Abstract: Embodiments herein describe systems and methods for automated clinical image quality assessment and uses thereof are provided. Many embodiments provide instructions and feedback for clinically relevant image quality. Several embodiments provide methods to generate a model to assess image quality.

Abstract: A method and an apparatus for generating random images of a herbal formulation includes: a container to hold a herbal formulation, a sweeper tool moveable in multiple degrees of freedom, a controller operatively coupled to the sweeper tool and configured to control the movement of the sweeper tool, a camera positioned to capture an image of the herbal formulation within the container, wherein in use, the sweeper tool is moveable within the container based on one or more randomly generated movement patterns to agitate the herbal formulation within the container, and; wherein the camera is controlled to capture an image of the herbal formulation within the container after the sweeper tool has stopped moving.

Abstract: An illumination adjustment apparatus comprises: a recognizer that recognizes an abnormal region in a captured image; and an adjuster that adjusts an intensity of illuminating light in accordance with a shape and a type of the abnormal region.

Abstract: High-accuracy fitting for complex shapes that can occur in semiconductor processing is performed. A shape model is a curved line that is drawn with one stroke from its start point to its end point along a periphery of a figure that is a combination of one or more ellipses and one or more line segments in an xy-plane defined by an x axis and a y axis which are perpendicular to each other, and constraint conditions of a shape model are derived on the basis of a processing dimension function that represents processing dimensions specified on the basis of singular points in the shape model and that has shape model parameters as its variables, and definition ranges of the processing dimensions.

Abstract: An computing system includes: receiving an image that captures a hand and an object; estimating a three-dimensional (3D) mesh of the hand based on the image; estimating a two-dimensional (2D) segment of the object based on the image; estimating a 3D feature representing a correlation of the hand and the object, based on the 3D mesh of the hand and the 2D segment of the object; and generating a first set of output parameters for a first 3D query point, based on the 3D feature representing the correlation of the hand and the object.

Abstract: A processor performs control of causing illumination light with a first set light quantity to be emitted from a plurality of emission positions one by one in order to obtain a plurality of interference-fringe images serving as a source of a super-resolution interference-fringe image with a resolution exceeding a resolution of an imaging element that picks up an image of interference fringes, and at least one of control of setting a light quantity of the illumination light to a second set light quantity different from the first set light quantity during switching from a current emission position to a next emission position, or control of causing the illumination light to be emitted at a separate emission position separated by at least one emission position from both the current emission position and the next emission position during the switching from the current emission position to the next emission position.

Abstract: An apparatus for distance estimation identifies a first partial region from a first surrounding image generated at a first point of time by a first camera, generates an interpolated second surrounding image corresponding to the first point of time by using a preceding second surrounding image and the succeeding second surrounding image, and estimates an interpolated position of the second camera corresponding to the first point of time. The apparatus detects one or more feature points for a target object represented in both the first partial region and the interpolated second surrounding image, and estimates the distance between the vehicle and the target object.

Abstract: A device may receive, from a robotic device, time-of-flight sensor data that includes a point cloud derived from depth images of an environment with a floor and one or more obstacles, and may shift the depth images by a number of pixels to generate shifted depth images. The device may subtract the shifted depth images from the depth images to generate final images, and may calculate Euclidean distances associated with the final images. The device may generate masks for the final images associated with Euclidean distances that are less than a search radius, and may calculate a covariance matrix based on the masks. The device may calculate eigenvectors for the covariance matrix, and may generate an occupancy grid for the environment based on the eigenvectors for the covariance matrix.

Abstract: A fluorescence microscope system including an optical detection system configured to capture a raw image of a sample, the raw image including a plurality of pixels, each pixel having a brightness value and a processor, configured to determine one or more invalid pixels in the raw image, assign a predetermined value to each invalid pixel, determine a range of brightness values including the brightness values of a majority of the plurality of pixels excluding the one or more invalid pixels, and generate a processed image of the sample based on the determined range of brightness values.

Abstract: An annotation system uses annotations for a first set of sensor measurements from a first sensor to identify annotations for a second set of sensor measurements from a second sensor. The annotation system identifies reference annotations in the first set of sensor measurements that indicates a location of a characteristic object in the two-dimensional space. The annotation system determines a spatial region in the three-dimensional space of the second set of sensor measurements that corresponds to a portion of the scene represented in the annotation of the first set of sensor measurements. The annotation system determines annotations within the spatial region of the second set of sensor measurements that indicates a location of the characteristic object in the three-dimensional space.

Abstract: A system comprises a computer including one or more processors and memory. The memory includes instructions such that the one or more processors are programmed to receive at least one of Red-Green-Blue (RGB) image data or infrared (IR) image data from one or more cameras communicatively connected to the one or more processors, where the RGB image data and the IR image data represents an environment including at least one individual disposed along a support surface. The one or more processors receive annotated depth image data that corresponds to the RGB image data and the IR image data. The one or more processors train a neural network with at least one of the RGB image data and the IR image data corresponding to the annotated depth image data, where the neural network is trained to predict when the individual is exiting the support surface.

Abstract: Provided are a device and method for processing a plenoptic multiplanar image (MPI). The device includes a video input part configured to receive plenoptic MPI data and a processor configured to calculate an entropy for each depth level of the plenoptic MPI data, control resolutions of level images according to the entropies, and encode the level images.

Abstract: Provided herein are techniques for automatically segmenting anterior segment of an eye in an optical coherence tomography (OCT) image. A method includes receiving an OCT image of an eye; cropping, based on one or more structures of the eye in the OCT image, the OCT image of the eye into one or more sub-images corresponding to the one or more structures; for each of the one or more sub-images of the OCT image of the eye: generating a background seed and a foreground seed of the sub-image; generating, based on the background and the foreground seeds, and an image segmentation model, a mask for a structure of the one or more structures of the eye included in the sub-image; generating, based on the mask for the structure, one or more contours of the structure included in the sub-image; and displaying the one or more contours on the sub-image.

Abstract: Methods, systems, and apparatuses for heatmap regression for neural networks are provided, including for systems with restricted computational resources, such as embedded systems. An exemplary method may include utilizing a trained neural network having a plurality of pooling layers, a plurality of convolutional layers, and an output layer to generate a heatmap. The plurality of pooling layers may reduce the spatial resolution of an input image, though the resolution may not be reduced below the spatial resolution of the output layer. The plurality of convolutional layers may utilize a plurality of depthwise convolutions and pointwise convolutions that will be stacked to provide a plurality of bottlenecks.

Abstract: Techniques for preparing data for high-precision absolute localization of a moving object along a trajectory are provided. In one technique, a sliding window of a set of adjacent points along a trajectory of a moving object is identified, along with a midpoint in the sliding window. Based on the set of adjacent points, a first polynomial equation is generated for a first dimension and a second polynomial equation is generated for a second dimension. A first derivative at a particular timestamp associated with the midpoint is a first velocity along the first dimension, while a particular first derivative at the particular timestamp is a second velocity along the second dimension. A velocity in direction of yaw is generated based on the first velocity, the second velocity, and a slip angle associated with the midpoint. A yaw angle is generated based on the velocity in direction of yaw.

Abstract: In a method for subpixel disparity calculation, image data for various images each representing a field of view of an input device is received by a processor, and the image data is applied to a machine learning model. The machine learning module uses the image data to compute an output representing calculated subpixel disparity between the various images. In an example of the method, the machine learning model is a neural network that produces accurate and reliable subpixel disparity estimation in real-time using synthetically generated data.

Abstract: A method for training a computer-based machine learning model to assess a characteristics-of-interest in embryos such as viability, sex or genetic superiority/inferiority by processing video of each of a plurality of embryos and wherein a subset of the embryos each embodies the characteristic-of-interest. The method includes obtaining (taking or otherwise procuring) a plurality of training videos, each video being of a target embryo and each video having a real-time frame speed (specifically not time-lapse frame speed) over a continuous recording duration of ten minutes or less. Each video includes image data representing micro-movement of the target embryo. The image data is processed thereby generating a computer-based machine learning model for assessing the characteristic-of-interest in future embryos from image data of those embryos.

Abstract: The present disclosure provides an image recognition method, apparatus and device. In the method, after a plurality of to-be-recognized images of a preset target are obtained, first image recognition is performed on the plurality of to-be-recognized images to obtain a plurality of recognition result vectors, and further, the plurality of recognition result vectors are input a preset image quality determination model, where the preset image quality determination model is configured to, based on vector distances between the plurality of recognition result vectors corresponding to the plurality of to-be-recognized images, determine a quality of the plurality of to-be-recognized images. Thus, based on the quality, second image recognition is performed. In this way, the quality of the to-be-recognized images is considered during image recognition, which solves the problems of missing detections and low recognition result accuracy in the existing image recognition.

Abstract: Aspects of the disclosure provide for a method and apparatus to quickly identify defects in a cable used in hydrocarbon recovery wireline operations. A series of high-speed cameras take pictures along a length of the wireline cable, while artificial intelligence data processing algorithms process the camera data.

Abstract: A dimension measurement method includes: extracting a plurality of lines from a plurality of images generated by shooting a target area from a plurality of viewpoints, and generating a line segment model which is a three-dimensional model of the target area that is expressed using the plurality of lines; calculating a dimension of a particular part inside the target area, using the line segment model; and outputting the dimension calculated.