Abstract: Systems and/or techniques for facilitating image forgery detection via headpose estimation may include a system that can receive a document from a client device. The system can identify, by executing a first trained machine learning model, an object that is depicted in the document. The system can determine, by executing a second trained machine learning model, a pose of the object. The system can determine, by executing a third trained machine learning model, whether the document is authentic or forged based on the pose of the object. The system can, in response to determining that the document is forged, transmit an unsuccessful validation message to the client device.

Abstract: An apparatus where a vertical image position of the scene point may depend on the horizontal image position. A mapper generates a modified image having a modified projection by applying a mapping to the first wide angle image corresponding to a mapping from the first projection to a perspective projection followed by a non-linear vertical mapping from the perspective projection to a modified vertical projection of the modified projection and a non-linear horizontal mapping from the perspective projection to a modified horizontal projection of the modified projection. Then a disparity estimator generates disparities for the modified image relative to a second image and representing a different view point than the first wide angle image.

Abstract: Disclosed is an object interaction method, comprising: determining a pose of a terminal device under a real environment coordinate system; according to pixel information and depth information of a first object, determining a pose of the first object relative to the terminal device; according to the pose of the terminal device under the real environment coordinate system and the pose of the first object relative to the terminal device, determining a pose of the first object under the real environment coordinate system; determining a pose of a second object under the real environment coordinate system; and according to the pose of the first object under the real environment coordinate system and the pose of the second object under the real environment coordinate system, controlling the first physical object and the second virtual object to interact with each other.

Abstract: An image segmentation method system, the system comprising: a training subsystem configured to train a segmentation machine learning model using annotated training data comprising images associated with respective segmentation annotations, so as to generate a trained segmentation machine learning model; a model evaluator; and a segmentation subsystem configured to perform segmentation of a structure or material in an image using the trained segmentation machine learning model. The model evaluator is configured to evaluate the segmentation machine learning model by (i) controlling the segmentation subsystem to segment at least one evaluation image associated with an existing segmentation annotation using the segmentation machine learning model and thereby generate a segmentation of the annotated evaluation image, and (ii) forming a comparison of the segmentation of the annotated evaluation image and the existing segmentation annotation.

Abstract: A device may receive training data for training a first machine learning model, a second machine learning model, and a third machine learning model and may train the first machine learning model, the second machine learning model, and the third machine learning model with the training data. The device may receive input content and may process the input content, with the first machine learning model, the second machine learning model, and the third machine learning model, to generate a first model result, a second model result, and a third model result, respectively. The device may process the first model result, the second model result, and the third model result, with an aggregation model, to generate a detection result indicating whether the particular input content is a deepfake or is real and may perform one or more actions based on the detection result.

Abstract: A system, neural network, and corresponding method generate 3D landmarks associated with an object in a 2D image. An embodiment is a system comprising a neural network detector configured to produce planar coordinates of landmarks at points of the object in the 2D image and a depth coordinate estimator. The planar coordinates include planar coordinate pairs. The depth coordinate estimator is configured to receive the 2D image and the planar coordinates and to estimate a depth coordinate for each planar coordinate pair of each landmark to generate the 3D landmarks. The system reduces network parameters from MB to KB and has better performance relative to state-of-the-art methods. The system may be configured to apply the 3D landmarks for face alignment, virtual face makeup, face recognition, eye gaze tracking, face synthesis, or other face related application.

Abstract: The embodiments of the disclosure provide an object tracking method and an object tracking device. The method includes: in a tracking state, controlling an image-capturing device to capture a first image of a specific object, wherein a plurality of light emitting elements are disposed on the specific object, and at least one first light emitting element of the light emitting elements is on and captured in the first image; determining a first object pose of the specific object based on the at least one first light emitting element of the light emitting elements in the first image; obtaining at least one second light emitting element of the light emitting elements based on the first object pose, wherein the at least one second light emitting element is estimated to be uncapturable by the image-capturing device; and turning off the at least one second light emitting element.

Abstract: A document liveness detection method comprising using a hardware processor for prompting an end-user of an electronic device, having a camera, to provide an image sequence of a physical document whose orientation is undergoing change; and image-processing the image sequence to yield an output indication of whether or not the image sequence was generated by capturing a live or physical document; and providing the output indication to at least one computerized system or human.

Abstract: This disclosure provides a method performed by a portable computer device configured to generate an augmented reality video. The device detects first object proposal region information, based on a frame of a video, using a first trained model configured to provide object proposal regions having an accurate width. The device also detects, based on the frame of the video, second object proposal region information using a second trained model configured to provide object proposal regions having an accurate height. The device then determines combined object proposal region information by combining overlapping object proposal regions of the first and second object proposal region information and generates an augmented reality video by generating an augmented frame.

Abstract: A method of identifying a particular mask for a patient for use in delivering a flow of breathing gas to the patient is carried out by first receiving a 2-D frontal image of the patient. Next, 3-D facial information of the patient is determined from the 2-D frontal image. At least some of the 3-D facial information is compared with dimensional information of a plurality of candidate masks. Finally, the particular mask for the patient is determined from a result of the comparison of the at least some of the 3-D facial information and the dimensional information of the plurality of candidate masks.

Abstract: Embodiments of the present application provide a detection method, a detection device, and a storage medium. The detection method may comprise: obtaining a to-be-detected image; obtaining a plurality of confidence levels corresponding to a plurality of detection items according to the to-be-detected image; and determining a detection result of the to-be-detected image according to the plurality of confidence levels and a plurality of detection thresholds, where the plurality of detection thresholds may be corresponding to the plurality of detection items and may be a non-inferior solution of objective functions of an overkill rate and a missed detection rate, the overkill rate may be the ratio of qualified ones detected as defective, and the missed detection rate may be the ratio of defective ones detected as qualified.

Abstract: Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.

Abstract: A content aware and style aware neural network based data augmentation model generates augmented data sets to train neural network based handwriting recognition models to recognize individuals' handwriting. The augmented data sets may be generated so as to be artificial, and to lack personal or confidential information. The data augmentation model may generate content reference sets of individual characters generated in different fonts, and style reference sets of pluralities of characters of a particular style, for example, an individual's handwriting.

Abstract: Voltage supply system with boost converter and charge pump. A voltage supply system can include a boost converter controllable to receive an input voltage at an input node and generate an output voltage when the output voltage is greater than or equal to the input voltage. The voltage supply system can include a charge pump controllable to receive the input voltage at the input node and generate the output voltage when the output voltage is less than the input voltage. The voltage supply system can further include a controller configured to receive a control signal and control the boost converter or the charge pump to generate the output voltage at an output node based on the control signal.

Abstract: A keyboard file verification method based on image processing comprises controlling a processor to perform following operations: obtaining a keyboard file; generating, according to the keyboard file, a search index and a feature image; obtaining a template image from a template database according to the search index; performing a calibration operation according to the feature image, wherein the calibration operation comprises: adjusting a resolution of the feature image according to a resolution of the template image; performing a shifting operation according to the feature image, to generate a plurality of candidate images; and comparing a key block of each of the plurality of candidate images with a key block of the template image to generate a difference map and a comparison result.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image embedding model. In one aspect, a method comprises: obtaining training data comprising a plurality of training examples, wherein each training example comprises: an image pair comprising a first image and a second image; and selection data indicating one or more of: (i) a co-click rate of the image pair, and (ii) a similar-image click rate of the image pair; and using the training data to train an image embedding model having a plurality of image embedding model parameters.

Abstract: A system and method for training a neural network is implemented for detecting at least one entity in a document to derive relevant inferences therefrom. The method describes obtaining at least one document. The at least one document is processed, via a detection module, to detect a widget entity. The detected widget entity is classified as active or inactive based on a detected state of the widget entity. The classified widget entity is modified into a corresponding machine-readable widget-entity based on the detected state. The at least one document is processed, via an extraction module, to detect a text entity in near vicinity of the classified widget entity. A training pair comprising the machine-readable widget entity and the corresponding text entity is generated. The neural network is trained using the generated training pair.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing medical chest images such as chest X-ray (CXR) images. Segmentation models derived via a deep learning process are used to segment the chest images and obtain a rib segmentation result and a lung segmentation result for each image. The rib segmentation result may include a rib sequence identified in the image while the lung segmentation result may include one or more lung fields identified in the image. The quality of each chest image (e.g., whether the image reflects a breath-holding state of the patient) may then be determined based on whether a sufficient number of ribs in the rib segmentation result overlap with the lung fields in the lung segmentation result. The segmentation results may be obtained in a coarse-to-fine manner, e.g., by first determining a large rib area and then further segmenting the large rib area to identify each individual rib.

Abstract: There is provided an information processing apparatus that includes a display controlling section that causes a display device to start displaying a first virtual object while a user is moving in a real space, performs distance keeping control to substantially keep a distance between the user and the first virtual object, ends the distance keeping control in a case where a predetermined condition is satisfied, and changes the distance between the user and the first virtual object.

Abstract: The method includes receiving a plurality of digital images each depicting a tissue sample; splitting each of the received images into a plurality of tiles; automatically generating tile pairs, each tile pair having assigned a label being indicative of the degree of similarity of two tissue patterns depicted in the two tiles of the pair, wherein the degree of similarity is computed as a function of the spatial proximity of the two tiles in the pair, wherein the distance positively correlates with dissimilarity; and training a machine learning module—MLM—using the labeled tile pairs as training data to generate a trained MLM, the trained MLM being configured for performing an image analysis of digital histopathology images.