Abstract: Examples include detecting objects and determining a set of features for the objects. Examples include receiving a first image input, generating a number of feature maps from the first image input using a number of convolution filters, generating a first number of fully connected layers directly based on the number of feature maps, and detecting a number of objects in the first image and determining a set of features for each object from the first number of fully connected layers.

Abstract: A decoding device, an encoding device and a method for point cloud decoding is disclosed. The method includes generating, for a 3D point cloud, a first 2D frame representing a first attribute and a second 2D frame representing a second attribute. The first 2D frame and the second 2D frame include respective clusters of projected points from the 3D point cloud. The method includes detecting missed points of the 3D point cloud and generating first and second additional points patches representing the first attribute and the second attribute, respectively, based on at least a subset of the missed points. The method includes including the first and second additional points patch in the first and second 2D frame, respectively. The method includes encoding the first 2D frame and the second 2D frame to generate a compressed bitstream and transmitting the compressed bitstream.

Abstract: A listing of objects expected to be loaded into a container is received, and as an object is added to a container sensors monitoring an opening of the container detect the object. The added object is identified by object recognition techniques applied to received sensor data. Detection of a triggering event indicating completion of adding objects to the container is determined, and in response, the identity of the added object is compared to the received list of objects. In response to determining the object added to the container fails to match an object on the received list of objects, a notification of non-matching content is generated, and in response to determining the object added to the container matches an object on the received list of objects, a notification confirming the match of content in the container to the list of objects is generated.

Abstract: A pattern inspection system inspects an image of an inspection target pattern of an electronic device using an identifier constituted by machine learning, based on the image of the inspection target pattern of the electronic device and data used to manufacture the inspection target pattern. The system includes a storage unit which stores a plurality of pattern images of the electronic device and pattern data used to manufacture a pattern of the electronic device, and an image selection unit which selects a learning pattern image used in the machine learning from the plurality of pattern images, based on the pattern data and the pattern image stored in the storage unit.

Abstract: Systems and methods are described herein for tracking an elongate instrument or other medical instrument in an image.

Abstract: A network-connected security device is communicatively coupled to an audio/video (A/V) recording and communication device having a camera and a speaker. A method receives video data captured by the camera, and performs an object recognition algorithm upon the received video data to identify an object therein. The method performs a table lookup using the identified object, into a data structure that associates objects with at least one description of a predefined voice message. The method selects a description of a predefined voice message associated with the identified object, and transmits the selected description's predefined voice message to the A/V recording and communication device for output through the speaker.

Abstract: There is provided a method of locating a tip of a metallic instrument inserted in a body, utilizing a baseline sinogram comprising projections in N exposure directions and derived from a prior computerized tomography (CT) scanning of the body, the method comprising: performing three-dimensional Radon space registration of a sparse repeat sinogram to the baseline sinogram, the repeat CT scanning having the metallic instrument inserted into the body, the metallic instrument having an attached marker located at a known distance from the instrument tip; subtracting the baseline sinogram from the repeat sinogram in accordance with the registration parameters to obtain projection difference images; and using the projection difference images and the known distance of the attached marker from the metallic instrument tip to determine a three-dimensional location of the metallic instrument tip.

Abstract: The present application provides a medical imaging method and system and a non-transitory computer-readable storage medium. The medical imaging method comprises identifying an image quality type of a medical image based on a trained learning network, and generating, based on the identified image quality type, a corresponding control signal for controlling a medical imaging system.

Abstract: In one embodiment a medical scan correction system includes a medical instrument having a distal end and an image sensor disposed at the distal end, and configured to be inserted into a body-part of a living subject, and a controller to register a medical scan of the body-part with a coordinate frame of the medical instrument, the medical scan including initial tissue-type indications, render a first image including a representation of the body-part and the initial tissue-type indications based on the medical scan, and a representation of the medical instrument, render a second image captured by the image sensor of a portion of the body-part, and responsively to a comparison between an appearance of a tissue in the second image of the portion of the body-part and an initial tissue-type indication of the tissue, update the medical scan to modify the tissue-type indication associated with the tissue.

Abstract: A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison.

Abstract: System and methods are provided for predicting a roadway feature. A geometric pattern in a roadway network is selected. The geometric pattern comprises one or more first links and one or more first nodes. A similar pattern to the geometric pattern is identified that comprises one or more second links and one or more second nodes in the roadway network. One or more features for the geometric pattern are identified. An absence of a feature in the similar pattern is determined based on the one or more features for the geometric pattern. A notification relating to the absence of the feature is generated.

Abstract: A determination processing unit determines a disease region in a medical image including an axisymmetric structure. A first determination section of the determination processing unit generates a feature amount map of the medical image from the medical image. A second determination section second inverts the feature amount map with reference to a symmetry axis to generate an inverted feature amount map. A third determination section superimposes the feature amount map and the inverted feature amount map on each other and determines the disease region in the medical image using the feature amount map and the inverted feature amount map superimposed on each other.

Abstract: An information processing device includes a categorizing section configured to extract a material component image identified as a material component from plural images obtained by imaging a sample fluid containing a plurality of types of material components and flowing through a flow cell, and to categorize the extracted material component image by predetermined category, a count derivation section configured to derive a count of the material component per standard visual field, or derive a count per unit liquid volume of the material component contained in the sample fluid, for each of the categories based on the number of material component images categorized by the categorizing section, and a generation section configured to generate an all-component image in which the material component images are arranged according to the counts that have been derived by the count derivation section for each of the categories.

Abstract: According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.

Abstract: A method of data processing for an object identification system comprising a neural network. The method comprises, in a secure environment, obtaining first sensed data representative of a physical quantity measured by a sensor. The first sensed data is processed, using the neural network in the secure environment, to identify an object in the first sensed data. The method includes determining that the identified object belongs to a predetermined class of objects. In response to the determining, a first portion of the first sensed data is classified as data to be secured, and a second portion of the first sensed data is classified as data which is not to be secured. The second sensed data, derived from at least the second portion, is outputted as non-secure data.

Abstract: A method for scene parsing includes: performing a convolution operation on a to-be-parsed image by using a deep neural network to obtain a first feature map, the first feature map including features of at least one pixel in the image; performing a pooling operation on the first feature map to obtain at least one second feature map, a size of the second feature map being less than that of the first feature map; and performing scene parsing on the image according to the first feature map and the at least one second feature map to obtain a scene parsing result of the image, the scene parsing result including a category of the at least one pixel in the image. A system for scene parsing and a non-transitory computer-readable storage medium can facilitate realizing the method.

Abstract: Systems and methods for property feature detection and extraction using digital images. The image sources could include aerial imagery, satellite imagery, ground-based imagery, imagery taken from unmanned aerial vehicles (UAVs), mobile device imagery, etc. The detected geometric property features could include tree canopy, pools and other bodies of water, concrete flatwork, landscaping classifications (gravel, grass, concrete, asphalt, etc.), trampolines, property structural features (structures, buildings, pergolas, gazebos, terraces, retaining walls, and fences), and sports courts. The system can automatically extract these features from images and can then project them into world coordinates relative to a known surface in world coordinates (e.g., from a digital terrain model).

Abstract: A virtual reality visual indicator apparatus comprising a virtual reality image capture device comprising a plurality of cameras configured to capture a respective plurality of images of a scene, the respective plurality of images of the scene configured to be connected at stitching regions to provide a virtual reality image of the scene; and a visual indicator provider configured to transmit, into the scene, a visual indicator at a location of at least one stitching region prior to capture of the respective plurality of images of the scene and provide no visual indicator during capture of the respective plurality of images.

Abstract: A method includes generating, with a mobile device having a camera, images of a portion of a real space containing an object having a position. The control device tracks the position of the object based at least on the images received from the mobile device. The control device monitors the position of the object for an event conforming to a rule specified at the control device. The event is based on the rule and the position of the object in the real space. The control device, or a client device, generates an indication that the event has been detected by the control device.

Abstract: Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model, which may also be known as a machine learning system, to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.