Hitachi High-tech Corporation

Hitachi-high-technologies Corporation

Takefumi Kakinuma

Yasutaka Toyoda

Masayoshi Ishikawa

Fumihiro Bekku

Shinichi Shinoda

Masaki Hasegawa

Kazuyuki Hirao

Shuyang Dou

Ayana Yamamoto

Kentaro Ohira

Toshinori Yamauchi

Sota Komatsu

According to the present invention, an image recognition system calculates importance of a feature for each target shape recognized in an image and for each type of feature, and determines correctness of a recognition result by comparing the importance with a statistic for each type of feature, for each target shape.

Image recognition system

The objective of the present invention is to provide an image classification device and a method therefor with which suitable teaching data can be created. An image classification device that carries out image classification using images which are in a class to be classified and include teaching information, and images which are in a class not to be classified and to which teaching information has not been assigned, said image classification device being characterized by being provided with: an image group input unit for receiving inputs of an image group belonging to a class to be classified and an image group belonging to a class not to be classified; and a subclassification unit for extracting a feature amount for each image in an image group, clustering the feature amounts of the images in the image group belonging to a class not to be classified, and thereby dividing the images into sub-classes.

Image classification device and method

The purpose of the present invention is to provide an image evaluation device and method which can detect unknown defects and which can prevent misrecognition by a machine learning model. This image evaluation device, which uses a machine learning classifier to classify defect information in a defect image of an electronic device, is characterized by being provided with: an image storage unit which stores a defect image of an electronic device; a defect region storage unit which stores defect region information that is in the defect image; a classifier which classifies the defect information with machine learning; an image extraction unit which, in the course of the defect image classification processing, extracts image-of-interest information which the classifier will focus on; and an evaluation unit which compares the image-of-interest information and the defect region information to evaluate the classifiability of the defect image.

Image evaluation apparatus and image evaluation method

Proposed is a charged particle beam device including an arithmetic processing unit that generates an image of a sample, based on a detection signal that is detected based on irradiation to the sample with a charged particle beam emitted from a charged particle source. The arithmetic processing unit searches a second image as a search target image with use of a first image as a template, and when a region corresponding to the first image is not detected in the second image, the arithmetic processing unit searches a third image that represents a region larger than a region displayed in the second image, with use of a second template.

Charged particle beam device

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Tokyo, Japan

Takefumi Kakinuma