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    • 41. 发明申请
    • IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, PROGRAM, AND RECORDING MEDIUM
    • 图像处理设备,图像处理方法,程序和记录介质
    • US20120250982A1
    • 2012-10-04
    • US13427199
    • 2012-03-22
    • Masato ITOKohtaro SabeJun Yokono
    • Masato ITOKohtaro SabeJun Yokono
    • G06K9/62
    • G06T7/0042G06T7/12G06T7/174G06T7/194G06T7/73G06T2207/20081G06T2207/20164
    • An image processing apparatus includes: an image feature outputting unit that outputs each of image features in correspondence with a time of the frame; a foreground estimating unit that estimates a foreground image at a time s by executing a view transform as a geometric transform on a foreground view model and outputs an estimated foreground view; a background estimating unit that estimates a background image at the time s by executing a view transform as a geometric transform on a background view model and outputs an estimated background view; a synthesized view generating unit that generates a synthesized view by synthesizing the estimated foreground and background views; a foreground learning unit that learns the foreground view model based on an evaluation value; and a background learning unit that learns the background view model based on the evaluation value by updating the parameter of the foreground view model.
    • 一种图像处理装置包括:图像特征输出单元,其与帧的时间相对应地输出每个图像特征; 前景估计单元,其通过在前景视图模型上执行视角变换作为几何变换来估计时间s的前景图像,并输出估计的前景视图; 背景估计单元,其通过在背景视图模型上执行视图变换作为几何变换来估计在时间s的背景图像,并输出估计的背景视图; 合成视图生成单元,其通过合成估计的前景和背景视图来生成合成视图; 前景学习单元,其基于评估值学习前景视图模型; 以及背景学习单元,通过更新前景视图模型的参数,基于评估值来学习背景视图模型。
    • 44. 发明申请
    • Image Processing Apparatus, Image Processing Method, and Program
    • 图像处理装置,图像处理方法和程序
    • US20070242876A1
    • 2007-10-18
    • US11697203
    • 2007-04-05
    • Kohtaro SabeJun Yokono
    • Kohtaro SabeJun Yokono
    • G06K9/00
    • G06K9/4652G06K9/6203G06K2009/6213
    • The present invention provides an image processing apparatus to recognize a predetermined model whose surface has a plurality of colors from an input color image that is obtained by capturing an image of a color object whose surface has a plurality of colors. The image processing apparatus includes a detecting unit configured to detect color areas from the input color image, each color area including adjoining pixels of the same color; and a recognizing unit configured to determine whether the color areas on the input color image detected by the detecting unit correspond to parts of the model to which color areas on a reference color image obtained by capturing an image of the model correspond, and determine whether the color object in the input color image is the model on the basis of the determination result.
    • 本发明提供了一种图像处理装置,其从通过捕获其表面具有多种颜色的彩色对象的图像而获得的输入彩色图像来识别其表面具有多种颜色的预定模型。 所述图像处理装置包括检测单元,其被配置为从所述输入彩色图像检测颜色区域,每个颜色区域包括相同颜色的相邻像素; 以及识别单元,其被配置为确定由所述检测单元检测到的输入彩色图像上的颜色区域是否对应于通过捕获所述模型的图像而获得的参考彩色图像上的颜色区域对应的模型的部分,并且确定是否 输入彩色图像中的彩色对象是基于确定结果的模型。
    • 46. 发明授权
    • Image processing system, learning device and method, and program
    • 图像处理系统,学习装置和方法,程序
    • US08582887B2
    • 2013-11-12
    • US11813404
    • 2005-12-26
    • Hirotaka SuzukiAkira NakamuraTakayuki YoshigaharaKohtaro SabeMasahiro Fujita
    • Hirotaka SuzukiAkira NakamuraTakayuki YoshigaharaKohtaro SabeMasahiro Fujita
    • G06K9/00
    • G06K9/00288G06K9/6211G06K9/623G06T7/00
    • The present invention relates to an image processing system, a learning device and method, and a program which enable easy extraction of feature amounts to be used in a recognition process. Feature points are extracted from a learning-use model image, feature amounts are extracted based on the feature points, and the feature amounts are registered in a learning-use model dictionary registration section 23. Similarly, feature points are extracted from a learning-use input image containing a model object contained in the learning-use model image, feature amounts are extracted based on these feature points, and these feature amounts are compared with the feature amounts registered in a learning-use model registration section 23. A feature amount that has formed a pair the greatest number of times as a result of the comparison is registered in the model dictionary registration section 12 as the feature amount to be used in the recognition process. The present invention is applicable to a robot.
    • 本发明涉及图像处理系统,学习装置和方法以及能够容易地提取在识别处理中使用的特征量的程序。 从学习用模型图像提取特征点,基于特征点提取特征量,并且将特征量登记在学习用模型字典注册部23中。同样,从学习用途中提取特征点 基于这些特征点提取含有包含在学习用模型图像中的模型对象的输入图像,并将这些特征量与在学习用模型登记部23中登记的特征量进行比较。特征量 作为比较的结果,在模型字典登记部12中登记了作为识别处理中使用的特征量的最大次数的对。 本发明可应用于机器人。
    • 47. 发明授权
    • Information processing apparatus, information processing method, and program
    • 信息处理装置,信息处理方法和程序
    • US08571315B2
    • 2013-10-29
    • US13288231
    • 2011-11-03
    • Kohtaro SabeKenichi HidaiKiyoto Ichikawa
    • Kohtaro SabeKenichi HidaiKiyoto Ichikawa
    • G06K9/00G06K9/38G06K9/62G06K9/68
    • G06K9/4614G06K9/6256
    • An information processing apparatus includes: a distinguishing unit which, by using an ensemble classifier, which includes a plurality of weak classifiers outputting weak hypotheses which indicates whether a predetermined subject is shown in an image in response to inputs of a plurality of features extracted from the image, and a plurality of features extracted from an input image, sequentially integrates the weak hypotheses output by the weak classifiers in regard to the plurality of features and distinguishes whether the predetermined subject is shown in the input image based on the integrated value. The weak classifier classifies each of the plurality of features to one of three or more sub-divisions based on threshold values, calculates sum divisions of the sub-divisions of the plurality of features as whole divisions into which the plurality of features is classified, and outputs, as the weak hypothesis, a reliability degree of the whole divisions.
    • 一种信息处理装置,包括:识别单元,其使用整体分类器,其包括输出弱假设的多个弱分类器,所述弱分类器指示响应于从所述图像提取的多个特征的输入,是否在图像中示出预定对象 图像和从输入图像提取的多个特征,顺序地对由弱分类器输出的关于多个特征的弱假设进行积分,并且基于积分值区分输入图像中是否显示预定对象。 所述弱分类器基于阈值将所述多个特征中的每一个分类为三个或更多个子分割中的一个,并且将所述多个特征的子分割的和除作为将所述多个特征分类成的整个分割,以及 作为弱假设的输出是整个分区的可靠性程度。