An image processing apparatus comprises: a model obtaining unit configured to obtain a learned model that has learned, based on a position of a predetermined feature point, a contour of a target in an image obtained by capturing the target; an image obtaining unit configured to obtain an input image; a position obtaining unit configured to obtain a position of an input point input on the input image by a user; a normalization unit configured to obtain a normalized image generated by coordinate-transforming the input image such that the position of the input point matches the position of the predetermined feature point in the learned model; and an estimation unit configured to estimate the contour of the target in the input image using the normalized image and the learned model.

There is provided a method for generating a personalized Head Related Transfer Function (HRTF). The method can include capturing an image of an ear using a portable device, auto-scaling the captured image to determine physical geometries of the ear and obtaining a personalized HRTF based on the determined physical geometries of the ear.

A road waterlogging depth measurement method based on contour lines generated from point clouds includes the steps of: S1, acquiring road surface point cloud data using a laser scanner, and processing the acquired road surface point cloud data; S2, generating a relatively regular triangulation network using oracle transportation management (OTM) software based on the processed data, and constructing a digital elevation model (DEM); S3, drawing contour lines by integrating the DEM with Toggle Contours technology, and performing quality inspection and post-processing on the drawn contour lines; S4, performing projection transformation on a contour map; and S5, calculating a road waterlogging depth. According to the present disclosure, by adopting the above method, urban waterlogging risk management in urban management systems can be effectively addressed, and installation and maintenance costs can be saved.

A computing system including a communication interface and a processing circuit. The communication interface communicates with a robot and a camera having a field of view. The processing circuit performs obtaining image information based on objects within the field of view and determines a first template matching score which indicates a degree of match between the image information and an model template. The processing circuit further determines image edge information based on the image information and determines a second template matching score which indicates a degree of match between the image edge information and a template. The processing circuit additional determines an overall template matching score based on the first template matching score and the second template matching score.

A method for clustering phishing web resources based on visual content image, executed on a computer device comprising at least a processor and memory, and the method comprises the following steps: receiving references to a set of phishing web resources; retrieving at least one image of the visual content of each web resource of the set; processing the content of each visual content image associated with one of the set web resources, while contouring the elements on each image of the phishing web resource visual content; filtering the identified contours in each visual content image by removing the identical contours; combining the web resource associated with the compared contours and the cluster based on pairwise comparison of the identified contours and cluster contours, wherein, if the similarity value overrides the threshold value, otherwise, creating a new cluster for the web resource; storing references to web resources associated with corresponding contours of the content from a set of specified clusters in a database.

Methods and systems for improved generation and georeferencing of floor plans are presented. In one embodiment, a method is presented that includes receiving images that depict sheets of a blueprint of a structure. Subsets of the images depicting floor sheets and elevation sheets may be identified. Exterior contours may be extracted from the images depicting floor sheets and elevation contours may be extracted from the images depicting elevation sheets. A corresponding structure within a three-dimensional map may be identified based on the exterior contours and the elevation contours. A three-dimensional contour of the exterior of the structure may be extracted from the three-dimensional map.

A system and method for scoring trained probes for use in analyzing one or more candidate poses of a runtime image is provided. A set of probes with location and gradient direction based on a trained model are applied to one or more candidate poses based upon a runtime image. The applied probes each respectively include a discrete set of position offsets with respect to the gradient direction thereof. A match score is computed for each of the probes, which includes estimating a best match position for each of the probes respectively relative to one of the offsets thereof, and generating a set of individual probe scores for each of the probes, respectively at the estimated best match position.

A method, a learning model evaluation system, and a program can appropriately evaluate a learning model for identifying a subject to be detected. At least one embodiment of the method includes: by a computer, inputting a subject image in which at least one subject to be detected is imaged to a learning model and acquiring first contour information indicating a contour of the at least one subject to be detected in the subject image; acquiring second contour information indicating a contour of the at least one subject to be detected and serving as a reference for evaluating the first contour information; acquiring a condition to be satisfied by the first contour information; and generating, based on the first contour information, the second contour information, and the condition, evaluation information in which performance of the learning model is evaluated.

An imaging system registers image data (D.sub.img) in connection with an installation performing at least one action relating to an animal. A system for image data quality assurance contains a control unit and a digital storage unit. The control unit obtains image data (D.sub.img) registered by the imaging system when the installation is in an idle mode. The control unit analyzes the obtained image data (D.sub.img) to determine if a cleaning action to remove dirt (D) from a front window of the imaging system has been performed. If it is determined that such a cleaning action has been performed, control unit (120) causes a point in time for the cleaning action to be recorded in the digital storage unit (130) for use performance tracking of the installation in conjunction with the cleaning actions.

According to at least one embodiment, a method, a computer system, and a computer program product for virtual reality is provided. The present invention may include detecting a selection of a real-world object via a passthrough function of a virtual reality headset; analyzing a contour of the selected real-world object via a contour approximation method; creating a first layer that maintains passthrough function within the analyzed contour of the selected real-world object; creating a second layer that comprises a virtual simulated environment and one or more virtual objects; and displaying the first layer on top of the second layer by overwrapping the first layer and the second layer.