Abstract: A method for assessing a condition of an organ or tissue of a target object is provided. The method may include: obtaining a target image of the target object; segmenting a target region from the target image, the target region of the target image corresponding to a sub-region of the organ or tissue; determining a morphological characteristic value of the target region in the target image; obtaining a reference standard associated with a sample organ or tissue of a plurality of sample objects, the sample organ or tissue being of a same type as the organ or tissue of the target object; and assessing the condition of the organ or tissue of the target object by comparing the morphological characteristic value of the target region in the target image with the reference standard.

Abstract: A biometric capture device is operative to adjust one or more environmental parameters to enhance a range (e.g., distance) at which a biometric may be captured from a subject. For example, a sample biometric capture device may be, include, or otherwise incorporate a retinal or iris scanner configured to capture an image of the retina or iris (e.g., a biometric) when the retina or iris is illuminated by infrared light. Generally, the amount of infrared light required to accurately image the retina or iris increases with the distance of the subject's retina or iris from the image capture device. The biometric capture device may capture a facial image using a first image sensor, identify a face in the facial image, capture an iris image using a second image sensor guided by the facial image, and identify a person using the iris image.

Abstract: Provided is an information processing apparatus that a user is able to acquire a file name the corresponds to content of a document image. In a case where the document image includes an image portion but does not include a character portion, a control unit uses a first learning model to acquire a classification name according to a characteristic of the image portion as a subject. In a case where a document image includes a character portion but does not include an image portion, the control unit uses a second learning model to acquire a classification name according to a character string included in the character portion as a subject. The control unit generates a file name including acquired subject as a file name of an image file.

Abstract: A system for object and information collection comprises a handheld tool to grab the object. The handheld tool comprises a trigger, a grabber mechanism, and a switch, wherein a user actuates the trigger to pick up an object with the grabber mechanism, and the switch causes transmission of a capture signal to initiate a classification event. A classification application executing on a processor is responsive to receiving the capture signal to capture information associated with the object and assign an object classification to the object based at least in part on the captured information.

Abstract: Embodiments described herein provide various examples of a machine-learning-based visual-haptic system for constructing visual-haptic models for various interactions between surgical tools and tissues. In one aspect, a process for constructing a visual-haptic model is disclosed. This process can begin by receiving a set of training videos. The process then processes each training video in the set of training videos to extract one or more video segments that depict a target tool-tissue interaction from the training video, wherein the target tool-tissue interaction involves exerting a force by one or more surgical tools on a tissue. Next, for each video segment in the set of video segments, the process annotates each video image in the video segment with a set of force levels predefined for the target tool-tissue interaction. The process subsequently trains a machine-learning model using the annotated video images to obtain a trained machine-learning model for the target tool-tissue interaction.

Abstract: Embodiments described herein provide a system for assessing the surface of an object for detecting contamination or other defects. During operation, the system obtains an input image indicating the contamination on the object and generates a synthetic image using an artificial intelligence (AI) model based on the input image. The synthetic image can indicate the object without the contamination. The system then determines a difference between the input image and the synthetic image to identify an image area corresponding to the contamination. Subsequently, the system generates a contamination map of the contamination by highlighting the image area based on one or more image enhancement operations.

Abstract: A method for multi-target detection and an apparatus for multi-target detection are capable of detecting at least two targets in real time or near real time. The real-time detection or near real time detection can be achieved by at least one of a Recipe Group Approach, an End Member Grouping Approach, and a Pixelated Grouping Based Approach.

Abstract: Devices, methods, and computer-readable media are disclosed describing an adaptive approach for image bracket selection and fusion, e.g., to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images, long exposure (or synthetic long exposure) images, EV0/EV? image pairs, etc., which are received according to a particular pattern. When a capture request is received, a set of rules and/or a decision tree may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. A noise reduction process may optionally be performed on the selected images before (or after) the registration and fusion operations.

Abstract: Devices and techniques are generally described for cross-domain machine learning. A first machine learning model may be trained using first data of a first domain. Predictions may be generated by inputting a plurality of domain data from other domains apart from the first domain into the first machine learning model. For each of the predictions, a prediction error may be determined. A grouping of similar domains from among the other domains may be determined based on the prediction errors. A second machine learning model may be trained for the grouping of similar domains.

Abstract: The present invention concerns the analysis of cell-free nucleic acids to determine the contribution of cell-free nucleic acids from specific tissues. In addition, the invention provides methods for diagnosing diseases based on cell-free nucleic acid analysis. The methods of the invention are also useful to detect quality defects in samples containing cell-free nucleic acids.

Abstract: In brain analysis, anatomical standardization is performed when analyzing a region of interest (ROI). There are individual differences in the shape and size of the brain and by converting the brain into a standard brain, these differences can be compared with each other and subjected to statistical analysis. When generating a standard brain analysis, a large number of pieces of image data are classified into a plurality of groups based on their anatomical features. An intermediate template that is an intermediate conversion image and a conversion map is calculated for each group, and the calculation of the intermediate template and the generation of the intermediate conversion image are repeated while gradually reducing the number of classifications, so that a final standard image is generated. Using the standard image and the intermediate template calculated during the generation of the standard image, spatial standardization of the measured image is performed.

Abstract: Systems and methods for orthognathic surgical planning are described herein. An example computer-implemented method can include generating a composite three-dimensional (3D) model of a subject's skull, defining a global reference frame for the composite 3D model, performing a cephalometric analysis on the composite 3D model to quantify at least one geometric property of the subject's skull, performing a virtual osteotomy to separate the composite 3D model into a plurality of segments, performing a surgical simulation using the osteotomized segments, and designing a surgical splint or template for the subject.

Abstract: According to the techniques of this disclosure, a method includes capturing, using a camera system of a vehicle, at least one image of an occupant of the vehicle, determining, based on the at least one image of the occupant, a location of one or more eyes of the occupant within the vehicle, and determining, based on the at least one image of the occupant, an eye gaze vector. The method may also include determining, based on the eye gaze vector, the location of the one or more eyes of the occupant, and a vehicle data file of the vehicle, a region of interest from a plurality of regions of interests of the vehicle at which the occupant is looking, wherein the vehicle data file specifies respective locations of each of the plurality of regions of interest, and selectively performing, based on the region of interest, an action.

Abstract: The present invention relates to a method for providing urinary stone information, and more particularly, to a method for providing urinary stone information, capable of providing information necessary for urinary stone surgery by detecting a region where a stone is present from a plurality of tomography images by using a machine learning model, and automatically extracting information including a location and a size of the stone.

Abstract: An image processing system acquires a scanned image obtained by scanning an original, and extracts a character region that includes characters from within the scanned image. The image processing system performs conversion processing, for converting a font of a character included in the extracted character region from a first font to a second font, on the scanned image using a conversion model for which training has been performed in advance so as to convert characters of the first font in an inputted image into characters of the second font and output a converted image. Then, the image processing system executes OCR on the scanned image after the conversion processing.

Abstract: Disclosed is a method of conditioning one or more parametric models. The method comprises obtaining a plurality of candidate parametric models, each describing a sequence of domains characterising a subsurface region and determining whether each sequence of domains described by one or more of said candidate parametric models is a valid sequence of domains. For each valid sequence of domains, each candidate parametric model describing that valid sequence of domains (or a subset of these models) is conditioned simultaneously, for example by using an Ensemble Kalman Filter or artificial neural network.

Abstract: A method of inspecting a component includes storing at least one inspection image file in a memory and receiving a search request associated with the at least one inspection image file. The method also includes accessing a database including a plurality of image files, comparing the hash code of the at least one inspection image file to the hash code of each image file of the plurality of image files, and identifying a first subset of image files based on the hash code comparison. The method also includes comparing the feature data of the at least one inspection image file to the feature data of each image file of the first subset of image files and classifying a second subset of image files as relevant based on the feature data comparison. The method further includes generating search results based on the second subset of image files.

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 neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.

Abstract: Systems and methods for authenticating handbags using a portable electronic device along with a bilinear convolutional neural network (CNN) model are described. One method includes using a portable electronic device comprising a camera, and a lens-accessory attached to the portable electronic device such that an optical feature of the lens-accessory is positioned in front of the camera. The portable electronic device acquires one or more pictures of a handbag and sends the one or more pictures to a bilinear CNN model via a network asset where an authenticity is determined. The systems and methods disclosed are capable of allowing the portable electronic device to be spaced apart from the handbag while acquiring pictures, and the lens-accessory can be between 10× and 50× magnification.