公开(公告)号：US11636268B2

公开(公告)日：2023-04-25

申请号：US16879252

申请日：2020-05-20

Applicant: FUJITSU LIMITED

Inventor： Lu Fang ,  Zhongguang Zheng ,  Yingju Xia ,  Jun Sun

IPC: G06F40/289 ,  G06N5/04 ,  G06N20/00

Abstract: The present disclosure relates to a method and device for generating a finite state automata for recognizing a chemical name in a text, and a recognition method. According to an embodiment of the present disclosure, the method comprises substituting representation constants of categories of character segments appearing in an organic compound name set into the organic compound name set to obtain a conversion name set; updating the conversion name set based on a conversion name segment which repeatedly appears in the conversion name set; and generating the finite state automata based on the updated conversion name set.

公开(公告)号：US11556735B2

公开(公告)日：2023-01-17

申请号：US16868860

申请日：2020-05-07

Applicant: FUJITSU LIMITED

Inventor： Chaoliang Zhong ,  Wensheng Xia ,  Ziqiang Shi ,  Jun Sun

IPC: G06K9/62 ,  G06N20/00

Abstract: A training device and a training method for training a multi-goal model based on goals in a goal space are provided. The training device includes a memory and a processor coupled to the memory. The processor is configured to set the goal space, to acquire a plurality of sub-goal spaces of different levels of difficulty; change a sub-goal space to be processed from a current sub-goal space to a next sub-goal space of a higher level of difficulty; select, as sampling goals, goals at least from the current sub-goal space, and to acquire transitions related to the sampling goals by executing actions; train the multi-goal model based on the transitions, and evaluate the multi-goal model by calculating a success rate for achieving goals in the current sub-goal space.

公开(公告)号：US20210073470A1

公开(公告)日：2021-03-11

申请号：US16879252

申请日：2020-05-20

Applicant: FUJITSU LIMITED

Inventor： Lu Fang ,  Zhongguang Zheng ,  Yingju Xia ,  Jun Sun

IPC: G06F40/289 ,  G06N20/00 ,  G06N5/04

Abstract: The present disclosure relates to a method and device for generating a finite state automata for recognizing a chemical name in a text, and a recognition method. According to an embodiment of the present disclosure, the method comprises substituting representation constants of categories of character segments appearing in an organic compound name set into the organic compound name set to obtain a conversion name set; updating the conversion name set based on a conversion name segment which repeatedly appears in the conversion name set; and generating the finite state automata based on the updated conversion name set.

公开(公告)号：US10296813B2

公开(公告)日：2019-05-21

申请号：US15254249

申请日：2016-09-01

Applicant: FUJITSU LIMITED

Inventor： Li Chen ,  Song Wang ,  Wei Fan ,  Jun Sun ,  Naoi Satoshi

IPC: G06K9/62 ,  G06T5/20 ,  G06N3/08

Abstract: A training method and a training apparatus for a neutral network for image recognition are provided. The method includes: representing a sample image as a point set in a high-dimensional space, a size of the high-dimensional space being a size of space domain of the sample image multiplied by a size of intensity domain of the sample image; generating a first random perturbation matrix having a same size as the high-dimensional space; smoothing the first random perturbation matrix; perturbing the point set in the high-dimensional space using the smoothed first random perturbation matrix to obtain a perturbed point set; and training the neutral network using the perturbed point set as a new sample. With the training method and the training apparatus, classification performance of a conventional convolutional neural network is improved, thereby generating more training samples, reducing influence of overfitting, and enhancing generalization performance of the convolutional neural network.

公开(公告)号：US09256782B2

公开(公告)日：2016-02-09

申请号：US14613710

申请日：2015-02-04

Applicant: FUJITSU LIMITED

Inventor： Pan Pan ,  Yuan He ,  Jun Sun

IPC: H04N1/407 ,  H04N1/409 ,  G06T5/00 ,  G06K9/00

CPC classification number: G06K9/00442 ,  G06K9/2027 ,  G06T5/00 ,  G06T5/008 ,  H04N1/407 ,  H04N1/4072

Abstract: An apparatus for and a method of processing a document image are provided. The method comprises: generating a luminance component image from the document image; estimating a luminance image from the luminance component image; and adjusting the luminance component image according to the estimated luminance image. Luminance values of pixels at least in horizontal edge areas of the luminance component image are estimated according to luminance values of pixels in a part of background of the luminance component image. If the estimated luminance values are acceptable according to a predetermined criterion, the luminance image is estimated according to the estimated luminance values. If the estimated luminance values are unacceptable, the luminance image is estimated by using the largest one of the luminance values of the pixels in each column of pixels in the luminance component image as the luminance values of all of the pixels in the column.

Abstract translation: 提供了一种用于处理文档图像的装置和方法。 该方法包括：从文档图像生成亮度分量图像; 从亮度分量图像估计亮度图像; 以及根据所估计的亮度图像来调整亮度分量图像。 至少在亮度分量图像的水平边缘区域中的像素的亮度值根据亮度分量图像的背景的一部分中的像素的亮度值来估计。 如果根据预定标准估计的亮度值是可接受的，则根据估计的亮度值来估计亮度图像。 如果估计的亮度值不可接受，则通过使用亮度分量图像中的每列像素中的像素的亮度值中的最大的一个来估计亮度图像作为列中所有像素的亮度值。

公开(公告)号：US09202282B2

公开(公告)日：2015-12-01

申请号：US13906001

申请日：2013-05-30

Applicant: FUJITSU LIMITED

Inventor： Yuan He ,  Jun Sun ,  Satoshi Naoi

IPC: G06T7/00 ,  G06T5/00 ,  G06T5/20

CPC classification number: G06T7/0085 ,  G06T5/003 ,  G06T5/20 ,  G06T7/12 ,  G06T7/181 ,  G06T2207/10008 ,  G06T2207/20192 ,  G06T2207/30176

Abstract: The present invention discloses a boundary extraction method and apparatus, the method including: a gradient estimation step of estimating a gradient of each pixel in a captured image; a gradient adjustment step of adjusting, by enhancing a gradient of a target boundary of an object contained in the captured image and weakening a gradient of a noise boundary, the estimated gradient, so that the adjusted gradient is considered as a current gradient; and a boundary extraction step of extracting a boundary of the object based on the current gradient. According to the embodiments of the invention, in a case of using a non-contact imaging device to capture an image, it is possible to more accurately extract a boundary of an object contained in the captured image.

Abstract translation: 本发明公开了一种边界提取方法和装置，该方法包括：估计拍摄图像中每个像素的梯度的梯度估计步骤; 梯度调整步骤，通过增强包含在所捕获的图像中的对象的目标边界的梯度并减弱噪声边界的梯度来估计所述估计的梯度，从而将所述调整的梯度视为当前梯度; 以及边界提取步骤，基于当前梯度提取对象的边界。 根据本发明的实施例，在使用非接触式成像装置拍摄图像的情况下，可以更准确地提取包含在拍摄图像中的对象的边界。

公开(公告)号：US20130330009A1

公开(公告)日：2013-12-12

申请号：US13912805

申请日：2013-06-07

Applicant: FUJITSU LIMITED

Inventor： Pan PAN ,  Yuan He ,  Jun Sun ,  Satoshi Naoi

IPC: G06T7/00

CPC classification number: G06T7/0085 ,  G06T7/12

Abstract: The invention provides an apparatus and method for extracting a boundary of an object in an image and an electronic device. The apparatus includes: a position determining unit, configured to determine a start point and an end point of a boundary of an object in an image and to determine a position of a reference point relevant to the start point and the end point; a first direction determining unit, configured to determine a first direction of the boundary; a gradient map obtaining unit, configured to obtain a gradient map of a first region; a gradient attenuating unit, configured to attenuate in the gradient map the gradients of a second region; and an extracting unit, configured to extract a boundary of an object. The technology of the invention can improve the accuracy of boundary extracting, and can be applied in the field of image processing.

Abstract translation: 本发明提供一种用于提取图像和电子设备中的对象的边界的装置和方法。 该装置包括：位置确定单元，被配置为确定图像中的对象的边界的起始点和终点，并确定与开始点和终点相关的参考点的位置; 第一方向确定单元，被配置为确定所述边界的第一方向; 梯度图获取单元，被配置为获得第一区域的梯度图; 梯度衰减单元，被配置为在梯度图中衰减映射第二区域的梯度; 以及提取单元，被配置为提取对象的边界。 本发明的技术可以提高边界提取的准确性，可以应用于图像处理领域。

公开(公告)号：US11409885B2

公开(公告)日：2022-08-09

申请号：US16452020

申请日：2019-06-25

Applicant: FUJITSU LIMITED

Inventor： Shenbin Zhang ,  Jun Sun ,  Bingfeng Pi

IPC: G06F21/57 ,  G06F9/46 ,  G06F9/48 ,  G06F9/52

Abstract: Disclosed are a method and apparatus for processing information by cooperation of multiple subjects, by submitting an information processing task to first multiple subjects; performing security analysis on information processing results obtained by executing the information processing task by the first multiple subjects, determining an updated processing manner of executing the information processing task based on a result of the security analysis; and submitting the information processing task to second multiple subjects according to the determined updated processing manner. Information processing results of executing the information processing task by the second multiple subjects are obtained, each of the second multiple subjects and each of the first multiple subjects save same information associated with the information processing task, and the information is updated based on the information processing results of executing the information processing task by the second multiple subjects.

公开(公告)号：US11210561B2

公开(公告)日：2021-12-28

申请号：US16718720

申请日：2019-12-18

Applicant: FUJITSU LIMITED

Inventor： Rong Zhou ,  Liuan Wang ,  Jun Sun

IPC: G06K9/62 ,  G06T3/40

Abstract: A device and a method for electromagnetic field simulation are provided. The image processing device is to obtain, with a first resolution, a first electromagnetic field simulation result of a simulation region, and to derive a second electromagnetic field simulation result with a second resolution, according to the first electromagnetic field simulation result, using a model trained based on a deep learning method. The second resolution is higher than the first resolution.

公开(公告)号：US11144817B2

公开(公告)日：2021-10-12

申请号：US15799533

申请日：2017-10-31

Applicant: FUJITSU LIMITED

Inventor： Li Sun ,  Song Wang ,  Wei Fan ,  Jun Sun

IPC: G06N3/04 ,  G06N3/08

Abstract: A device for determining a CNN model for a database according to the present disclosure includes: a selecting unit configured to select at least two CNN models from multiple CNN models whose classification capacity is known; a fitting unit configured to fit, based on the classification capacity and first parameters of the at least two CNN models, a curve taking classification capacity and a first parameter as variables; a predicting unit configured to predict, based on the curve, a first parameter of other CNN models; and a determining unit configured to determine a CNN model applicable to the database from the multiple CNN models. With the device and the method according to the present disclosure, there is no need to train all the CNN models, thereby greatly reducing the amount of computation and simplifying the process of designing the CNN model.