Image recognition and report generation is described. A camera component of a mobile device captures image frames of a physical environment. Each frame is run through an image recognition library to identify object(s) of interest. An object of interest is determined and it is an item associated with periodic maintenance. An identification of the identified object of interest is transmitted to a server. The mobile device receives information about the identified object of interest from the server that is based on a location of the mobile device, the information including a maintenance plan for the identified object of interest.

A method for detecting buried longitudinal structures using a ground-penetrating radar, the method includes the steps of: acquiring a plurality of radar signals for a region of ground, determining, based on the radar signals, a 3D point cloud, each point corresponding to one radar detection and being geolocated in space, selecting, from the 3D point cloud, at least one set of points comprising a number of points higher than or equal to a minimum detection threshold allowing a longitudinal structure to be characterized, the points of the set being substantially aligned with one another.

An object detection method of a vehicle lidar system according to an embodiment includes, in a contour of an object to be separated formed by connecting peak points among point data of the object to be separated, determining a contour line in which a connecting section of a contour line connecting the peak points includes point data equal to or less than a reference, and setting a separation reference line for separating the contour of the object to be separated into two regions while passing through the contour line including the point data equal to or less than the reference to recognize point data of the two regions as respective objects.

Generating a vector representation of a hand-drawn sketch is described. To do so, the sketch is segmented into different superpixel regions. Superpixels are grown by distributing superpixel seeds throughout an image of the sketch and assigning unassigned pixels to a neighboring superpixel based on pixel value differences. The border between each pair of adjacent superpixels is then classified as either an active or an inactive boundary, with active boundaries indicating that the border corresponds to a salient sketch stroke. Vector paths are generated by traversing edges between pixel vertices along the active boundaries. To minimize vector paths included in the vector representation, vector paths are greedily generated first for longer curves along active boundaries until each edge is assigned to a vector path. Regions encompassed by vector paths corresponding to a foreground superpixel are filled to produce a high-fidelity vector representation of the sketch.

Techniques are disclosed for using and training a descriptor network. An image may be received and provided to the descriptor network. The descriptor network may generate an image descriptor based on the image. The image descriptor may include a set of elements distributed between a major vector comprising a first subset of the set of elements and a minor vector comprising a second subset of the set of elements. The second subset of the set of elements may include more elements than the first subset of the set of elements. A hierarchical normalization may be imposed onto the image descriptor by normalizing the major vector to a major normalization amount and normalizing the minor vector to a minor normalization amount. The minor normalization amount may be less than the major normalization amount.

A first device may receive, from a second device, a request to approve a transaction wherein the request includes transaction data related to the transaction and an image of a signature of an individual that submitted the request. The first device may determine, after receiving the request, a priority level associated with the transaction based on the transaction data. The first device may process the image of the signature using a computer vision technique and/or a vector-based technique. The first device may select, from a memory storing a plurality of comparator signatures, a comparator signature for the signature based on the priority level. The first device may use the comparator signature to verify the signature to approve or deny the transaction. The first device may perform a comparison of the comparator signature and the signature in the image after processing the image and selecting the comparator signature.

Provided is a system capable of determining a stress coping style of a test subject in a non-contact state. The system includes a biological information acquiring part which acquires biological information of a test subject in a non-contact state, and a determining part which determines a stress coping style of the test subject based on the biological information and a response pattern specified in advance. The response pattern is specified by a hemodynamic parameter.

Methods and image processing systems are provided for determining a dominant gradient orientation for a target region within an image. A plurality of gradient samples are determined for the target region, wherein each of the gradient samples represents a variation in pixel values within the target region. The gradient samples are converted into double-angle gradient vectors, and the double-angle gradient vectors are combined so as to determine a dominant gradient orientation for the target region.

Devices, systems and methods for assessing anatomical surface features are described herein. In some embodiments, a method of assessing a surface feature on a patients skin surface includes (i) capturing one or more data sets from a patients skin surface including the skin surface feature, and (ii) determining outline data of the skin surface feature based on at least one of the one or more data sets. The method can further include determining one or more confidence attributes associated with the determined outline data.

The present disclosure relates to a method for controlling a movable platform. The method may include obtaining a target object selection operation input by a user on an interaction interface, the interaction interface displaying a three-dimensional model of an operation area, the target object selection operation configured to determine a position of a target object in the operation area; determining a target orientation of the movable platform when the target object is operated based on an orientation of the three-dimensional model displayed on the interaction interface when the target object selection operation is obtained; and determining a target position of the movable platform when the movable platform performs operation on the target object based on the position of the target object, the target orientation, and an operation distance of the movable platform.