An apparatus, method and computer program is described comprising detecting a first object in a first image of a sequence of images using a neural network, wherein the means for detecting the first object provides an object area indicative of a first location of the first object; and tracking the first object, wherein the means for tracking the first object further comprises generating a predicted future location of the first object and generating an updated location of the first object using the neural network. The means for generating the predicted future location of the first object may, for example, receive said object area indicative of a first location of the first object and may receive said updated location information of the first object.

Systems, processes and methods for detecting rotated or angled text in an image based on global text geometry estimations are provided. A method includes, at an electronic device with memory and one or more processors, receiving an image including a plurality of pixels (802); determining, based on the image, one or more pixels of the plurality of pixels included in the image that contain text (804); identifying, based on the one or more pixels that contain text, a plurality of components in the image (810); determining a subset of components based on the plurality of components (814); determining, based on the pixels that contain text of the subset of components, one or more candidate text angles (816); determining a global text angle based on the determined one or more candidate text angles (824); and determining a first plurality of bounding boxes based on the global text angle (830).

An information processing device (2) includes a division unit (322) that divides an input image into a plurality of divided images having the same size, a rotation unit (324) that rotates, according to positions on the input image before the division, the plurality of divided images, and a recognition unit (325) that recognizes the plurality of divided images after the rotation.

An airport approach assistance system for aircraft includes a controller including electronic circuitry configured to: obtain a first image captured by a camera configured to capture images of a field of view ahead of the aircraft; obtain a second image stored in a database representing a runway where the aircraft is supposed to land; make changes to the first image and/or to the second image, in order to transpose them into a same spatial reference frame; extract a first region of interest from the first image, so as to isolate a landing area; compare the extracted first region of interest with a second region of interest associated with the second image; and generate a signal according to a comparison result. Thus, a pilot of the aircraft is assisted in confirming that the landing takes place on the expected landing runway.

An airport approach assistance system for aircraft includes a controller including electronic circuitry configured to: obtain a first image captured by a camera configured to capture images of a field of view ahead of the aircraft; obtain a second image stored in a database representing a runway where the aircraft is supposed to land; make changes to the first image and/or to the second image, in order to transpose them into a same spatial reference frame; extract a first region of interest from the first image, so as to isolate a landing area; compare the extracted first region of interest with a second region of interest associated with the second image; and generate a signal according to a comparison result. Thus, a pilot of the aircraft is assisted in confirming that the landing takes place on the expected landing runway.

A method for matching an image feature point, an electronic device, and a storage medium are provided. The method may include: for images in an acquired image sequence, performing operations including: obtaining a mapping image of a current image based on mapping transformation information between adjacent images before the current image; determining in the mapping image a target area for matching with a feature point in a last image frame prior to the current image; matching the feature point in the last image frame with a feature point in the target area corresponding to the feature point, to determine matching information between the feature point of the current image and the feature point of the last image frame; and determining mapping transformation information between the current image and the last image frame, based on the matching information.

A collation device includes a memory that stores, as a registration image group, a random pattern depending on a microscopic pattern on a side surface of a target having an elliptic cylindrical shape including a columnar shape, and a processor, in which the registration image group is composed of plural registration images respectively acquired at plural positions at a predetermined interval over a whole circumference of the side surface, and the processor executes a program to collate a collation image obtained by imaging the side surface at an angle of view including at least one registration image among the plurality of registration images and the registration image group and to output a collation result.

A neural network training method, an image processing method, and apparatuses thereof are provided. The neural network training method includes obtaining a first domain image and a second domain image, where the first domain image and the second domain image are unpaired images in different domains; obtaining a scaled first domain image by scaling, at an iteration, the first domain image; obtaining a training patch by cropping the scaled first domain image, where each training patch has a same number of pixels with different contents; inputting the training patch into the neural network at the iteration, and outputting an output patch; calculating a contrastive loss based on a query sub-patch and negative sub-patches selected from the training patch and a corresponding positive sub-patch selected from the output patch; and updating model parameters of the neural network based on the contrastive loss and a generative adversarial network loss.

The present invention relates to a method for producing a biometric image using an optical biometric imaging arrangement including a set of micro lenses adapted to redirect incoming light onto a photodetector pixel array, the method comprising: capturing a biometric image including a set of sub-images using an optical biometric imaging device; estimating a spatial transformation between the at least one biometric sub-image and corresponding masking calibration sub-images; applying the spatial transformation to retrieved illuminance calibration sub-images adapted for illuminance calibration for captured biometric sub-images to align the illuminance calibration sub-images with the biometric image; and normalizing the biometric sub-images using the aligned calibration sub-images for providing a normalized biometric image based on the normalized biometric sub-images.

The present invention relates to a method for producing a biometric image using an optical biometric imaging arrangement including a set of micro lenses adapted to redirect incoming light onto a photodetector pixel array, the method comprising: capturing a biometric image including a set of sub-images using an optical biometric imaging device; estimating a spatial transformation between the at least one biometric sub-image and corresponding masking calibration sub-images; applying the spatial transformation to retrieved illuminance calibration sub-images adapted for illuminance calibration for captured biometric sub-images to align the illuminance calibration sub-images with the biometric image; and normalizing the biometric sub-images using the aligned calibration sub-images for providing a normalized biometric image based on the normalized biometric sub-images.