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    • 41. 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。
    • 43. 发明授权
    • Learning device for generating a classifier for detection of a target
    • 用于生成用于检测目标的分类器的学习装置
    • US09053358B2
    • 2015-06-09
    • US13470660
    • 2012-05-14
    • Jun Yokono
    • Jun Yokono
    • G06K9/00G06K9/62
    • G06K9/00389G06K9/4604G06K9/6219G06K9/6257G06K9/627
    • Disclosed is a learning device. A feature-quantity calculation unit extracts a feature quantity from each feature point of a learning image. An acquisition unit acquires a classifier already obtained by learning as a transfer classifier. A classifier generation unit substitutes feature quantities into weak classifiers constituting the transfer classifier, calculates error rates of the weak classifiers on the basis of classification results of the weak classifiers and a weight of the learning image, and iterates a process of selecting a weak classifier of which the error rate is minimized a plurality of times. In addition, the classifier generation unit generates a classifier for detecting a detection target by linearly coupling a plurality of selected weak classifiers.
    • 公开了一种学习装置。 特征量计算单元从学习图像的每个特征点提取特征量。 获取单元获取已经通过学习作为传送分类器获得的分类器。 分类器生成单元将特征量替换为构成传输分类器的弱分类器,基于弱分类器的分类结果和学习图像的权重计算弱分类器的误码率,并且迭代选择弱分类器的弱分类器 误差率最小化多次。 此外,分类器生成单元通过线性地耦合多个选择的弱分类器来生成用于检测检测对象的分类器。
    • 45. 发明授权
    • Information processing device and method, and program
    • 信息处理装置及方法,程序
    • US08755594B2
    • 2014-06-17
    • US13116412
    • 2011-05-26
    • Jun YokonoKohtaro Sabe
    • Jun YokonoKohtaro Sabe
    • G06K9/00
    • G06K9/6257G06K9/00791G06K2009/4666
    • An information processing device includes a first calculation unit which calculates a score of each sample image including a positive image in which an object as an identification object is present and a negative image in which the object as the identification object is not present, for each weak identifier of an identifier including a plurality of weak identifiers, a second calculation unit which calculates the number of scores when the negative image is processed, which are scores less than a minimum score among scores when the positive image is processed; and an realignment unit which realigns the weak identifiers in order from a weak identifier in which the number calculated by the second calculation unit is a maximum.
    • 一种信息处理设备,包括:第一计算单元,其计算包含其中存在作为识别对象的对象的正图像和不存在作为识别对象的对象的负图像的每个样本图像的得分,对于每个弱 标识符包括多个弱标识符的标识符;第二计算单元,当处理正图像时,计算负图像处理时的得分数,该分数小于分数中的最小得分; 以及重新排列单元,其从由第二计算单元计算的数量最大的弱识别符依次重新排列弱标识符。
    • 46. 发明授权
    • Image processing apparatus, image processing method, image recognition apparatus, and image recognition method
    • 图像处理装置,图像处理方法,图像识别装置和图像识别方法
    • US08411906B2
    • 2013-04-02
    • US11951437
    • 2007-12-06
    • Atsushi OkuboJun Yokono
    • Atsushi OkuboJun Yokono
    • G06K9/00
    • G06K9/00268G06K9/6231G06K9/6234
    • The present invention provides an information processing apparatus including combination generating means for getting a first feature quantity of N dimensions, N being an integer of at least two, from first information prepared for execution of learning and use the first feature quantity of N dimensions to generate at least two of a first feature quantity combination that are not greater than N dimensions of the first feature quantity; and learning processing executing means for computing a correlation coefficient between the plurality of first feature quantity combinations generated by the combination generating means and a learning model feature quantity matching each dimension of the plurality of first feature quantity combinations and, by use of the first correlation coefficient, classify the first information, thereby executing learning processing for classifying predetermined second information.
    • 本发明提供了一种信息处理装置,包括从用于执行学习的第一信息获得N维的第一特征量N,至少为2的整数的组合生成装置,并且使用N维的第一特征量来生成 不大于第一特征量的N维的第一特征量组合中的至少两个; 以及学习处理执行装置,用于计算由组合生成装置生成的多个第一特征量组合和与多个第一特征量组合中的每个维度相匹配的学习模型特征量之间的相关系数,并且通过使用第一相关系数 对第一信息进行分类,从而执行用于对预定的第二信息进行分类的学习处理。
    • 50. 发明授权
    • Memory system, memory method, and robotic apparatus
    • 存储系统,存储器方法和机器人装置
    • US07216112B2
    • 2007-05-08
    • US10388559
    • 2003-03-14
    • Shinya OhtaniJun Yokono
    • Shinya OhtaniJun Yokono
    • G06F1/00G06F3/00G06F15/18G06G7/00
    • G06N3/008B25J9/161G06N3/08
    • A memory system and a method as well as robotic apparatus are strong against noise and excellent in memory capacity, volume of calculation, quantity of physical memory, and memory responsiveness. It is designed to store, in the frame form, the first information on a symbol as well as the second information on a symbol supplied separately from a variety of inputs in relation to competitive neurons corresponding to the symbol in a way to strengthen the connection between relevant input neurons and competitive neurons in response to the input patterns of a variety of inputs for each symbol with the use of a competitive neural network having a set of input layers composed of a plurality of input neurons and a set of competitive layers composed of a plurality of competitive neurons.
    • 存储系统和方法以及机器人装置对于噪声和存储容量,计算量,物理存储量和记忆响应性都很强。 它被设计为以框架形式存储关于符号的第一信息以及关于与对应于该符号的竞争性神经元分开提供的各种输入分开提供的符号的第二信息,以加强第 响应于每个符号的各种输入的输入模式的相关输入神经元和竞争神经元,使用具有由多个输入神经元组成的一组输入层的竞争性神经网络和由一组由 多个竞争神经元。