Build material handling unit for a powder module for an apparatus for additively manufacturing three-dimensional objects, which apparatus is adapted to successively layerwise selectively irradiate and consolidate layers of a build material which can be consolidated by means of an energy source, wherein the build material handling unit is coupled or can be coupled with a powder module, wherein the build material handling unit is adapted to level and/or compact a volume of build material arranged inside a powder chamber of the powder module by controlling the gas pressure inside the powder chamber.

In one embodiment, L dimensional images are trained, mapped, and aligned to an M dimensional topology to obtain azimuthal angles. The aligned L dimensional images are then trained and mapped to an N dimensional topology to obtain 2.sup.N vertex classifications. The azimuthal angles and the 2.sup.N vertex classifications are used to map L dimensional images into 0 dimensional images.

Visual observation of morphological features of a cell group or individual cells acquired in 3D image data is facilitated, thus improving observation accuracy. Provided is an image processing device that generates, on the basis of a plurality of 2D images acquired by a microscope at different focus positions on a cell clump, 3D images of respective cells constituting the cell clump, that processes the generated 3D images and analyzes feature amounts on the basis of at least one measurement parameter, that displays analysis results in a graph, that allows a user to select a region of interest on the displayed graph, and that generates, from the 3D images that correspond to the plurality of cells that are included in the selected region of interest, 2D display images each in a plane with reference to an axis that is determined on the basis of a shape feature of the corresponding cell and displays the 2D display images in a list.

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

In one embodiment, L dimensional images are trained, mapped, and aligned to an M dimensional topology to obtain azimuthal angles. The aligned L dimensional images are then trained and mapped to an N dimensional topology to obtain 2.sup.N vertex classifications. The azimuthal angles and the 2.sup.N vertex classifications are used to map L dimensional images into O dimensional images.

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.

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

A computer-implemented method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system. The DHM image comprises depictions of one or more cell objects and background. A reference image is generated based on the DHM image. This reference image may then be used to reconstruct a fringe pattern in the DHM image into an optical depth map.

In one embodiment, L dimensional images are trained, mapped, and aligned to an M dimensional topology to obtain azimuthal angles. The aligned L dimensional images are then trained and mapped to an N dimensional topology to obtain 2.sup.N vertex classifications. The azimuthal angles and the 2.sup.N vertex classifications are used to map L dimensional images into 0 dimensional images.

In one embodiment, L dimensional images are trained, mapped, and aligned to an M dimensional topology to obtain azimuthal angles. The aligned L dimensional images are then trained and mapped to an N dimensional topology to obtain 2.sup.N vertex classifications. The azimuthal angles and the 2.sup.N vertex classifications are used to map L dimensional images into 0 dimensional images.