Abstract: In an object detection model training method, a classifier that has been trained in a first phase is duplicated to at least two copies, and in a training in a second phase, each classifier obtained through duplication is configured to detect to-be-detected objects with different sizes, and train an object detection model based on a detection result.

Abstract: There is provided an image processing device to which a visible light image and an infrared ray image are input, the visible light image having been obtained corresponding to irradiation of a visible light in a second direction, the infrared ray image having been obtained corresponding to irradiation of an infrared ray in a first direction, which is a direction having an irradiation range larger than an irradiation range of the second direction, and which synthesizes the infrared ray image and the visible light image to generate a color image.

Abstract: A specimen analysis system includes at least one processor to receive image information that represents a respective plurality of pixels of an image of each of a plurality of testing areas to indicate the presence or absence of a test subject compound; to determine for each testing area a number of pixels that indicates either the presence or the absence of the test subject compound, if the number of pixels indicating positive for each of a plurality of testing areas equals or exceeds a first minimum threshold value indicating that the testing area is positive for either the presence or absence of the test subject compound, totaling all testing areas indicating positive for either the presence or absence of the test subject compound, and if the total number of the testing areas indicating positive for either the presence or absence of the test subject compound equals or exceeds a second minimum threshold value indicating an overall positive test result for either the presence or absence of the test subject co

Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

Abstract: A blood sample processor for imaging a centrifuged blood sample is provided including a transparent container with the centrifuged blood sample therein. An illumination source is positioned to illuminate the centrifuged blood sample at a non-right angle to the transparent container. A digital camera disposed opposite the transparent container images the centrifuged blood sample and the image is processed to determine the relative locations of component layers of the centrifuged blood sample.

Abstract: The feature extraction device according to one aspect of the present disclosure comprises: a reliability determination unit that determines a degree of reliability with respect to a second region, which is a region that has been extracted as a foreground region of an image and is within a first region that has been extracted from the image as a partial region containing a recognition subject, said degree of reliability indicating the likelihood of being the recognition subject; a feature determination unit that, on the basis of the degree of reliability, uses a first feature which is a feature extracted from the first region and a second feature which is a feature extracted from the second region to determine a feature of the recognition subject; and an output unit that outputs information indicating the determined feature of the recognition subject.

Abstract: A keypoint detection system includes: a camera system including at least one camera; and a processor and memory, the processor and memory being configured to: receive an image captured by the camera system; compute a plurality of keypoints in the image using a convolutional neural network including: a first layer implementing a first convolutional kernel; a second layer implementing a second convolutional kernel; an output layer; and a plurality of connections between the first layer and the second layer and between the second layer and the output layer, each of the connections having a corresponding weight stored in the memory; and output the plurality of keypoints of the image computed by the convolutional neural network.

Abstract: A notification control unit includes a notification setting information selection unit, a notification setting information storage unit, a notification setting information setting unit, and the notification execution unit. Notification setting information is associated with individual setting information to be preserved in the notification setting information storage unit. The notification execution unit performs a notification according to setting of use-notification setting information which is selected by using the individual setting information by the notification setting information selection unit. The created or changed notification setting information is preserved in the notification setting information storage unit.

Abstract: Provided is an in-vehicle stereo camera which enables the continuation of automated driving if failures occur during imaging. A pair of captured images 301, 302, which is captured by a pair of imaging units so that both contain an overlapping region, are acquired (S201), it is determined whether an abnormal region is present in at least one of the pair of captured images, and if an abnormal region is present, the degree of impact from the abnormal region on an object recognition process of an object recognition unit is diagnosed (S202), and the processing content of the object recognition process is updated according to the degree of impact (S203).

Abstract: A method for detecting a tumor from images which are required to be shrunken in resolution obtains one or more first images. Then, the method segments or divides the detection images into a number of detection image blocks according to an input size of training data of a convolutional neural network architecture, before segmenting, each of the plurality of detection image blocks comprising coordinate values. The detection image blocks are input into a preset tumor detection model to generate image blocks of a result of the detection images. The method merges the image blocks into a single image according to the coordinate values of each detection image block. Colors of normal areas, abnormal areas, and overlapping areas of the abnormal areas are all different. The method generates a final detection according to color depths in the image. A tumor detection device and a non-transitory storage medium are provided.

Abstract: A device may receive a target document. The device may segment the target document into multiple segments. The device may determine, for each segment of the multiple segments, a set of color parameters for a corresponding set of pixels included in that segment. The device may determine, for each segment of the multiple segments, an average color parameter for that segment based on the set of color parameters for the corresponding set of pixels included in that segment. The device may generate a target color profile for the target document based on determining the average color parameter for each segment. The device may compare the target color profile and a model color profile associated with classifying the target document. The device may classify the target document based on comparing the target color profile and the model color profile.

Abstract: A method is for obtaining at least one feature of interest, especially a biomarker, from an input image acquired by a medical imaging device. The at least one feature of interest is the output of a respective node of a machine learning network, in particular a deep learning network. The machine learning network processes at least part of the input image as input data. The used machine learning network is trained by machine learning using at least one constraint for the output of at least one inner node of the machine learning network during the machine learning.

Abstract: A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.

Abstract: A method of manufacturing and analyzing a quality of an orthodontic aligner is described. An orthodontic aligner is manufactured by printing a mold associated with a dental arch of a patient based on a digital model of the mold, forming an orthodontic aligner over the mold, and trimming the orthodontic aligner. A quality of the orthodontic aligner is then assessed by imaging the orthodontic aligner to generate a first digital representation of the orthodontic aligner, comparing the first digital representation of the orthodontic aligner to a digital file associated with the orthodontic aligner, determining whether a cutline variation is detected between the orthodontic aligner and the digital file, and determining whether there is a manufacturing defect of the orthodontic aligner based on whether the cutline variation exceeds a cutline variation threshold.

Abstract: Various examples are provided related to face identification of material. An image can be captured by a vision system and feature parameters determined and compared to a material feature database to determine which face of the material is being presented. The vision system can employ a set of parameters for configuration to acquire the image. The system can communicate the identification to downstream processes in real time. A near-universal, color agnostic, angular orientation independent identification of material faces can be determined without the need for physical manipulation of the material.

Abstract: An image processing apparatus includes a search unit that searches a search area, which is a part of a first image extending over two pages included in the first image, for a dividable area, the first image being indicated by first image data and a division unit that generates second image data indicating two second images, each of which corresponds to one of the two pages, by dividing the first image along a dividing line passing through, in a second direction, which is perpendicular to a first direction, the dividable area found by the search unit, the first direction being a direction across the two pages.

Abstract: The innovation disclosed and claimed herein, in aspects thereof, comprises systems and methods of identifying AUs and emotion categories in images. The systems and methods utilized a set of images that include facial images of people. The systems and methods analyze the facial images to determine AUs and facial color due to facial blood flow variations that are indicative of an emotion category. In aspects, the analysis can include Gabor transforms to determine the AUs, AU intensities and emotion categories. In other aspects, the systems and method can include color variance analysis to determine the AUs, AU intensities and emotion categories. In further aspects, the analysis can include deep neural networks that are trained to determine the AUs, emotion categories and their intensities.

Abstract: A device includes an image sensor and a processor to: receive training images that each include multiple visual features and an ROI; receive indications of locations of ROIs within each training image; perform at least one transform on the multiple visual features and ROI of at least one training image to align the multiple visual features and ROIs among all of the training images to identify a common set of visual features present within all of the training images; derive a converged ROI from at least a portion of the ROI of at least one training image; and generate an anchor model based on the converged ROI and the common set of visual features, wherein the common set of visual features defines the anchor and are each specified relative to the converged ROI, and the anchor model is used to derive a location of a candidate ROI in an image.

Abstract: Systems and methods for distributing photo filters based on the location of the object in the image are described. A photo filter publication system detects that a client device in communication with the system has captured an image, identifies an object in the image, identifies a location of the object in the image, identifies an image overlay associated with the identified location and having object criteria satisfied by the identified object, and provides the identified image overlay to the client device.

Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.