Seiko Epson Corporation

Hikaru Kurasawa

Ryoki Watanabe

Naoki Kuwata

Masashi Kanai

Hideto Ishiguro

Tsukasa Eguchi

Yoshifumi Arai

Teruyuki Nishimura

Yoshihiro Oshita

A method of making a single processor or a plurality of processors perform classification processing of classification target data using a machine learning model includes the steps of (a) preparing N machine learning models in a memory assuming N as an integer no smaller than 2, and (b) performing the classification processing of the classification target data using the N machine learning models. Each of the N machine learning models is configured so as to classify input data into any of a plurality of classes with learning using training data, and is configured so as to have at least one class different from a class of another of the N machine learning models.

Method of executing classification process using machine learning model, information processing device, and non-transitory computer-readable medium storing computer program

Provided is a determination method that includes obtaining measurement data, selecting one or more second wavelengths from a plurality of first wavelengths including at least one of a plurality of measurement wavelengths to generate a plurality of individuals, by using a genetic algorithm, inputting, to a first model learned to reproduce a correct answer label of a target object, the measurement data of the target object belonging to a remaining group and a second spectroscopic spectrum determined by the second wavelength to discriminate a label of the target object belonging to the remaining group, for each of the plurality of individuals, and determining whether or not to use the second wavelength as the wavelength of the spectroscopic spectrum for discrimination based on a rate at which the label is correctly discriminated.

Determination method

An object detection method includes inputting an input image to a learned machine learning model and generating a similarity image from an output of at least one specific layer, and generating a discriminant image to which at least an unknown label is assigned, by comparing a similarity of each pixel in the similarity image to a predetermined threshold value.

Object detection method, object detection apparatus, and non-transitory computer-readable storage medium storing computer program

A quality determination method includes: (a) generating a plurality of pieces of training data by classifying a plurality of pieces of non-defective product data into a plurality of classes; (b) executing learning of a machine learning model using the plurality of pieces of training data; (c) preparing a known feature spectrum group; and (d) executing quality determination processing of inspection data using the machine learning model and the known feature spectrum group. The (d) includes (d1) calculating a feature spectrum related to the inspection data, (d2) calculating a similarity between the feature spectrum and the known feature spectrum group, and (d3) determining the inspection data to be non-defective when the similarity is equal to or greater than a threshold value and determining the inspection data to be defective when the similarity is less than the threshold value.

Method for determining quality of inspection data using machine learning model, information processing apparatus, and non-transitory computer readable storage medium storing computer program

An image processing method includes: an image pickup step of picking up an RGB image of a target object to be picked up, and picking up a spectroscopic image of the target object in a predetermined wavelength range and thus acquiring spectroscopic information peculiar to the target object in the wavelength range; and a display step of displaying a complemented image complemented by superimposing the spectroscopic information on the RGB image.

Image processing method, image processing device, and information system

An identification method for identifying a target to be measured includes accepting, from an input section, an information representing a condition for acquiring spectral information specific to a target to be measured, capturing, by a spectrometry camera, an image of the target, acquiring the spectral information specific to the target based on the captured image, and identifying the target based on (i) the spectral information and (ii) a database, stored in a memory, containing a plurality of pieces of spectral information corresponding to a plurality of objects. Acquiring the spectral information includes preferentially acquiring the spectral information specific to the target in a specific wavelength region where the target is identifiable.

Information system and identification method

A calibration data acquisition unit (a) acquires Q optical spectra, S evaluation spectra, and a reference spectrum of a target component, (b) extracts R subsets from a set of the optical spectra, (c) performs independent component analysis in which component amounts are treated as independent components on each subset so as to acquire component natural spectra and component calibration spectra, (d) obtains an inner product value between the component calibration spectrum and the evaluation spectrum, (e) obtains a correlation degree between a component amount for the target component and the inner product value with respect to each component calibration spectrum, (f) obtains a similarity between each component natural spectrum and a reference spectrum, (g) selects a component calibration spectrum causing a comprehensive evaluation value based on the correlation degree and the similarity to be greatest as the target component calibration spectrum, and (h) creates a calibration curve.

Calibration apparatus and calibration curve creation method

A spectroscopic system includes a main body including a light source that radiates light to a light transmissive measurement target, an imaging device that captures an image based on transmitted light having passed through the measurement target, and a spectroscopy section that is provided in an optical path between the light source and the imaging device and selectively transmits light that belongs to a specific wavelength region, and an attachment that includes an optical path changer that changes the direction of the optical path of the light outputted from the light source and is so attached to the main body as to form a placement space which is located between the optical path changer and the main body and in which the measurement target is placed.

Spectroscopic system

A calibration data acquisition unit (a) acquires Q optical spectra and S evaluation spectra, (b) extracts R subsets from a set of the Q optical spectra, (c) performs independent component analysis in which component amounts in each sample treated as independent components on each of R subsets so as to acquire R&#xd7;N component calibration spectra, (d) obtains an inner product value between the R&#xd7;N component calibration spectrum and an evaluation spectrum, (e) selects a component calibration spectrum for which a correlation degree between a component amount for the target component and the inner product value is the maximum as the target component calibration spectrum from among the R&#xd7;N component calibration spectra, and (f) creates a calibration curve by using the target component calibration spectrum.

Calibration apparatus, calibration curve creation method, and independent component analysis method

A calibration data acquisition unit uses an equation X=YW in which an optical spectrum matrix X is the same as a product between a component natural spectrum matrix Y and a component amount matrix X, performs independent component analysis in which column vectors of the component amount matrix W are treated as independent components so as to determine the component natural spectrum matrix Y, and employs row vectors of a general inverse matrix Yof the component natural spectrum matrix Y as component calibration spectra corresponding to a plurality of components. A calibration curve is created by using a target component calibration spectrum corresponding to a target component.

A calibration curve creating method includes: (a) acquiring observation data of a plurality of samples of a test object; (b) acquiring content of a target component of each sample; (c) estimating a plurality of independent components when the observation data of each sample is separated into the plurality of independent components, and acquiring a mixing coefficient corresponding to the target component for each sample; and (d) acquiring a regression equation of a calibration curve. (c) includes acquiring an independent component matrix by performing a first preprocessing including normalization of the observation data, a second preprocessing including whitening, and an independent component analysis process in this order. &#x3b2; divergence is used as an independence index of the independent component analysis process, and a robust regression method is used in (d).

Growing community of inventors

Shiojiri, Japan

Hikaru Kurasawa