Abstract: Systems and methods for the automated culture of cells and apparatus and methods for dissociating one or more colonies in a culture of cells are described. The automated systems include at least an imaging module, a pipette module, a handling module, and optionally a stage module. Coordinated operation of the foregoing modules is effected by at least one processor based on one or more characteristics of the culture of cells calculated from one or more images captured at a first time point and at one or more subsequent time points. The apparatus includes a mounting surface, having one or more impact bumpers, connected to a rotatable drive, a platform rotatable dependently or independently of the drive, and one or more impact brackets connected to the platform and collidable with the one or more impact bumpers to transmit a dissociative force to a culture of cells in a cell culture vessel upon the platform.

Abstract: A computer-implemented method. Includes determining an estimate of a state of an object detected at a first time step, based on a pointwise estimate of the state of the object at the first time step and pointwise measurements of the state of the object at a plurality of further time steps. Includes generating, using the estimate of the state of the object, a proposed annotation associated with the object at the first time step. Includes rendering, via a user interface, a visual representation of the environment at the first time step and a visual representation of the proposed annotation. Includes receiving, via the user interface, user input indicating a user-approved annotation associated with the object at the first time step. Includes generating training data for a machine learning model for use in controlling an autonomous vehicle, based at least in part on the user-approved annotation.

Abstract: A method for reading a test region of an assay includes: capturing a plurality of images of an assay with an imaging device; from each image of the plurality of images, extracting a region of interest comprising pixels of the image associated with a test region of the assay; from each extracted region of interest, estimating respective intensity values of at least a portion of the pixels; grouping the estimated intensity values into one or more clusters, said grouping comprising determining a total number of intensity values grouped into each cluster and a variance of each cluster; selecting the cluster having a total number of intensity values at or above a predetermined threshold and a smallest variance; calculating a mean intensity value of the selected cluster; and outputting the calculated mean intensity value as a result of the assay.

Abstract: The detection device 1X mainly includes an acquisition means 12X, a common feature quantity extraction means 13X, a feature information detection means 14X, an attribute identification means 15X, and an output means 16X. The acquisition means 12X acquires data relating to a detection target. The common feature quantity extraction means 13X extracts, from the data, common feature quantity common to plural candidates of an attribute of the detection target. The feature information detection means 14X detects the feature information for each of the plural candidates based on the common feature quantity. The attribute identification means 15X identifies the attribute of the detection target based on the data. The output means 16X outputs the feature information corresponding to the identified attribute.

Abstract: Disclosed is a method for region-of-interest enhanced PET image reconstruction based on multi-task learning, which comprises the following steps: firstly, acquiring a backprojection image of the PET original data, and designing a main task of establishing a mapping between the backprojection image and a reconstructed PET image by using a three-dimensional deep convolution neural network. A new auxiliary task 1 is designed to predict a computerized tomography (CT) image with the same anatomical structures as the PET image reconstructed from the backprojection image, so as to reduce the noise in the reconstructed PET image by using the local smoothing information of the high-resolution CT image.

Abstract: A semiconductor wafer inspection method is provided. The semiconductor wafer inspection method includes: providing a wafer with target and reference dies; obtaining a candidate image of a first region of the target die and a reference image of a second region of the reference die; performing an imaging process on the candidate image to obtain a high resolution candidate image including sub-pixels for each pixel of the candidate image; performing the imaging process on the reference image to obtain a high resolution reference image including sub-pixels for each pixel of the candidate image; shifting the high resolution reference image in units of the sub-pixels; obtaining a difference image based on a difference between the high resolution candidate image and the high resolution reference image; and detecting whether a defect signal generated based on the difference image exceeds a threshold value.

Abstract: A method for image segmentation includes (a) clustering, based upon k-means clustering, pixels of an image into first clusters, (b) outputting a cluster map of the first clusters (c) re-clustering the pixels into a new plurality of non-disjoint pixel-clusters, and (d) classifying the non-disjoint pixel-clusters in categories, according to a user-indicated classification. Another method for image segmentation includes (a) forming a graph with each node of the graph corresponding to a first respective non-disjoint pixel-cluster of the image and connected to each terminal of the graph and to all other nodes corresponding to other respective non-disjoint pixel-clusters that, in the image, are within a neighborhood of the first respective non-disjoint pixel-cluster, (b) setting weights of connections of the graph according to a user-indicated classification in categories respectively associated with the terminals, and (c) segmenting the image into the categories by cutting the graph based upon the weights.

Abstract: Some devices, systems, and methods obtain training images; select a first reference image and a second reference image from the training images; generate a first set of aligned training images, wherein generating the first set of aligned training images includes aligning the training images to the first reference image; generate a first anomaly-detection model based on the first set of aligned training images; generate a second set of aligned training images, wherein generating the second set of aligned training images includes aligning the training images to the second reference image; and generate a second anomaly-detection model based on the second set of aligned training images.

Abstract: Digital image processing a digital image of sample drill cuttings retrieved from a geological formation and related methods include identifying individual zones in the image that depict at least a predetermined minimum heterogeneity of a first physical property, extracting particles in each identified zone that depict a second physical property within a predetermined quantitative range, and measuring a third physical property of each extracted particle. The first physical property may be texture, size, color, or spectral response within the zone. The second physical property may be brightness, color, contrast, hue, saturation, or wavelet energy. The third physical property may be size or color.

Abstract: Quality associated with an interpretation of data captured as unstructured data can be determined. Attributes can be identified within the unstructured data automatically. Subsequently, sentiment associated with each of the attributes can be determined based on the unstructured data. Correctness of the unstructured data, and thus the interpretation, can be assessed based on a comparison of the attribute and associated sentiment with structured data. A quality score can be generated that captures the quality of the data interpretation in terms of correctness and as well as results of another analysis including completeness, among others. Comparison of the quality score to a threshold can dictate whether or not the interpretation is subject to further review.

Abstract: The systems and methods described herein provide improvements to mapping project sites by using ground and/or aerial imagery. A system can receive one or more ground images of site locations within a site area and metadata associated with each of the one or more ground images. The metadata can include GPS coordinates that correspond to the one or more ground images. The system can determine that aerial imagery represents at least the one or more site locations and determine a relative position of each of the one or more site locations within the aerial imagery. Such a determination may be based on the metadata (e.g., on the GPS coordinates). The system can then generate data to display on a user interface one or more features, such as a map, an indication of the site area, and one or more indicators.

Abstract: Among the various aspects of the present disclosure is the provision of methods and systems for segmenting images and expediting a contouring process for MRI-guided adaptive radiotherapy (MR-IGART) comprising applying a convolutional neural network (CNN), wherein the CNN accurately segments organs (e.g., the liver, kidneys, stomach, bowel, or duodenum) in 3D MR images.

Abstract: An apparatus for constructing kinematic information of a robot manipulator is provided. The apparatus includes: a robot image acquisition part for acquiring a robot image containing shape information and coordinate information of the robot manipulator; a feature detection part for detecting the type of each of a plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint using a feature detection model generated through deep learning based on the robot image containing shape information and coordinate information; and a variable derivation part for deriving Denavit-Hartenberg (DH) parameters based on the type of each of the plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint.

Abstract: A computing system identifies broadcast video data for a game. The computing system generates tracking data for the game from the broadcast video data using computer vision techniques. The tracking data includes coordinates of players during the game. The computing system generates optical character recognition data for the game from the broadcast video data by applying one or more optical character recognition techniques to each frame of the plurality of frames to extract score and time information from a scoreboard displayed in each frame. The computing system detects a plurality of events that occurred in the game by applying one or more machine learning techniques to the tracking data. The computing system receives play-by-play data for the game. The computing system generates enriched tracking data. The generating includes merging the play-by-play data with one or more of the tracking data, the optical character recognition data, and the plurality of events.

Abstract: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle and identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs. The methods also include selecting a subset of the plurality of unlabeled sensor data logs that have an importance score greater than a threshold, the importance score being determined based on the one or more trends. The subset of the plurality of unlabeled sensor data logs is used for updating the machine learning model to generate an updated model.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) receive first data of one or more geographical environments from a first type of localization sensor, (ii) receive second data of the one or more geographical environments from a second type of localization sensor, (iii) determine constraints from the first data and the second data, (iv) determine shared pose data associated with both of the first data and the second data using the constraints determined from both the first data and the second data by determining one or more sequences of common poses between respective poses generated from each of the first and second data, wherein the shared pose data provides a common coordinate frame for the first data and the second data, and (v) generate a map of the one or more geographical environments using the determined shared pose data.

Abstract: Devices and techniques are generally described for object matching in image data. In various examples, first image data and second image data may be received. A first feature map representing the first image data and a second feature map representing the second image data may be generated. The first feature map and second feature map may be combined and a first and second segmentation mask may be generated using the combined feature map. The first segmentation mask may be used to filter the first feature map to generate a filtered representation. The second segmentation mask may be used to filter the second feature map to generate a filtered representation. A determination may be made that a first object depicted in the first image data corresponds to a second object depicted in the second image data using the filtered representations.

Abstract: A method comprising: dividing a 3D space into a voxel grid comprising a plurality of voxels; associating a plurality of distance values with the plurality of voxels, each distance value based on a distance to a boundary of an object; selecting an approximate interpolation mode for stepping a ray through a first one or more voxels of the 3D space responsive to the first one or more voxels having distance values greater than a threshold; and detecting the ray reaching a second one or more voxels having distance values less than the first threshold; and responsively selecting a precise interpolation mode for stepping the ray through the second one or more voxels.

Abstract: A substrate inspection apparatus for inspecting a substrate, includes: an acquisition part configured to acquire an estimated image of an inspection target substrate after a process by a substrate processing apparatus, based on an image estimation model created by a machine learning by using a captured image before the process by the substrate processing apparatus and a captured image after the process by the substrate processing apparatus for each of a plurality of substrates, and a captured image of the inspection target substrate before the process by the substrate processing apparatus; and a determination part configured to determine the presence or absence of a defect in the inspection target substrate, based on a captured image of the inspection target substrate and the estimated image of the inspection target substrate after the process by the substrate processing apparatus.

Abstract: Video frames from a video are compressed into a single image or a single data structure that represents a unique visual flowprint or visual signature for a given activity being modeled from the video frames. The flowprint comprises a computed summary of the original pixel values associated with the video frames within the single image and the flowprint is specific to movements occurring within the video frames that are associated with the given activity. In an embodiment, the flowprint is provided as input to a machine-learning algorithm to allow the algorithm to perform object tracking and monitoring from the flowprint rather than from the video frames of the video, which substantially improves processor load and memory utilization on a device that executes the algorithm, and substantially improved responsiveness of the algorithm.