公开(公告)号：US20240160196A1

公开(公告)日：2024-05-16

申请号：US18283411

申请日：2022-03-25

Applicant: Panasonic Intellectual Property Management Co., Ltd.

Inventor： Yao ZHOU ,  Athul M. MATHEW ,  Ariel BECK ,  Chandra Suwandi WIJAYA ,  Nway Nway AUNG ,  Khai Jun KEK ,  Yuya SUGASAWA ,  Jeffry FERNANDO ,  Yoshinori SATOU ,  Hisaji MURATA

IPC: G05B19/418 ,  G05B13/02

CPC classification number: G05B19/41875 ,  G05B13/0265 ,  G05B2219/32368

Abstract: First, a plurality of models that predict categories of input data are pooled. At least one of the plurality of models is a model trained by machine learning. Next, each of a plurality of hybrid model candidates that judge the categories are created by selecting and combining two or more models from among the plurality of pooled models. Then, by comparing the plurality of hybrid model candidates, one of the plurality of hybrid model candidates is selected as a hybrid model.

公开(公告)号：US20220253995A1

公开(公告)日：2022-08-11

申请号：US17173822

申请日：2021-02-11

Applicant: PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO., LTD.

Inventor： Ariel BECK ,  Chandra Suwandi WIJAYA ,  Athul M. MATHEW ,  Nway Nway AUNG ,  Ramdas KRISHNAKUMAR ,  Zong Sheng TANG ,  Yao ZHOU ,  Pradeep RAJAGOPALAN ,  Yuya SUGASAWA

IPC: G06T7/00 ,  G06K9/62 ,  G06K9/46

Abstract: A method and system for checking data gathering conditions or image capturing conditions associated with images during AI based visual-inspection process. The method comprises generating a first representative (FR1) image for a first group of images and a second representative image (FR2) for a second group of images. A difference image data is generated between FR1 image and the FR2 image based on calculating difference between luminance values of pixels with same coordinate values. Thereafter, one or more of a plurality of white pixels or intensity-values are determined within the difference image based on acquiring difference image data formed of luminance difference-values of pixels. An index representing difference of data-capturing conditions across the FR1 image and the FR2 image is determined, said index having been determined at least based on the plurality of white pixels or intensity-values, for example, based on application of a plurality of AI or ML techniques.

公开(公告)号：US20210294488A1

公开(公告)日：2021-09-23

申请号：US16824467

申请日：2020-03-19

Applicant: PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO., LTD.

Inventor： Vasileios VONIKAKIS ,  Yao ZHOU ,  Chandra Suwandi WIJAYA ,  Ariel BECK

IPC: G06F3/0484 ,  G06T7/00

Abstract: A method implemented in a computing-device with a display screen for image inspection. The method comprises displaying a distribution of a quality-indicia of at least one object in each of a plurality of images to be inspected, within a first area of the display screen. Within a second area of the display screen, a user-control is displayed to adjust a threshold-value with respect to an acceptance of at least one object in said plurality of images to be inspected. The threshold-value may be determined manually or automatically. A change in or update of threshold value is determined based on a user-operation performed over the user-control for adjusting the threshold value. Thereafter, a quality-indicia of at least one object in each the plurality of images is determined. Acceptable objects in respect of an image inspection procedure based on the updated threshold value and the determined quality-indicia are indicated.

公开(公告)号：US20240185576A1

公开(公告)日：2024-06-06

申请号：US18284794

申请日：2022-03-14

Applicant: Panasonic Intellectual Property Management Co., Ltd.

Inventor： Yuya SUGASAWA ,  Yoshinori SATOU ,  Hisaji MURATA ,  Jeffery FERNANDO ,  Yao ZHOU ,  Nway Nway AUNG

IPC: G06V10/774 ,  G06V10/70 ,  G06V10/80

CPC classification number: G06V10/774 ,  G06V10/809 ,  G06V10/87

Abstract: An image determination device according to the present disclosure includes: a trainer that obtains one or more first models by training machine learning models of one or more types with use of a first training data set including first images and first labels, and obtains one or more second models by training machine learning models of one or more types with use of one or more second training data sets each including second images different from the first images, second labels, and at least part of the first training data set; an image obtainer that obtains a target image; and a determiner that outputs a determination result of a label of the target image obtained by the image obtainer, which is obtained by using, for the target image, at least two models including one of the one or more first models and one of the one or more second models.