Abstract: A system and method for generating data suitable for determining one or more parameters of at least one eye of a subject. The method includes receiving image data of an eye at a first time from a camera of known camera intrinsics and defining an image plane. A first ellipse in the image data is determined. The method includes determining a first eye intersecting line in 3D expected to intersect a 3D center of the eyeball at the first time as a line which is, in the direction of the orientation vector, parallel-shifted to the first center line by an expected distance between the center of the eyeball and a center of the pupil.

Abstract: This invention provides a system and method for finding patterns in images that incorporates neural net classifiers. A pattern finding tool is coupled with a classifier that can be run before or after the tool to have labeled pattern results with sub-pixel accuracy. In the case of a pattern finding tool that can detect multiple templates, its performance is improved when a neural net classifier informs the pattern finding tool to work only on a subset of the originally trained templates. Similarly, in the case of a pattern finding tool that initially detects a pattern, a neural network classifier can then determine whether it has found the correct pattern. The neural network can also reconstruct/clean-up an imaged shape, and/or to eliminate pixels less relevant to the shape of interest, therefore reducing the search time, as well significantly increasing the chance of lock on the correct shapes.

Abstract: When it is assumed that a large-scale Deep Neural Network for autonomous driving applied compression, there are problems of a decrease in recognition accuracy of a post-compression Neural Network (NN) model and an increase in a compression design period, due to a large number of harmful or unnecessary training images (invalid training images). A training image selection unit B100 calculates an influence value on an inference, and generates an indexed training image set 1004-1 necessary for an NN compression design, by using the influence value. A neural network compression unit P200 notified of the result via a memory P300 compresses the NN.

Abstract: According to one embodiment, a work estimation apparatus includes processing circuitry. The processing circuitry acquires video data on a predetermined area, calculates a work value related to work performed by a worker included in the video data, based on the video data, and displays the work value.

Abstract: In one example, a method is provided that includes receiving lidar data obtained by a lidar device. The lidar data includes a plurality of data points indicative of locations of reflections from an environment of the vehicle. The method includes receiving images of portions of the environment captured by a camera at different times. The method also includes determining locations in the images that correspond to a data point of the plurality of data points. Additionally, the method includes determining feature descriptors for the locations of the images and comparing the feature descriptors to determine that sensor data associated with at least one of the lidar device, the camera, or a pose sensor is accurate or inaccurate.

Abstract: An image acquisition and analysis system are disclosed. The system enables high throughput, objective analysis of microbial samples over days or weeks. The system may accommodate upwards of twelve 96- or 384-well plates simultaneously (liquid or solid media). The system may acquire and analyze a large number of samples in a short period of time. For example, over 384 samples per minute or 18,432 samples per hour. The system hardware may include a multi-spectral imager (fluorescence and bright field detection), electro-mechanical assemblies, and an optional high-resolution stage. The system may automate image acquisition, image data processing, simplify data storage, and enable automated analysis tools to significantly reduce the manual labor and time associated with such tasks. The system may allow for quick processing and analysis of data into clear phenotypic classes.

Abstract: Selections of content shared from a remote device during a video conference are copied to a destination of a computing device connected to the video conference live or at which a recording of the video conference is viewed. The content shared from the remote device during the video conference is output at a display of the computing device. A portion of the content is selected according to an instruction received from a user of the computing device while output at the display of the computing device to copy to a destination associated with software running at the computing device. The portion of the content is identified using a machine vision process performed against the content while output at the display of the computing device. The portion of the content is then copied to the destination.

Abstract: A system may be configured to perform enhanced detection of occluded objects in a multiple object detection system. In some aspects, the system may determine a first size of a queue captured in a video frame based on an object detection system and a video capture device, and determine a second size of the queue based on the first size of the queue and a correction model. Further, the system may detect a person at a front of the queue based on an object tracking system, determine a first period of time spent by the person at the front of the queue based on an object tracking system, and estimate a second period of time spent in the queue based at least in part on the first period of time and the second size of the queue.

Abstract: A camera apparatus includes control circuitry, where in a training phase, the control circuitry is configured to generate a set of binary images of foliage masks, where each binary image comprises one or more foliage regions demarcated from a non-foliage region. A modified training dataset of color images is generated and a custom neural network model for foliage detection is trained. The control circuitry causes the custom neural network model to not only learn a plurality of features related to foliage but also learn a color variation range of predefined color associated with the plurality of features and utilize combination of the plurality of features related to foliage and the color variation range to obtain trained custom neural network model. The control circuitry is configured to operate the trained custom neural network model to detect one or more foliage regions in a new color image in real time.

Abstract: To present a region which is not fully monitored, provided is a monitoring device comprising a detection means, an estimation means, a determination means, and a presentation means. The detection means detects a person from an image. The estimation means estimates a region which is being viewed by the person who is detected by the detection means. On the basis of the region estimated by the estimation means, the determination means determines a region which requires special attention within a monitoring area. The presentation means presents information indicating the region which requires special attention determined by the determination means.

Abstract: To present a region which is not fully monitored, provided is a monitoring device comprising a detection means, an estimation means, a determination means, and a presentation means. The detection means detects a person from an image. The estimation means estimates a region which is being viewed by the person who is detected by the detection means. On the basis of the region estimated by the estimation means, the determination means determines a region which requires special attention within a monitoring area. The presentation means presents information indicating the region which requires special attention determined by the determination means.

Abstract: To present a region which is not fully monitored, provided is a monitoring device comprising a detection means, an estimation means, a determination means, and a presentation means. The detection means detects a person from an image. The estimation means estimates a region which is being viewed by the person who is detected by the detection means. On the basis of the region estimated by the estimation means, the determination means determines a region which requires special attention within a monitoring area. The presentation means presents information indicating the region which requires special attention determined by the determination means.

Abstract: A computing apparatus to classify anomalies in images, by unsupervised anomaly detection on an input dataset of the images to detect anomaly portions from said images to generate, for an image in the dataset, a corresponding mask image transmitting a detected anomaly portion in the image and blocking anomaly-free portions; train a classifier ANN, including, in a first epoch process processing a masked version of the input dataset with the classifier ANN, the masked version including the image of the input dataset masked by the corresponding mask image, and training the classifier ANN to classify anomaly portions into one of plural classes by minimising a cross entropy loss function using generated labels as ground truths; extracting, from the classifier ANN, a latent feature representation of the image of the masked version in the input dataset; and in a second epoch process generating a set of pseudo labels corresponding to the masked version of the input dataset by applying an unsupervised clustering algori

Abstract: To present a region which is not fully monitored, provided is a monitoring device comprising a detection means, an estimation means, a determination means, and a presentation means. The detection means detects a person from an image. The estimation means estimates a region which is being viewed by the person who is detected by the detection means. On the basis of the region estimated by the estimation means, the determination means determines a region which requires special attention within a monitoring area. The presentation means presents information indicating the region which requires special attention determined by the determination means.

Abstract: Embodiments include receiving image data comprising contours of teeth of an individual and generating a new image based on the image data and one or more parametric functions associated with tooth color that comprise a first variable for a first image axis and a second variable for a second image axis, wherein a shape of the teeth in the new image is based on the image data and a color of the teeth in the new image is based on applying the one or more parametric functions to at least a portion of the image data.

Abstract: A real-time measurement system for mechanical parameters of rock slag during excavation includes a rock slag conveyor belt configured to convey rock slag; cleaning device, located on both sides of the rock slag conveyor belt, configured to clean the rock slag; an image acquisition device, located behind the cleaning device and on both sides and above the rock slag conveyor belt, configured to collect images of rock slag; an on-site loading device, for conducting on-site mechanical loading procedure on the rock slag to obtain on-site mechanical parameters. The on-site loading device includes a loading bottom plate, a push plate and a pressure head. The loading bottom plate is provided with a loading area. The push plate is located on the loading bottom plate and pushed by a pushing mechanism to push the rock slag.

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically identifying table locations and table cell types of located tables. One example method includes receiving a request to detect tables. Features are extracted from an input spreadsheet and provided to a trained table detection model trained to predict whether worksheet cells are table cells or background cells and to a cell classification model that is trained to classify worksheet cells by cell structure type. The table detection model generates binary classifications that indicate whether cells are table cells or background cells. A contour detection process is performed on the binary classifications to generate table location information that describes at least one table boundary in the spreadsheet. The trained cell classification model generates a cell structure type classification for each cell that is included in a table boundary generated by the contour detection process.

Abstract: A method of compressing image data comprising a set of image values each representing a position in image-value space so as to define an occupied region thereof. The method comprises selectively applying a series of compression transforms to subsets of the image data items to generate a transformed set of image data items occupying a compacted region of value space. The method further comprises identifying a set of one or more reference data items that quantizes the compacted region in value space. For each image data item in the set of image data items, a sequence of decompression transforms from a fixed set of decompression transforms is identified that generates an approximation of that image data item when applied to a selected one of the one or more reference data items. Each image data item in the set of image data items is encoded as a representation of the identified sequence of decompression transforms for that image data item.

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

Abstract: Systems, methods, and computer-readable storage media for digital fingerprinting of media content. The system does this by normalizing and pre-processing the media content into a standard format, then generating fingerprints for image, audio, and/or video aspects of the media. These fingerprints are mathematical vectors generated using one or more techniques, such as perceptual hashes coupled with machine learning. Once the fingerprints for new content is generated, the system can compare those fingerprints to fingerprints of known content to determine if the new content contains prohibited content.