Abstract: An information processing system includes circuitry configured to acquire an optical character recognition (OCR) result of image data, obtained by performing OCR processing on the image data, store the OCR result in a memory, extract a first character string from the OCR result stored in the memory, and generate a file name to be assigned to a file of the image data using the extracted first character string.

Abstract: A simple and quick verification of the authenticity of a blister of elements, such as pills in the pharmaceutical industry, is provided. The pills packaged in a blister can be authenticated using mobile imaging devices such as a mobile smartphone. Generally speaking, an image of the pills and their differentiating physical characteristics in the blister is acquired and compared to at least one code which is marked on the blister itself. This code may also be encrypted. A dedicated software application on the mobile smartphone can be used to decrypt the code and compare the code to the image of the pills in the blister, to thereby evaluate authenticity of the blister and its contents.

Abstract: Systems and methods which obtain template images corresponding to pattern units of tessellated images using a template unit boundary identification technique are described. Template unit boundary identification techniques of embodiments operate to analyze a sample of a tessellated image to determine a template unit size for the template image, use the determined template unit size to define pixel analysis line segments for identifying template unit boundaries in the tessellated image, and select a template image based on the template unit boundaries identified using the pixel analysis line segments. One or more sample area images may be selected for use in determining a template unit size. Pixel analysis line segments, configured based upon a determined template unit size, may be used for identifying boundaries of a template unit. Moreover, dynamic template image updating may be provided.

Abstract: A method of estimating a position includes: determining a first rotation reference point based on localization information estimated for a target and map data; estimating a second rotation reference point from image data associated with a front view of the target; and correcting the localization information using a rotation parameter calculated based on the first rotation reference point and the second rotation reference point.

Abstract: A system for automated user input alignment receives the user input at a touchscreen display. A skew of the user input is identified as the user input is being received at a touchscreen display. A skew correction is determined based on the identified skew. The skew correction is applied to the user input to align the user input on the touchscreen display. The skew correction applied in an automated alignment process that. The user input is displayed with the applied skew correction on the touchscreen display with improved efficiency and without user manipulation to perform the alignment.

Abstract: The present invention relates to the field of three-dimensional face modeling, and provides a three-dimensional real face modeling method and a three-dimensional real face camera system. The three-dimensional real face modeling method includes the following steps: 1, projecting structured light to a target face, and taking a photo to acquire facial three-dimensional geometric data; 2, acquiring facial skin chroma data and brightness data; 3, triangulating the facial three-dimensional geometric data; 4, acquiring patch chroma data corresponding to each of triangular patch regions; 5, performing interpolation calculation to acquire patch brightness data corresponding to each of the triangular patch regions; and 6, calculating a reflectivity of a facial skin region corresponding to each of pixel points. The three-dimensional real face camera system includes: a standard light source, a control and calculation module and a three-dimensional portrait acquisition unit.

Abstract: An optical measuring system for fitting spectacles to a subject and/or for diagnosing and/or monitoring ocular diseases and/or disorder in the subject, and methods of use thereof. The system includes at least one image capturing device for capturing at least one image of at least part of the face of the subject. The system also includes at least one movable mount for mounting the image capturing device in front of the subject and moving the image capturing device relative to the subject; and at least one processor operatively connected to the image capturing device for generating a three dimensional (3D) model of the at least part of the face of the subject. The at least one processor is also configured to determine, from the 3D model generated, one or more optical measurements of the subject.

Abstract: The present disclosure provides a display screen, including a display layer and a light shielding layer. The display layer includes an upper surface and a lower surface opposite to the upper surface, the upper surface faces a user, the light shielding layer is arranged to the lower surface of the display layer, and the light shielding layer has a hole. The present disclosure further provides a display device, including the above-mentioned display screen and a fingerprint module disposed below the display screen. The fingerprint module is located at a position corresponding to the hole. The fingerprint module includes a light emitter and a light inductor, and a light signal emitted by the light emitter is transmitted to the fingerprint through the hole and the display layer, and is received by the light inductor after being reflected by the fingerprint. The present disclosure further provides a mobile terminal.

Abstract: The example embodiments are directed to refinement process for generating an accurate image segmentation map. A refinement network may enhance an initially generated segmentation map using a model that is trained using synthetic images. In one example, the method may include storing an image of content which includes a plurality of categories of data, receiving an initial segmentation map of the image, the initial segmentation map comprising pixel probability values with respect to the plurality of categories, executing a refinement predictive model on the initial segmentation map and the image to generate a refined segmentation map, wherein the predictive model is trained using synthetic images of the plurality of categories of data, and generating a segmented image based on the refined segmentation map.

Abstract: Road sign recognition method and system.

Abstract: Systems and methods are provided for presenting an interactive user interface that visually marks locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Annotation data generated by the machine learning models may be obtained, and one or more visual bounding shapes generated based on the annotation data. A user interface may present at least a portion of the radiograph's image data, along with display of the visual bounding shapes appearing to be overlaid over the at least a portion of the image data to visually mark the presence and location of a given pathology or other condition.

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: 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: 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: 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.