Panasonic Intellectual Property Management Co., Ltd.

Yuya Sugasawa

Hideto Motomura

Jeffry Fernando

Hisaji Murata

Hideyuki Arai

Ariel Beck

Chandra Suwandi Wijaya

Nway Nway Aung

Hisashi Aikawa

Athul M Mathew

Zong Sheng Tang

Yao Zhou

Ramdas Krishnakumar

Pradeep Rajagopalan

Even when data that can belong to a new class that is not in an existing class is input, this data can be easily classified appropriately. Classification system includes input reception part, classification part, calculation part, determination part, and presentation part. Input reception part receives an input of target data. Classification part classifies the target data into any one of a plurality of classes. Calculation part calculates a feature amount of the target data. Determination part determines a possibility that the target data is classified into the new class based on a classification result in classification part and the feature amount of the target data calculated by calculation part. When determination part determines that there is a possibility that the target data is classified into the new class, presentation part presents a determination result of determination part.

Classification system and classification method

Reliability regarding a class determination for an object is improved. Classification system includes first classification part, second classification part, and determination part. First classification part classifies first target data into at least one of a plurality of first classes. Second classification part classifies second target data into at least one of a plurality of second classes. Determination part decides whether to use one or both of a first classification result that is a classification result obtained by first classification part and a second classification result that is a classification result obtained by second classification part, and determines a class of object based on one or both of them. The first target data is image data of object. The second target data is manufacturing data regarding a manufacturing condition of object.

Classification system and classification method using lerned model and model including data distribution

A learning device includes a camera configured to acquire image data by imaging a sample of a product, a physical property information acquisition unit configured to acquire physical property information of the sample, and a processing unit configured to generate a learning model. The processing unit is configured to identify a category of the sample based on rule information relating the physical property information to the category, to generate teacher data by relating the identified category to the image data, and to generate a learning model by machine learning using the teacher data. The learning model outputs the category of the sample in response to an input of the image data of the sample.

Learning device, inspection device, learning method, and inspection method

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 (FR) image for a first group of images and a second representative image (FR) for a second group of images. A difference image data is generated between FRimage and the FRimage 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 FRimage and the FRimage 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.

Method and system for checking data gathering conditions associated with image-data during AI enabled visual-inspection process

Growing community of inventors

Osaka, Japan

Yuya Sugasawa