Abstract: An apparatus includes at least one processing device configured to obtain input frames from a video. The at least one processing device is also configured to generate a forward flow from a first input frame to a second input frame and a backward flow from the second input frame to the first input frame. The at least one processing device is further configured to generate an occlusion map at an interpolated frame coordinate using the forward flow and the backward flow. The at least one processing device is also configured to generate a consistency map at the interpolated frame coordinate using the forward flow and the backward flow. In addition, the at least one processing device is configured to perform blending using the occlusion map and the consistency map to generate an interpolated frame at the interpolated frame coordinate.

Abstract: A method includes obtaining multiple video frames. The method also includes determining whether a bi-directional optical flow between the multiple video frames satisfies an image quality criterion for bi-directional consistency. The method further includes identifying a non-linear curve based on pixel coordinate values from at least two of the video frames. The at least two video frames include first and second video frames. The method also includes generating interpolated video frames between the first and second video frames by applying non-linear interpolation based on the non-linear curve. In addition, the method includes outputting the interpolated video frames for presentation.

Abstract: This application discloses a secure face image transmission method performed by an electronic device. In this application, when a camera component of the electronic device acquires any face image, face image information can be read from a buffer. The face image information is used for indicating a quantity of all face images historically acquired by the camera component. Identification information is embedded in the face image to obtain the face image carrying the identification information. The identification information is used for indicating the face image information but not perceivable by a human being. The face image carrying the identification information is transmitted to a remote server for authenticating the face image using the identification information. Therefore, the security of the face image acquired by the camera component is improved, and the security of the face image transmission process is effectively ensured.

Abstract: A processor-implemented method with pose estimation includes: tracking a position of a feature point extracted from image information comprising a plurality of image frames, the image information being received from an image sensor; predicting a current state variable of an estimation model for determining a pose of an electronic device, based on motion information received from a motion sensor; determining noise due to an uncertainty of the estimation model based on a residual between a first position of the feature point extracted from the image frames and a second position of the feature point predicted based on the current state variable; updating the current state variable based on the current state variable, the tracked position of the feature point, and the noise; and determining the pose of the electronic device based on the updated current state variable.

Abstract: A system is provided for performing a validation of an examination environment. The system acquires a video of the examination environment. The system applies one or more machine learning models to images (frames) of the video to indicate whether the image includes a prohibited item. A machine learning model may be trained using images of items labeled with an indication of whether an image includes a prohibited item. The system determines whether the validation has passed based on whether an image includes a prohibited item. The system notifies a proctor of when the validation has not passed and provides to the proctor an indication of an image that contains a prohibited item. The proctor then decides whether the validation should pass or fail.

Abstract: In some examples, a computerized system for analyzing images comprises at least one programmable processor and a machine-readable medium having instructions stored thereon which, when executed by the at least one programmable processor, cause the at least one programmable processor to execute operations comprising training an autoencoder using a plurality of image model training samples, the autoencoder comprising a plurality of interconnected layers and combined instances of neural networks, passing input data into a trained autoencoder model, the input data including at least one pixel image, encoding the input data into a compressed version of the input data, and decoding the compressed version of the input data to generate to create an output, the output including a sparse reconstruction of the input data, the output including a predicted pixel image label or score.

Abstract: An object tracking apparatus (2000) detects an object from video data (12) and performs tracking processing of the detected object. The object tracking apparatus (2000) detects that a predetermined condition is satisfied for a first object (20) and a second object (30), using the video data (12). Thereafter, the object tracking apparatus (2000) performs tracking processing of the second object (30) in association with the first object (20).

Abstract: A face authentication apparatus includes a face image acquisition unit that acquires, as a first face image, a face image of a user who moves from a first area to a second area via a gate provided between the first area and the second area, a collation unit that performs face authentication, a flow rate measurement unit that measures a flow rate of users who move from the first area to the second area via the gate and a flow rate of users who move from the second area to the first area via the gate, and a security level determination unit that determines the security level of the first area and the security level of the second area on the basis of the flow rates measured.

Abstract: According to one embodiment, a reading system includes a processing device. The processing device includes an extractor, a line thinner, a setter, and an identifier. The extractor extracts a partial image from an input image. A character of a segment display is imaged in the partial image. The segment display includes a plurality of segments. The line thinner thins a cluster of pixels representing a character in the partial image. The setter sets, in the partial image, a plurality of determination regions corresponding respectively to the plurality of segments. The identifier detects a number of pixels included in the thinned cluster for each of the plurality of determination regions, and identifies the character based on a detection result.

Abstract: An object recognition method is provided. The method includes: detecting an occlusion region of an object in an image, to obtain a binary image; obtaining occlusion binary image blocks; querying a mapping relationship between occlusion binary image blocks and binary masks included in a binary mask dictionary to obtain binary masks corresponding to the occlusion binary image blocks; synthesizing the binary masks queried based on each of the occlusion binary image blocks, to obtain a binary mask corresponding to the binary image; and determining a matching relationship between the image and a prestored object image, based on the binary mask corresponding to the binary image, a feature of the prestored object image, and a feature of the to-be-recognized image.

Abstract: An image encoding method including: a constraint information generating step of generating tile constraint information indicating whether or not there is a constraint in filtering on boundaries between adjacent tiles among a plurality of tiles obtained by dividing a picture, and storing the tile constraint information into a sequence parameter set; and a filter information generating step of generating, for each of the boundaries, one of a plurality of filter information items respectively indicating whether or not filtering is executed on the boundaries, and storing the plurality of filter information items into a plurality of picture parameter sets, wherein, in the filter information generating step, the plurality of filter information items which indicate identical content are generated when the tile constraint information indicates that there is the constraint in the filtering.

Abstract: In an object recognition apparatus, a storage unit stores a table in that a plurality of feature amounts are associated with each object having feature points of respective feature amounts. An object region detection unit detects object regions of a plurality of objects from an input image. A feature amount extraction unit extracts feature amounts of feature points from the input image. A refining unit refers to the table, and refines from all objects of recognition subjects to object candidates corresponding to the object regions based on feature amounts of feature points belonging to the object regions. A matching unit recognizes the plurality of objects by matching the feature points belonging to each of the object regions with feature points for each of the object candidates, and outputs a recognition result.

Abstract: A method of generating a correction plan for a knee of a patient includes obtaining a ratio of reference bone density to reference ligament tension in a reference population. A bone of the knee of the patient may be imaged. From the image of the bone, a first dataset may be determined including at least one site of ligament attachment and existing dwell points of a medial femoral condyle and lateral femoral condyle of the patient on a tibia of the patient. Desired positions of contact in three dimensions of the femoral condyles of the patient with the tibia of the patient may be obtained by determining a relationship in which a ratio of bone density to ligament tension of the patient is substantially equal to the ratio of reference bone density to reference ligament tension.

Abstract: A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

Abstract: A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

Abstract: The present disclosure provides a tissue density analysis system. The system includes an acquisition module configured to obtain image data and tissue density distribution data; a display module configured to display the obtained tissue density distribution data in one or more charts; a processing module configured to adjust the tissue density distribution data displayed in the one or more charts; and a storage module configured to store the image data, the tissue density distribution data and an instruction.

Abstract: A method for extracting text from an input image and generating a document includes: generating an edges mask from the input image; generating an edges image that is derived from the edges mask; identifying, within the edges mask, one or more probable text areas; extracting a first set of text characters by performing a first optical character recognition (OCR) operation on each of one or more probable text portions, of the derived edges image, corresponding to each of the probable text areas; generating a modified image by erasing, from the input image, image characters corresponding to the first set of text characters extracted by the first OCR operation; and generating a document by overlaying the extracted first set of text characters on the modified image.

Abstract: The disclosed invention includes systems and methods for training and employing equivariant models for generating representations (e.g., vector representations) of temporally-varying content, such as but not limited to video content. The trained models are equivariant to temporal transformations applied to the input content (e.g., video content). The trained models are additionally invariant to non-temporal transformations (e.g., spatial and/or color-space transformations) applied to the input content. Such representations are employed in various machine learning tasks, such as but not limited to video retrieval (e.g., video search engine applications), identification of actions depicted in video, and temporally ordering clips of the video.

Abstract: Methods and systems for calibrating radiological data associated with a tissue in a subject are provide. Various embodiments described herein analyze a radiological image of the tissue to calculate a raw value indicative of a biological parameter such as metabolic activity in the tissue, and analyze a microscopy image of the tissue to identify a distribution of cells of interest within the tissue. A calibrated value indicative of a biological parameter (e.g. metabolic activity) of cells of interest within the tissue is then calculated, by correcting the raw value based on the identified distribution of cells of interest within the tissue.

Abstract: Aspects relate to systems and methods for the application of computer vision and sensor fusion to assist in the management and operation of a facility. For large facilities, many of the expenses and staffing requirements incurred such as energy, custodial duties, maintenance and security can scale with size rather than usage, and therefore be subject to gross inefficiencies. These challenges may arise from a lack of timely information available with which to make such optimizations and improvements. The approaches disclosed leverage recent advancements in computer vision technology to extract actionable information from raw sensor data collected through-out the facility. This information may be processed and applied in either an autonomous, semi-autonomous, or user driven approach to control and manage a number of processes occurring regularly within a facility, such as lighting, cleaning, and security.