Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: A method includes receiving first intraoral scan data comprising a first depiction of a dental site comprising a preparation tooth; generating a virtual three-dimensional (3D) model of the dental site using the first intraoral scan data; processing at least one of the first intraoral scan data or data from the virtual 3D model to identify a margin line of the preparation tooth, wherein at least a portion of the margin line is unclear; outputting instructions to generate second intraoral scan data of the dental site depicting the portion of the margin line that is unclear; receiving the second intraoral scan data; and updating the virtual 3D model of the dental site using the second intraoral scan data, wherein the portion of the margin line that was unclear in the virtual 3D model is replaced with information from the second intraoral scan data.

Abstract: An imaging system and method for microbial growth detection, counting or identification. One colony may be contrasted in an image that is not optimal for another type of colony. The system and method provides contrast from all available material through space (spatial differences), time (differences appearing over time for a given capture condition) and color space transformation using image input information over time to assess whether microbial growth has occurred for a given sample.

Abstract: Systems and methods for obtaining diagnostic data of a target are disclosed. The systems include an imaging device and a drape. The imaging device includes at least one excitation light source for fluorescent imaging, and an optical sensor configured to detect signals responsive to illumination of the target with the excitation light. The drape is configured to reduce ambient light surrounding the target during imaging.

Abstract: A recognition apparatus and a training method are provided. The recognition apparatus includes a memory configured to store a neural network including a previous layer of neurons, and a current layer of neurons that are activated based on first synaptic signals and second synaptic signals, the first synaptic signals being input from the previous layer, and the second synaptic signals being input from the current layer. The recognition apparatus further includes a processor configured to generate a recognition result based on the neural network. An activation neuron among the neurons of the current layer generates a first synaptic signal to excite or inhibit neurons of a next layer, and generates a second synaptic signal to inhibit neurons other than the activation neuron in the current layer.

Abstract: In an environment in which a plurality of second pedestrians moves along predetermined movement patterns, a plurality of movement routes when a first pedestrian moves toward a destination point is recognized. Data, in which an environmental image indicating a visual environment in front of a virtual robot when the virtual robot moves along each of the movement routes and a moving direction command indicating a moving direction of the virtual robot are combined, is generated as learning data. In the environmental image, colors corresponding to kinds of the moving objects are applied to at least a portion of moving object image regions indicating pedestrians (moving objects) present around a robot. Model parameters of a CNN (action model) is learned using the learning data, and a moving velocity command for the robot is determined using a learned CNN.

Abstract: A safety system for use with a power tool that is usable by a user, the power tool including a base and a moving component that is movable relative to the base, includes a sensor assembly and a controller. The sensor assembly monitors a predetermined danger zone that is adjacent to the moving component of the power tool. The sensor assembly is configured to generate data relating to the predetermined danger zone. The controller receives the data from the sensor assembly and analyzes the data from the sensor assembly to determine if at least a portion of a hand of the user is present within the predetermined danger zone. The safety system can further include a wearable component including infrared only reflective material that is coupled to the hand of the user. The controller analyzes the data from the sensor assembly to determine if the wearable component is present within the predetermined danger zone.

Abstract: A distribution of a plurality of predictions generated by a deep neural network using sensor data is calculated, and the deep neural network includes a plurality of neurons. At least one of a measurement or a classification corresponding to an object is determined based on the distribution. The deep neural network generates each prediction of the plurality of predictions with a different number of neurons.

Abstract: A method, apparatus, and computer program product are provided for providing improved neural network implementations using a repeated convolution-based attention module. Example embodiments implement a repeated convolution-based attention module that utilizes multiple iterations of a repeated convolutional application layer and subsequent augmentations to generate an attention module output. Example methods may include augmenting an attention input data object based on a previous iteration convolutional output to produce a current iteration input parameter, inputting the input parameter to a repeated convolutional application layer to generate a current iteration input parameter, repeating for multiple iterations, and augmenting the attention input data object based on the final convolutional output to produce an attention module output.

Abstract: A server for knowledge recommendation for defect review. The server includes a processor electronically coupled to an electronic storage device storing a plurality of knowledge files related to wafer defects. The processor is configured to execute a set of instruction to cause the server to: receive a request for knowledge recommendation for inspecting an inspection image from a defect classification server; search for a knowledge file in the electronic storage device that matches the inspection image; and transmit the search result to the defect classification server.

Abstract: An apparatus includes an interface and a processor. The interface may be configured to receive pixel data of an area external to a vehicle. The processor may be configured to generate video frames from the pixel data, perform computer vision operations on the video frames to detect objects in the video frames and determine characteristics of the objects, analyze the characteristics of the objects to determine elevation characteristics of a driving surface with respect to the vehicle, perform a comparison of the elevation characteristics to clearance data of the vehicle and determine an approach angle for the vehicle in response to the comparison. The approach angle may be determined to prevent an impact between the vehicle and the driving surface. The approach angle may be presented to a vehicle system.

Abstract: A system for an ID card identification prevents unauthorized entry to a venue by comparing ID card data for validity and internal matching of a person to a photo on an ID card. The system includes an identification card offered by an individual seeking entry; wherein the identification card includes a photograph of the individual the card is assigned to. An electronic device having a camera thereon takes a photograph of the photograph on the identification card. The electronic device takes a picture of individual offering the identification card for access to the venue. The electronic device verifies the authenticity of the identification card. The electronic device having circuitry and software which determines a match, low match or no match between the individual in the photograph on the identification card and the photograph of the individual presenting the identification card, for entry into the venue.

Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.

Abstract: Systems, methods, and computer storage media are disclosed for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).

Abstract: Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

Abstract: A two-dimensional image acquisition unit acquires a plurality of two-dimensional images generated from a three-dimensional image of a patient in a case where an observation target is viewed from a plurality of different viewpoints, and a surface data acquisition unit acquires surface data of each of a plurality of structures on the patient from the three-dimensional image. A composite image generation unit generates a composite image in which a composite target image generated from the surface data and the two-dimensional image are composed, and a display switching control unit receives an operation of displaying or non-displaying any of the structures and performs display by switching between the composite image of the composite target image and the two-dimensional image corresponding to the structure to be displayed and the two-dimensional image of the structure to be non-displayed.

Abstract: A facility identifies anatomical objects visualized by a medical imaging image. The facility applies two machine learning models to the image: a first trained to predict a view probability vector that, for each of a list of views, attributes a probability that the image was captured from the view, and a second trained to predict an object probability vector that, for each of a list of anatomical objects, attributes a probability that the object is visualized by the image. For each object, the facility: (1) accesses a list of views in which the object is permitted; (2) multiplies the predicted probability that the object is visualized by the image by the sum of the predicted probabilities that the accessed image was captured from views in which the object is permitted; and (3) where the resulting probability exceeds a threshold, determines that the object is visualized by the accessed image.

Abstract: A training method, system, and computer program product include training a neural network including using two-sided ReLU as a non-linear function or norm-pooling as a non-linear function and increasing a confidence gap.

Abstract: A method for automatic selection of display settings for a medical image is provided. The method includes receiving a medical image, mapping the medical image to an appearance classification cell of an appearance classification matrix using a trained deep neural network, selecting a first WW and a first WC for the medical image based on the appearance classification and a target appearance classification, adjusting the first WW and the first WC based on user preferences to produce a second WW and a second WC, and displaying the medical image with the second WW and the second WC via a display device.

Abstract: The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.