A visual web page analysis system includes an image analyzing unit, a block analyzing unit, a vision identifying unit, and an output unit. The image analyzing unit loads information of a web page and segments content of the web page into a plurality of blocks based on a visual feature. The block analyzing unit classifies the blocks based on an attribute of each block. The vision identifying unit compares at least a relative feature of each block to determine a function of each block on the web page. The output unit collects the blocks and their functions into an information interface and outputs the information interface.

A method for analyzing performance of an imaging device including a scanner with a phantom includes receiving image data related to a scanning, by the scanner, of a first part of the phantom including at least part of a first test component. The method also includes obtaining at least one positioning parameter indicative of a positioning manner of the phantom during the scanning. The method further includes generating a first test image based on the received image data and determining a first region of interest (ROI) related to the first test component in the first test image based on the at least one positioning parameter.

A computer-implemented system for automatic identification and classification of an insect that has a device connected to a camera, a processor, and a non-transitory machine-readable medium having instructions stored therein, which when executed by the processor, cause the processors to perform the following operations: receiving a live-time image of the insect captured by the camera, determining from the live-time image an irrelevant image data and a relevant image data, determining identification and classification of the insect from the relevant image data by using a convolutional neural network (CNN) module, and providing an alert based on the identification and classification of the insect.

Disclosed is a computer-implemented method of determining a spatial relationship between planning image data and current surface data which leads to improved surface registration accuracy by considering the elasticity and deformability of the tissue. The knowledge about the tissue can be estimated based on type of tissue and atlas information. For the process of generating surface registration points on specific anatomical regions, e.g. the face or forehead, are acquired with a classical navigated pointer or laser pointer. It is also possible to acquire points with surface scanners. Confidence values defining a probability for certain parts of the surface registration points being deformed in comparison to a planning image are read from atlas data and used to compensate for the deformation in the registration between the surface registration points and the planning image in order to render the registration valid.

A system for creating a composite georeferenced, geo-oriented geospatial/realtime image by combining prestored geographic data with realtime video streams and state data characterizing the source of the realtime video stream and visually displaying the composite image under a user's realtime control.

A rice cooker assembly uses machine learning models to identify and classify different types of food stored. The rice cooker has a chamber including different compartments for storing different types of food. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model classifies the different types of food stored. The rice cooker determines a mixture of different types of food based on nutrition value and/or taste. The rice cooker creates the mixture and controls the cooking process accordingly. The one or more machine learning models may be resident in the rice cooker or it may be accessed via a network.

Aspects of the disclosure relate to placing labels on photographic images so as not to obstruct a particular object. An image associated with a geolocation is identified, where the geolocation is based on a user action. The image is analyzed to identify at least first and second objects within the image. Object types for each object are determined. A determination is made that the first object is to be annotated with a label according to the first object type, and that the first and second object types differ. The image is also analyzed to determine a position on the second object to place the label so that the label does not obstruct the first object. Based on this analysis, the label is placed on the second object without obstructing the first object.

A localization device and a localization method for a vehicle are provided. The localization device includes an inertia measurer, an encoder, an image capturing device, and a processor. The processor obtains an encoded data by the encoder to generate a first odometer data, obtains an inertial data by the inertia measurer to generate a heading angle estimation data, and obtains an environmental image data by the image capturing device to generate a second odometer data. In a first fusion stage, the processor fuses the heading angle estimation data and the first odometer data to generate first fusion data. In a second fusion stage, the processor fuses the first fusion data, the heading angle estimation data and the second odometer data to generate pose estimation data corresponding to the localization device.

A method including acquiring a captured image of an object with a camera, detecting a first pose of the object on the basis of 2D template data and either the captured image at initial time or the captured image at time later than the initial time, detecting a second pose of the object corresponding to the captured image at current time on the basis of the first pose and the captured image at the current time, displaying an AR image in a virtual pose based on the second pose in the case where accuracy of the second pose at the current time falls in a range between a first criterion and a second criterion; and detecting a third pose of the object on the basis of the captured image at the current time and the 2D template data in the case where the accuracy falls in the range.

Provided is a monitoring system, comprising a imaging-side device, and a matching-side device which receives image data from the imaging-side device and matches the image data. The imaging-side device wirelessly transmits, to the matching-side device, feature value data which denotes feature of an image subject. Upon receiving the feature value data, and on the basis of the result of a comparison between the feature value data and feature value reference data, the matching-side device wirelessly transmits image data request information to the imaging-side device. Upon receiving the image data request information, the imaging-side device wirelessly transmits image data to the matching-side device.