Abstract: A surgical instrument navigation system is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient, wherein the surgical instrument, as provided, may be a steerable surgical catheter with a biopsy device and/or a surgical catheter with a side-exiting medical instrument, among others. Additionally, systems, methods and devices are provided for forming a respiratory-gated point cloud of a patient's respiratory system and for placing a localization element in an organ of a patient.

Abstract: According to one embodiment, a sensor includes a first control line, a first signal line, a first detection switch, a common electrode, a first detection electrode, a first circuit and a second circuit. The common electrode is located above the first control line, the first signal line and the first detection switch, opposed to the first control line, the first signal line and the first detection switch. The first detection electrode is located above the common electrode. The first circuit and the second circuit are located under the common electrode, and are opposed to the common electrode.

Abstract: An information processing method includes: obtaining noise region estimation information output from a first converter by a first image including a noise region being input to the first converter; obtaining a second image, on which noise region removal processing has been performed, output from a second converter by the noise region estimation information and the first image being input to the second converter; generating a fourth image including the estimated noise region by using the noise region estimation information and a third image including no noise region and a scene corresponding to the first image; training the first converter by using machine learning in which the first image is reference data and the fourth image is conversion data; and training the second converter by using machine learning in which the third image is reference data and the second image is conversion data.

Abstract: In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.

Abstract: An artificial intelligence (AI) platform, method and program product for detecting and sizing a lesion in real time during a clinical procedure. An AI platform is disclosed that includes: a trained classifier that includes a deep learning model trained to detect lesions and reference objects in image data; a real time video analysis system that receives a video feed during a clinical procedure, uses the trained classifier to determine if a video frame from the video feed has both a lesion and a reference object, calculates an actual size of the lesion based on a pixel size of both the lesion and the reference object, and outputs an indication that the lesion was detected and the actual size of the lesion.

Abstract: There are included: a frequency distribution creating unit for referring to the face information detected by the face information detecting unit, and when a preset change occurs in the driver's face, creating a frequency distribution of face information in a preset time section from the face information detected by a face information detecting unit; a mode value calculating unit for calculating a mode value of the face information from the frequency distribution created; and a reference value calculating unit for calculating a reference value indicating a steady state of the driver from the mode value of the face information.

Abstract: Apparatus detecting driving incapability state of a driver includes: a head detection portion that successively detects a head portion, which is higher than a neck of the driver, based on an image of a driver's seat captured by an imaging device mounted in a vehicle; and an out-of-frame state detection portion that detects that the driver is incapable of driving, when the head portion detected by the head detection portion is out of a predetermined scope in the image during travelling of the vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating still images and videos. One method includes receiving a plurality of images and a plurality of videos and determining whether the images are related to the videos. The determining includes, for an image and a video, extracting features from the image and extracting features frames of the video, and comparing the features to determine whether the image is related to the video. The method further includes maintaining a data store storing data associating each image with each video determined to be related to the image.

Abstract: In one embodiment, in addition to detecting or recognizing an actual lane, a virtual lane is determined based on the current state or motion prediction of an ADV. A virtual lane may or may not be identical or similar to the actual lane. A virtual lane may represent the likely movement of the ADV in a next time period given the current speed and heading direction of the vehicle. If an object is detected that may cross a lane line of the virtual lane and is a closest object to the ADV, the object is considered as a CIPO, and an emergency operation may be activated. That is, even though an object may not be in the path of an actual lane, if the object is in the path of a virtual lane of an ADV, the object may be considered as a CIPO and subject to a special operation.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for facial recognition. A specific embodiment of the method comprises: extracting a to-be-recognized dark light image captured in a dark light environment; inputting the dark light image into a pre-trained first convolutional neural network to obtain a target image after the dark light image is preprocessed, the first convolutional neural network being used to preprocess the dark light image; and inputting the target image into a pre-trained second convolutional neural network to obtain a facial recognition result, the second convolutional neural network being used to represent a corresponding relationship between the image and the facial recognition result. This embodiment improves accuracy of the facial recognition on the image captured in the dark light environment.

Abstract: A method and apparatus for automatically performing medical image analysis tasks using deep image-to-image network (DI2IN) learning. An input medical image of a patient is received. An output image that provides a result of a target medical image analysis task on the input medical image is automatically generated using a trained deep image-to-image network (DI2IN). The trained DI2IN uses a conditional random field (CRF) energy function to estimate the output image based on the input medical image and uses a trained deep learning network to model unary and pairwise terms of the CRF energy function. The DI2IN may be trained on an image with multiple resolutions. The input image may be split into multiple parts and a separate DI2IN may be trained for each part. Furthermore, the multi-scale and multi-part schemes can be combined to train a multi-scale multi-part DI2IN.

Abstract: This invention relates to a system that adaptively compensates for subject motion in real-time in an imaging system. An object orientation marker (30), preferably a retro-grate reflector (RGR), is placed on the head or other body organ of interest of a patient (P) during a scan, such as an MRI scan. The marker (30) makes it possible to measure the six degrees of freedom (x, y, and z-translations, and pitch, yaw, and roll), or “pose”, required to track motion of the organ of interest. A detector, preferably a camera (40), observes the marker (30) and continuously extracts its pose. The pose from the camera (40) is sent to the scanner (120) via an RGR processing computer (50) and a scanner control and processing computer (100), allowing for continuous correction of scan planes and position (in real-time) for motion of the patient (P).

Abstract: An electronic device according to embodiments may include: a communication module; a first processor configured to correct images, and a second processor operatively connected to the communication module and the first processor. The second processor is configured to control the first processor to correct a first image set obtained from a database, select at least one image from the first image set based at least on a correction result of the first image set, and transmit feature information on the selected at least one image to the first processor. The first processor is configured to obtain a second image set from the database based on the feature information, and generate a third image set by correcting the second image set. Various other embodiments may be possible.

Abstract: A component picked up by a suction nozzle of a component mounting machine is imaged by a camera, the captured image is processed by an image recognition system to recognize the component, the image is determined to be normal or abnormal based on the recognition result, and in addition to classifying the image as a normal image or an abnormal image and storing the image in a storage device, component mounting boards, unloaded from a component mounting machine, are inspected with an inspection device. A stored image reclassification computer acquires the inspection result from the inspection device, reclassifies the normal image stored in the storage device, based on the inspection result, as an image whose determination as a normal image is suspect or as an image whose determination as a normal image is not suspect, and then stores the normal image in the storage device.

Abstract: A display panel including a display function layer displaying an image in a display area, a cover member including a first face and a second face disposed on an opposite side of the first face and faces the display panel, and a fingerprint sensor including a plurality of first detection electrodes disposed in a sensor base and configured to detect unevenness of an object brought into contact with or approaching the first face of the cover member, a shield electrode disposed to face the plurality of first detection electrodes and configured to suppress a change in capacitance between the first detection electrodes and the shield electrode, and switching elements disposed in correspondence with the first detection electrodes, the fingerprint sensor being arranged between the cover member and the display panel and arranged to overlap with the display area when viewed in a direction perpendicular to the first face are included.

Abstract: A method and device for displaying a two-dimensional image of a viewed object simultaneously with an image depicting a three-dimensional geometry of the viewed object using a video inspection device is disclosed. The video inspection device displays a two-dimensional image of the object surface of a viewed object, and determines the three-dimensional coordinates of a plurality of surface points. At least one rendered image of the three-dimensional geometry of the viewed object is displayed simultaneously with the two-dimensional image. As measurement cursors are placed and moved on the two-dimensional image, the rendered image of the three-dimensional geometry of the viewed object is automatically updated.

Abstract: A vehicle insurance image processing method includes: acquiring a vehicle insurance image; processing the vehicle insurance image by using a preset image classification algorithm, to determine at least one category label of the vehicle insurance image; and storing, based on classification, the vehicle insurance image in a corresponding usage scenario according to the at least one category label.

Abstract: Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image combination can include assigning an eye pose region of a plurality of eye pose regions to each eye image and determining a representation of an eye pose in each eye image. The set of the determined representations can be fused to generate a hybrid eye image, which can in turn be used to generate an iris code associated with a confidence score.

Abstract: The present disclosure provides an optical compensation film, a photomask, and an exposure apparatus. The optical compensation film includes a first region of the optical compensation film and a second region of the optical compensation film. The first region of the optical compensation film is positioned to correspond to an overlapping portion of the prisms, and is configured to allow light to pass therethrough and impinge on the overlapping portion of the prisms. The second region of the optical compensation film is positioned to correspond to a non-overlapping portion of the prisms, and is configured to allow light to pass therethrough and impinge on the non-overlapping portion of the prisms. Light transmittance of the first region of the optical compensation film is greater than light transmittance of the second region of the optical compensation film.

Abstract: A process for improving situational awareness at an incident scene includes first receiving audio or video of a user and detecting, in one or both of the audio and video, an instruction directed to a crowd. A compliance metric associated with the instruction is then accessed and one or more available second cameras having a field of view that incorporates a current location of the crowd are identified. One or more video streams from the second cameras are received that include the crowd. From the one or more video streams, determinations are made that the crowd as a whole meets a minimum level of compliance with the instruction and that a level of compliance of a particular other user in the crowd has fallen below a threshold level of compliance. In response, a computing device performs a noncompliance action.