Abstract: An intraoral scanning apparatus has a source of low coherence light. An interferometer directs the low coherence light to a reference path and a sample path and generates image data according to interference from combined light returned along the reference and sample paths. A fixture is optically coupled to the sample path and has a bite portion featured for clamping between the jaws of a patient, a track that defines a curved scan path for scanning, one or more scanners configured to direct the sample path light to and from the teeth, and an actuator and translation apparatus that urges the one or more scanners along the curved scan path. A control logic processor synchronizes light scanning and acquisition from the fixture. A display is in signal communication with the control logic processor for display of acquired scan data.

Abstract: A diagnosis device provided with a position data generation unit (224) and a linking unit (226). The position data generation unit (224) generates first position data and second position data using a relative position image showing the relative positions of an upper bow and a bite-fork, an upper-part image showing the positional relationship of the bite-fork and the maxillary dental arch of a patient, and an articulation image showing the positional relationship of the maxillary dental arch and the mandibular dental arch of the patient. The linking unit (226) generates a virtual articulator by positioning three-dimensional models of the patient's maxillary dental arch and mandibular dental arch generated by a model generation unit (223), using the first position data and the second position data.

Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: An image device utilizing non-planar projection images to generate a depth map includes two image capturers and a depth engine. The two image capturers are used for generating two non-planar projection images, wherein when each non-planar projection image of the two non-planar projection images is projected to a space corresponding to an image capturer corresponding to the each non-planar projection image, projection positions of each row of pixels of the each non-planar projection image in the space and optical centers of the two image capturers share a plane. The depth engine is used for generating a depth map according to the two non-planar projection images.

Abstract: A mid-protocol evaluation system is operable to receive a set of medical scans corresponding to a proper subset of a plurality of sequence types in a medical scan protocol captured for a patient. Abnormality data is generated by performing an inference function on the set of medical scans, where the inference function utilizes a computer vision model trained on a plurality of medical scans corresponding to the subset of the plurality of sequence types. A confidence score for the abnormality data is calculated, and additional sequence necessity data is generated for transmission to a client device for display via a display device. The additional sequence necessity data indicates at least one additional medical scan is necessary when the confidence score compares unfavorably to a confidence score threshold. The additional sequence necessity data indicates no further medical scans are necessary when the confidence score compares favorably to the confidence score threshold.

Abstract: A pixel having an original RGB color is processed by determining which color component of the pixel that will be clipped for a luma value resulting in a luminance value that is closest to an original luminance value obtained based on the original RGB color. A LUT is selected based on which color component of 5 the pixel that will be clipped. At least one LUT index is obtained based on LUT-specific maximum and minimum values for the selected LUT and the original luminance value. A luma value is determined for the pixel based on a LUT entry fetched from the selected LUT using the at least one LUT index. The invention thereby achieves a feasible LUT implementation of luma adjustment that can be used to improve luminance values of pixels.

Abstract: A method for identification of characteristic points of a calibration pattern within an image includes: (i) overlaying a template arrangement of template points over the candidate point such that a principal point of the template points coincides with the candidate point, wherein the template arrangement corresponds to the calibration pattern or to a sub-pattern of the calibration pattern, (ii) for each template point of the template arrangement, except the principal point, identifying from the set of candidate points the candidate point closest to the template point, and (iii) determining a degree of deviation by summing the distances between each template point of the template arrangement, except the principal point, and the candidate point closest to this template point; and identifying as characteristic points of the calibration pattern all those candidate points of the set of candidate points with a degree of deviation below a deviation threshold.

Abstract: Aspects of the subject matter disclosed herein include methods, systems, and other techniques for training, in a first phase, an object classifier neural network with a first set of training data, the first set of training data including a first plurality of training examples, each training example in the first set of training data being labeled with a coarse-object classification; and training, in a second phase after completion of the first phase, the object classifier neural network with a second set of training data, the second set of training data including a second plurality of training examples, each training example in the second set of training data being labeled with a fine-object classification.

Abstract: Apparatuses and methods associated with personal history in a track and trace system are disclosed herein. In embodiments, a method includes acquiring first information corresponding to a physical object; identifying first authentication data based on the first information; storing the first authentication data in a database system; subsequent to storing the first authentication data, acquiring second information corresponding to a target physical object and identifying second authentication data based on the second information; identifying third information corresponding to the target physical object; querying the database system to seek a matching record based on the second authentication data; in the case that a matching record is returned responsive to the querying, updating the matching record with an indication that the second authentication data was matched to it and updating history data of said matching record based on said third information. Other embodiments may be disclosed or claimed.

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

Abstract: A method for correcting an image includes: orthographically correcting an image; removing a curtain artifact by applying a first filter to the orthographically corrected image; correcting brightness of the image, from which the curtain artifact is removed, by applying a second filter to the image, from which the curtain artifact is removed. The first filter includes a first function and a second function for a first domain and a second domain, which are orthogonal to each other in a frequency region, and the first filter is differentiable and continuous in the first domain and the second domain.

Abstract: In one or more embodiments, a method for calibration between a lidar sensor and a camera comprises determining translation parameters of extrinsic calibration parameters by using a location of the camera with respect to the lidar sensor. The method further comprises orienting a target chessboard such that it is aligned with axes of a lidar coordinate system. Also, the method comprises optimizing a best fit transformation between a camera coordinate system and a target chessboard coordinate system. In addition, the method comprises determining a rotation matrix using the best fit transformation between the camera coordinate system and the target chessboard coordinate system. Additionally, the method comprises extracting Euler angles of the extrinsic calibration parameters from the rotation matrix. Also, the method comprises collecting, by the lidar sensor and the camera, calibration scenes using the target chessboard.

Abstract: Methods, apparatuses, systems, and implementations for creating a patient-specific soft bolus for radiotherapy treatment are disclosed. 2D and/or 3D images of a desired radiotherapy treatment site may be acquired, such as the head, neck, skin, breast, anus, and/or vulva. A user may interact with one or more representations of the images via an interactive user interface such as a graphical user interface (GUI). The images may include target/avoidance structures and radiation beam arrangement. The user may interact with the images to create a visualization of a patient-specific bolus. The visualization and properties of the bolus may be modified as the user manipulates aspects of the image. Data corresponding to the bolus models may be used to create 3D printed negative molds of the bolus model using 3D printing technology. A soft patient-specific bolus may be cast using the 3D printed model.

Abstract: Embodiments relate to an intelligent computer platform to compute visual similarity across image objects. An object detection algorithm is utilized to identify image objects and to produce a tensor representation of the identified object. Multi-visual contextual similarity of the object is conducted to assess and determine related object images. A re-assessment of similarity is dynamically conducted in response to a product image selection. The re-assessment utilizes the tensor representations of the related object images, thereby conducting a mathematical assessment of similarity and object image identification. A final product is identified and selected based on the dynamic re-assessment and convergence on a directed outcome with minimal iterations of object interaction.

Abstract: This present disclosure relates to systems and processes for capturing an unstructured light field in a plurality of images. In particular embodiments, a plurality of keypoints are identified on a first keyframe in a plurality of captured images. A first convex hull is computed from all keypoints in the first keyframe and merged with previous convex hulls corresponding to previous keyframes to form a convex hull union. Each keypoint is tracked from the first keyframe to a second image. The second image is adjusted to compensate for camera rotation during capture, and a second convex hull is computed from all keypoints in the second image. If the overlapping region between the second convex hull and the convex hull union is equal to, or less than, a predetermined size, the second image is designated as a new keyframe, and the convex hull union is augmented with the second convex hull.

Abstract: Provided is a video display system that includes a display device and an image processing device. The display device is mounted on the head of a user for use. The image processing device generates a plurality of unsynthesized images having different resolutions that are to be used for display by the display device, and transmits the generated unsynthesized images to the display device. The display device receives the unsynthesized images transmitted from the image processing device, and displays a display image that is obtained by synthesizing the unsynthesized images.

Abstract: Color mapping information can be used to transform one color to another color. The present embodiments provide a solution for representing the color mapping information using a successive application of multiple color mapping functions. Parameters for the multiple color mapping functions can be encoded into a bitstream. In one embodiment, color mapping functions are consecutively applied on their own domains of definition only. In another embodiment, the first color mapping (CRI1) is applied on its domain of definition only, but the second color mapping is applied only on samples that have been previously color mapped by CRI1 and which are also inside the domain of definition of the second color mapping function. At the decode side, the multiple color mapping functions can be reconstructed and successively applied to a decoded picture to generate another picture.

Abstract: Disclosures of the present invention describe a moving object detection system and method, wherein a pre-processer module, a feature extraction module, an image optical flow estimation module, a feature points grouping module, and a moving object determination module are provided in a controlling and processing module of the system by a form of library, variables, or operands. Moreover, a feature difference calculation unit, a matrix establishing unit and a corner feature point acquiring unit are provided in the feature extraction module, and that is helpful for enhancing computing speed of the controlling and processing device in verifying corner feature points from image frames. Therefore, after the corner feature points are applied with a cluster labeling process, the moving object determination module can achieve motion detection of at least one object locating in a monitoring area by determining whether corner feature point groups move or not.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: There are provided a medical image processing device, an endoscope system, a diagnosis support device, and a medical service support device that can allow the observation of a medical image performed by a user not to be obstructed, allow a user not to miss a region of interest, and allow attention required during observation to be continued. An image acquisition unit acquires a medical image including a subject. A medical image is displayed in a first display region (A1) of a monitor. A bounding box, which is notification information, is superimposed and displayed on the medical image in the first display region (A1). In a case where the area of a region of interest is increased, or the like, the notification information is not displayed in the first display region (A1) and an icon, which is separate notification information, is displayed in a second display region (A2) of the monitor.