A computer-implemented method including receiving a first representation of a geographical polygon defining a parcel of land, which first representation includes latitude and longitude coordinates that represent at least the corners of the geographical polygon; based on the first representation, determining various features of the geographical polygon; and preparing a second representation of the geographical polygon, which second representation includes the geometric centre of geographical polygon, first two alphanumeric characters, second two alphanumeric characters, a fifth alphanumeric character, and a sixth alphanumeric character.

Embodiments of this application provide example target detection methods and apparatuses. One target detection method includes obtaining an image by using a photographing apparatus. A region of interest can be marked in the image based on a parameter of the photographing apparatus and a preset traveling path. The image can be detected by using a target detection algorithm to obtain a category to which a target object in the image belongs, a first location region of the target object in the image, and a confidence of the category. The confidence of the category can be modified, based on a relative location relationship between the first location region and the region of interest, to obtain a first confidence.

An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

A method for detecting spoof fingerprints with an under-display fingerprint sensor includes illuminating, with incident light emitted from a display, a target region of a fingerprint sample disposed on a top surface of the display; detecting a first scattered signal from the fingerprint sample with a first image sensor region of an image sensor located beneath the display, the first image sensor region not directly beneath the target region, the first scattered signal including a first portion of the incident light scattered by the target region; determining a scattered light distribution based at least in part on the first scattered signal; and identifying spoof fingerprints based at least in part on the scattered light distribution.

A method for detecting spoof fingerprints with an under-display fingerprint sensor includes illuminating, with incident light emitted from a display, a target region of a fingerprint sample disposed on a top surface of the display; detecting a first scattered signal from the fingerprint sample with a first image sensor region of an image sensor located beneath the display, the first image sensor region not directly beneath the target region, the first scattered signal including a first portion of the incident light scattered by the target region; determining a scattered light distribution based at least in part on the first scattered signal; and identifying spoof fingerprints based at least in part on the scattered light distribution.

There is provided a system and method for mask inspection, comprising: obtaining a plurality of images, each representative of a respective part of the mask; generating a CD map of the mask comprising a plurality of composite values of a CD measurement of a POI respectively derived from the plurality of images, comprising, for each given image: dividing the given image into a plurality of sections; searching for the POI in the plurality of sections, giving rise to a set of sections, each with presence of at least one of the POI therein; for each section, obtaining a value of the CD measurement using a printing threshold, giving rise to a set of values of the CD measurement corresponding to the set of sections; and combining the set of values to a composite value of the CD measurement corresponding to the given image.

An inspection apparatus selects at least one character area, in a first preview image obtained by reading and previewing a print product, sets a direction, for a character in the selected character area, registers the set direction and the character in the selected character area in association with each other, selects at least one character inspection area, in a second preview image obtained by reading and previewing a print product as an inspection target, sets a direction, for a character in the selected character inspection area, rotates the character inspection area to match the set direction, with the direction set for the character in the selected character area, performs character recognition, for the character in the rotated character inspection area, and inspects the character inspection area, based on a result of the character recognition and a result of recognizing the character in the selected character area.

In an obstacle detection apparatus, the captured image of a vicinity of a vehicle from an imaging apparatus is acquired. A three-dimensional estimation image showing a three-dimensional position of a feature point in the captured image is generated, and a three-dimensional position of an object is estimated. An attribute image in which an object in the captured image is classified into one or more classes that include at least a road-surface class is generated. The three-dimensional estimation image and the attribute image are fused, the feature points and the classes are associated, and road-surface points associated with the road-surface class are extracted. Based on the road-surface points, a road-surface height in the vicinity of the vehicle is estimated. Based on the estimated road-surface height, the three-dimensional position of the object is corrected. Based on the three-dimensional position of the object, an obstacle in the vicinity of the vehicle is detected.

A method for training a deep learning network for face recognition includes: utilizing a face landmark detector to perform face alignment processing on at least one captured image, thereby outputting at least one aligned image; inputting the at least one aligned image to a teacher model to obtain a first output vector; inputting the at least one captured image a student model corresponding to the teacher module to obtain a second output vector; and adjusting parameter settings of the student model according to the first output vector and the second output vector.