A method, an apparatus, a device and a storage medium for processing an image are provided. The method may include: acquiring a target image; determining at least one stamp image included in the target image; determining position information of a character in the at least one stamp image; and determining a text in the at least one stamp image based on the position information.

A system and method of executing a deep neural network (DNN) in a local area network (LAN) may include executing a partitioned deep neural network in multiple computational nodes (CPs) in devices operating on the LAN. An image frame may be captured by a device. The image frame may be processed by a first layer of the partitioned neural network by a CP operating on the device. In response to the device that captured the image frame determining to request processing assistance from another CP, a request using a peer-to-peer protocol to other CPs on the LAN may be performed. A feature map may be communicated to another CP selected using the peer-to-peer protocol to process the feature map by a next layer of the DNN.

A search device identifies names of POI from a document group having not been determined. A storage unit that stores a POI presence/absence learning model having learned contexts relating to presence/absence of POI, a POI state learning model having learned contexts relating to states of POI, and a POI name learning model having learned features relating to names of POI, an acceptance unit that accepts a first document group that is a determination target, first and second determination units and an identifying unit that identifies a name of a POI using the POI name learning model from each document of a third document group for which information relating to states of POI is determined by the second determination unit in a second document group are included.

The present invention discloses an identification system which includes an image sensor, a storage unit and a comparing unit. The image sensor captures a plurality of images of the motion trajectory generated by a user at different timings. The storage unit has stored motion vector information of a group of users including or not including the user generating the motion trajectory. The comparing unit compares the plurality of images with the motion vector information to identify the user. The present invention also provides an identification method.

According to one embodiment of the present invention, a method for coding an iris pattern divides an iris area into a plurality of sectors on the basis of the assumption that a user's pupil and iris are not circular and then can code an iris pattern included in each sector. According to the present invention, an error occurrence frequency can be minimized compared with a case that an iris pattern is coded on the basis of the assumption that a pupil and an iris are circular.

A computer-implemented method of preoperatively planning a surgical procedure on a knee of a patient including determining femoral condyle vectors and tibial plateau vectors based on image data of the knee, the femoral condyle vectors and the tibial plateau vectors corresponding to motion vectors of the femoral condyles and the tibial plateau as they move relative to each other. The method may also include modifying a bone model representative of at least one of the femur and the tibia into a modified bone model based on the femoral condyle vectors and the tibial plateau vectors. And the method may further include determining coordinate locations for a resection of the modified bone model.

Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.

A method of recognizing a feature of an image may include receiving an input image including an object; extracting first feature information using a first layer of a neural network, the first feature information indicating a first feature corresponding to the input image among a plurality of first features; extracting second feature information using a second layer of the neural network, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and recognizing an element corresponding to the object based on the first feature information and the second feature information.

A vehicle is provided that may distinguish between dynamic obstacles and static obstacles. Given a detector for a class of static obstacles or objects, the vehicle may receive sensor data indicative of an environment of the vehicle. When a possible object is detected in a single frame, a location of the object and a time of observation of the object may be compared to previous observations. Based on the object being observed a threshold number of times, in substantially the same location, and within some window of time, the vehicle may accurately detect the presence of the object and reduce any false detections.

In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.