute value is determined based on the values of the pixels in the corresponding set of pixels. Various implementations described provide adaptive pixel-based spatio-temporal filtering of images or video to reduce film grain or noise. Implementations may achieve an "even" amount of noise reduction at each pixel while preserving as much picture detail as possible by, for example, averaging each pixel with a constant number, N, of temporally and/or spatially correlated pixels.
    • 在实现中,从目标数字图像中选择像素。 基于多个候选像素的值来评估来自一个或多个数字图像的多个候选像素。 对于所选择的像素,基于多个候选像素的评估以及相应组中是否包括预定阈值数量的像素,从多个候选像素确定相应的像素集合。 此外,对于所选择的像素,基于相应像素集合中的像素的值来确定替代值。 所描述的各种实现提供了对图像或视频的自适应基于像素的时空滤波以减少胶片颗粒或噪声。 实施方式可以在每个像素处实现“均匀”的噪声降低量,同时通过例如使用恒定数量N对时间和/或空间相关像素对每个像素进行平均,尽可能地保留尽可能多的图像细节。
    • 28. 发明申请
    • METHOD AND APPARATUS FOR VIDEO OBJECT SEGMENTATION
    • 视频对象分割的方法和装置
    • WO2011090789A1
    • 2011-07-28
    • PCT/US2011/000106
    • 2011-01-20
    • THOMSON LICENSINGBHASKARANAND, MalavikaBHAGAVATHY, Sitaram
    • BHASKARANAND, MalavikaBHAGAVATHY, Sitaram
    • G06T7/20
    • G06T7/2053G06T7/194G06T7/254G06T2207/10016
    • Methods and apparatus for video object segmentation are provided, suitable for use in a super-resolution system. The method comprises alignment of frames of a video sequence, pixel alignment to generate initial foreground masks using a similarity metric, consensus filtering to generate an intermediate foreground mask, and refinement of the mask using spatio-temporal information from the video sequence. In various embodiments, the similarity metric is computed using a sum of squared differences approach, a correlation, or a modified normalized correlation metric. Soft thresholding of the similarity metric is also used in one embodiment of the present principles. Weighting factors are also applied to certain critical frames in the consensus filtering stage in one embodiment using the present principles.
    • 提供视频对象分割的方法和装置,适用于超分辨率系统。 该方法包括对视频序列的帧进行对齐,使用相似性度量生成初始前景掩码,生成中间前景掩码的一致滤波,以及使用来自视频序列的时空信息来细化掩码。 在各种实施例中,使用平方差法,相关性或修正的归一化相关度量的和来计算相似性度量。 相似性度量的软阈值也用于本原理的一个实施例中。 使用本原理,在一个实施例中,加权因子也应用于一致性过滤阶段中的某些关键帧。