公开(公告)号：US20160070986A1

公开(公告)日：2016-03-10

申请号：US14477215

申请日：2014-09-04

Applicant: Xerox Corporation

Inventor： Boris Chidlovskii ,  Gabriela Csurka

IPC: G06K9/62 ,  G06K9/00

CPC classification number: G06K9/6259 ,  G06K9/00624 ,  G06K9/00771 ,  G06K9/00785 ,  G06K9/46 ,  G06K9/62 ,  G06K9/6217 ,  G06K9/6256 ,  G06K9/6257 ,  G06K9/6267 ,  G06K9/627 ,  G06K9/6288

Abstract: In camera-based object labeling, boost classifier ƒT(x)=Σr=1Mβrhr(x) is trained to classify an image represented by feature vector x using a target domain training set DT of labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets DS1, . . . , DSN acquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers hr(x) and weights βr. The rth iteration of the AdaBoost algorithm trains candidate base classifiers hrk(x) each trained on a training set DT∪DSk, and selects hr(x) from previously trained candidate base classifiers. The target domain training set DT may be expanded based on a prior estimate of the labels distribution for the target domain. The object labeling system may be a vehicle identification system, a machine vision article inspection system, or so forth.

Abstract translation: 在基于相机的对象标签中，训练增强分类器ƒT（x）=＆Sgr; r = 1M＆bgr; rhr（x）以使用特征向量x表示的图像来分类由特征向量x表示的图像的标记特征向量的目标域训练集DT， 相同的摄像机和多个源域训练集DS1，。 。 。 ，其他相机收购的DSN。 训练采用自适应增强（AdaBoost）算法生成基本分类器hr（x）和权重bgr; r。 AdaBoost算法的第r次迭代训练在训练集DT∪DSK上训练的候选基类分类器hrk（x），并从先前训练过的候选基类分类器中选择hr（x）。 可以基于目标域的标签分布的先前估计来扩展目标域训练集DT。 物体标识系统可以是车辆识别系统，机器视觉物品检查系统等。

公开(公告)号：US20140288982A1

公开(公告)日：2014-09-25

申请号：US13847172

申请日：2013-03-19

Applicant: XEROX CORPORATION

Inventor： Boris Chidlovskii

IPC: G06Q10/02

CPC classification number: G06Q10/025 ,  G06Q10/04 ,  G06Q50/30

Abstract: Methods and systems for matching real trips to schedules in a public transportation system. Inputs can be reduced to a two-dimensional sequence alignment of data indicative of a temporal series of arrival and departure timestamps. A dynamic programming solution is applied to the two-dimensional sequence alignment of the data. Then, symmetric and asymmetric cases are analyzed with respect to the two-dimensional sequence alignment of the data to thereby match real trip data to schedule data in the public transportation system based on the temporal series of the arrival and departure timestamps.

Abstract translation: 将实际旅行与公共交通系统中的时间表进行匹配的方法和系统。 输入可以减少到表示到达和离开时间戳的时间序列的数据的二维序列比对。 将动态规划解决方案应用于数据的二维序列对齐。 然后，相对于数据的二维序列对齐来分析对称和非对称情况，从而基于实时旅程数据，基于到达和离开时间戳的时间序列来规划公共交通系统中的数据。