Techniques are described for dynamically correcting image distortion during imaging of a patterned sample having repeating spots. Different sets of image distortion correction coefficients may be calculated for different regions of a sample during a first imaging cycle of a multicycle imaging run and subsequently applied in real time to image data generated during subsequent cycles. In one implementation, image distortion correction coefficients may be calculated for an image of a patterned sample having repeated spots by: estimating an affine transform of the image; sharpening the image; and iteratively searching for an optimal set of distortion correction coefficients for the sharpened image, where iteratively searching for the optimal set of distortion correction coefficients for the sharpened image includes calculating a mean chastity for spot locations in the image, and where the estimated affine transform is applied during each iteration of the search.

An image processing device 100 includes, in a case where designation of a plurality of partial regions corresponding to a cell morphology is received, the plurality of partial regions being extracted from a pathological image, a generation unit 154 that generates auxiliary information indicating information about a feature amount effective when a plurality of partial regions is classified or extracted with respect to a plurality of feature amounts calculated from the image; and in a case where setting information about an adjustment item according to the auxiliary information is received, an image processing unit 155 that performs an image process on the image using the setting information.

A system and method for reducing the responsibility of the user significantly by applying an optical system that can identify container like sample tubes with respect to their characteristics, e.g., shapes and inner dimensions, from their visual properties by capturing images from a rack comprising container and processing said images for reliably identifying a container tyle.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Condensation associated with the collection and identification of airborne particles is detected. Upon the detection, one or more condensation countermeasures are triggered to address the condensation.

A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the image into a region of interest that includes information useful for classification and a background region by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.

Disclosed herein include embodiments of a system, a device, and a method for sorting a plurality cells of a sample. A plurality of raw images comprising pixels of complex values in a frequency space can be generated from a plurality of channels of fluorescence intensity data of fluorescence emissions of fluorophores, the fluorescence emissions being elicited by fluorescence imaging using radiofrequency-multiplexed excitation in a temporal space. Spectral unmixing can be performed on the raw images prior to a sorting decision being made.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

The present disclosure relates to method and apparatus for processing cross matching image based on deep learning.

Embodiments of this invention provide a measurement map configuration method and apparatus. A wafer to be inspected is provided. The wafer includes a plurality of inspection marks. A first inspection result is obtained based on a first set of inspection marks. A second set of inspection marks is selected based on a preset rule. The second set of inspection marks is less than the first set of inspection marks. A second inspection result is obtained based on the second set of inspection marks. If an overlay accuracy of the second inspection result matches an overlay accuracy the first inspection result, a measurement map for the wafer is set based on target inspection marks. The target inspection marks are the second set of inspection marks of the measurement map.