Abstract:
A learning apparatus learns a machine learning model for performing semantic segmentation of determining a plurality of classes in an input image in units of pixels by extracting, for each layer, features which are included in the input image and have different frequency bands of spatial frequencies. A learning data analysis unit analyzes the frequency bands included in an annotation image of learning data. A learning method determination unit determines a learning method using the learning data based on an analysis result of the frequency bands by the learning data analysis unit. A learning unit learns the machine learning model via the determined learning method using the learning data.
Abstract:
Provided are an imaging device that make it possible to perform high-speed image capture of an observation target such as a cell, and to correct a shake caused by the movement of a stage or the like appropriately and simply. The imaging device includes a stage on which a vessel having an observation target received therein is installed, an imaging optical system that forms an image of the observation target, a horizontal driving unit that moves the stage in a main scanning direction and a sub-scanning direction orthogonal to the main scanning direction, and moves the stage forward and backward in the main scanning direction, an imaging unit that receives the image formed by the imaging optical system, and outputs an image signal of the observation target, and a shake correction unit that performs shake correction for correcting a shake caused by movement of the stage on the image signal.
Abstract:
There is provided an image processing apparatus including: a display control unit that performs a control for displaying a learning input image which is input, as learning data, to a segmentation model for performing semantic segmentation, which determines a plurality of classes in an image in units of pixels; a reception unit that receives, for each of a plurality of estimated regions which are estimated as different classes in the learning input image, an input of a marker having a size smaller than a size of the estimated region; a calculation unit that calculates feature quantities for each of a plurality of partitions in the learning input image; a classification unit that classifies a plurality of the feature quantities for each of the plurality of partitions into clusters for at least the number of the estimated regions; and a generation unit that generates an annotation candidate image in which a classification result of the clusters is reflected in the learning input image so as to be identified.
Abstract:
Provided is an information processing apparatus including an acquiring unit that acquires cell information indicating a state of a cell from production of a pluripotent stem cell to differentiation of the pluripotent stem cell into a specific differentiated cell by differentiation induction, and process history information indicating a history of a processing process for obtaining the differentiated cell, and a deriving unit that derives differentiation potency information indicating differentiation potency of the pluripotent stem cell based on the cell information and the process history information which are acquired by the acquiring unit.
Abstract:
Disclosed are an image processing device, a printing apparatus, an image processing method, and a program capable of making processing common among printing modes to simplify an entire image processing flow in image processing based on a plurality of printing modes with different definition. An image processing unit 14 includes an image size adjustment unit 20 which adjusts the size of an input image, and a halftone processing unit 24 which performs halftone processing on the input image size-adjusted by the image size adjustment unit 20 to generate a halftone image. The image size adjustment unit 20 adjusts the input image to the same size in two or more printing modes among a plurality of printing modes with different definition, and the input image of the same size is subjected to halftone processing in the halftone processing unit 24.
Abstract:
The method of manufacturing a conductive sheet of the present invention is provided with: a creation step for creating image data that indicates a meshed pattern; and an outputting step for outputting and forming wire materials on a base body on the basis of the created image data, and manufacturing a conductive sheet having the meshed pattern. The image data has, in convolution integration of a power spectrum of the image data and standard vision responsiveness of human beings, a characteristic of having each of the integration values at a spatial frequency band that is not less than 1/4 and not more than 1/2 of a Nyquist frequency corresponding to the image data to be greater than integration values at a null-space frequency.
Abstract:
At least two dot formers are provided for each of a plurality of colors. Dot formers, which form dots in a particular color, are disposed at a greater interval along a direction perpendicular to an array direction of dot forming elements than intervals along the direction perpendicular to the array direction at which other dot formers, which form dots in the remaining colors, are disposed.
Abstract:
An image processing apparatus includes an extraction unit that extracts, from among a plurality of designated regions in which labels of classes are designated, complicated regions which are regions of at least a part of the designated regions and are regions having relatively complicated contours, in an annotation image given as learning data to a machine learning model for performing semantic segmentation in which a plurality of the classes in an image are discriminated on a per-pixel basis, and a setting unit that sets additional labels for the complicated regions separately from original labels originally designated for the annotation image.
Abstract:
In a case where the operation program is started, a CPU of the mini-batch learning apparatus functions as a calculation unit, a specifying unit, and an update unit. The calculation unit calculates an area ratio of each of a plurality of classes in mini-batch data. The specifying unit specifies a rare class of which the area ratio is lower than a setting value. The update unit sets an update level of the machine learning model in a case where the rare class is specified by the specifying unit to be lower than an update level of the machine learning model in a case where the rare class is not specified by the specifying unit.
Abstract:
An evaluator 21 that evaluates states of cells included in each region of interest of a cell image, and a predictor 22 that performs, in advance, machine learning of a relationship between evaluation results for a specific region of interest within a first cell image obtained by imaging cells before a staining process and regions around the specific region of interest and staining states of cells of the specific region of interest within a second cell image obtained by imaging the same imaging targets as the cells of the first cell image after the staining process are provided. The predictor 22 predicts staining states of cells of a specific region of interest based on evaluation results for the specific region of interest and regions around the specific region of interest among the evaluation results for the third cell image of the cells before the staining process.