Abstract: A system for incorporating geographical data into a map-related system, adding objects being tracked while moving within a specific geographic area and automatically analyzing their movement characteristics; wherein the tracking is done by visual means from a sky-borne platform.

Abstract: The present disclosure discloses a spatial positioning method, a spatial positioning device, a spatial positioning system and a computer readable medium. The spatial positioning method includes: capturing a feature image containing multiple feature markers of a to-be-positioned object; determining coordinate positions of respective feature markers in the feature image in an image coordinate system; traversing the respective feature markers in the image coordinate system according to preset multiple standard vector relations, and arbitrarily selecting at least three of the feature markers to calculate vector relations corresponding to the standard vector relations; determining position information of at least three feature markers according to position information corresponding to the standard vector relations in the image coordinate system when it is determined that the calculated vector relations satisfy any standard vector relation.

Abstract: A three-dimensional position estimation device includes: a feature point detection unit detecting feature points from images captured by an image capturing device mounted in a moving object; a position-posture change amount calculation unit calculating change amounts of a position and a posture of the moving object; a feature point association unit associating feature points using a feature amount indicating a feature of the feature point; a three-dimensional position acquisition unit acquiring three-dimensional positions of the feature points and acquires a position and a posture of the image capturing device; a graph structure data generation unit generating graph structure data configured with feature point and image nodes, and a feature point and image capturing edges; an error function setting unit setting first and second error functions; and a graph structure data optimizing unit optimizing the three-dimensional position and the position and the posture of the image capturing device.

Abstract: Methods for detecting digital or physical tampering of an imaged physical credential include the actions of: receiving a digital image representing a physical credential having one or more high value regions, the digital image including an array of pixels; processing the digital image with a tamper detector to generate an output corresponding to an intrinsic characteristic of the digital image, the tamper detector configured to perform a pixel-level analysis of the high value regions of the digital image with respect to a predetermined tampering signature; and determining, based on the output from the tamper detector, whether the digital image has been digitally tampered with.

Abstract: A method for super resolution of an image imitating optical zoom implemented on a resource-constrained mobile device is includes inputting a first-resolution image. The method also includes assigning a super resolution factor from a predefined set of super resolution factors that indicate increasing the first resolution to obtain a second-resolution image. The super resolution factor indicates a trained intelligent system imitating the operation of the corresponding optical system among a plurality of intelligent systems trained for each super resolution factor of the predefined set of super resolution factors. The method further includes transforming the first-resolution image into the second-resolution image using said trained intelligent system.

Abstract: An example apparatus for selecting keypoints in image includes a keypoint detector to detect keypoints in a plurality of received images. The apparatus also includes a score calculator to calculate a keypoint score for each of the detected keypoints based on a descriptor score indicating descriptor invariance. The apparatus includes a keypoint selector to select keypoints based on the calculated keypoint scores. The apparatus also further includes a descriptor calculator to calculate descriptors for each of the selected keypoints. The apparatus also includes a descriptor matcher to match corresponding descriptors between images in the plurality of received images. The apparatus further also includes a feature tracker to track a feature in the plurality of images based on the matched descriptors.

Abstract: A method, a device and a non-transitory computer readable medium for tracking poses of multiple objects. The method includes detecting, by a processor, objects from each of a plurality of consecutive frames in a video sequence; estimating, by the processor, a pose of each of the objects within each of the plurality of consecutive frames; and tracking, by the processor, the poses of each of the objects across the plurality of consecutive frames.

Abstract: A support program is a program for supporting creation of an interpretation report including a medical image obtained by examining a patient and a region of interest to focus on in the medical image.

Abstract: A list of possible mated minutiae in a reference fingerprint and a search fingerprint is identified. The list of possible mated minutiae is divided into different groups of mated minutiae using pose grouping based on rigid transformation parameters. For each group, the possible mated minutiae are filtered to get a set of consistent mated minutiae pairs having similar rigid transformation. The topological consistency between groups is determined. If any two groups are topologically consistent to each other, they are merged to form a (virtual) larger group. A non-linear transformation parameters are estimated from the mated minutiae in the merged group. The overall similarity of mated and non-mated minutiae in the overlapping area is determined after non-linear alignment of the two fingerprints, and the maximum similarity is output as the final similarity score between two fingerprints.

Abstract: An image processing method that generates high-resolution image data from low-resolution image data and an image receiving apparatus that is operated by the image processing method are provided. In the image processing method that generates high-resolution image data from low-resolution image data, the low-resolution image data is divided to generate a plurality of first image data, among the plurality of first image data, one of two adjacent image data is second image data, and the other is third image data. Surroundings of the second image data are supplemented with pixel data to generate fourth image data. The pixel data includes part of the third image data. A convolutional neural network using the fourth image data as an input is implemented, fifth image data is output from the convolutional neural network, and a plurality of the fifth image data is combined to generate high-resolution image data. The image receiving apparatus is operated by the image processing method.

Abstract: Correcting motion-based inaccuracy in point cloud data generated by one or more sensors carried by a scanning platform, and associated systems and methods are disclosed herein. A representative method includes associating a motion model with a target object of the point cloud, estimating adjusting factors based on the motion model, and adjusting scanning points in the point cloud using the adjusting factors.

Abstract: A system, device, and method for inspecting the cosmetic and operational features of electronic devices, including computing and telecommunications devices. The cosmetic inspection system includes an image capture unit for capturing the images of the electronic devices, and a user interface for processing the captured images and providing relevant information to the user of the system. Images of the external components such as external casing materials or touch screens of electronic devices are captured and the cosmetic inspection system uses baseline images to make determinations to identify defective components of the electronic devices. Based on these determinations, the system may conclude which, if any, replacement components of the devices are needed to restore the electronic device. In one embodiment, a user of the system may then be provided with information through a user interface about defective components and options for ordering replacement components.

Abstract: A motion detection method includes acquiring a raw image, detecting a motion object image according to the raw image by using a motion detector, cropping the raw image to generate a sub-image according to the motion object image, and inputting the sub-image to a processor for determining if a motion object of the sub-image matches with a detection category. The processor includes a neural network. The shape of the sub-image is a polygonal shape.

Abstract: Methods and systems for combining information present in measured images of semiconductor wafers with additional measurements of particular structures within the measured images are presented herein. In one aspect, an image-based signal response metrology (SRM) model is trained based on measured images and corresponding reference measurements of particular structures within each image. The trained, image-based SRM model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. In another aspect, a measurement signal synthesis model is trained based on measured images and corresponding measurement signals generated by measurements of particular structures within each image by a non-imaging measurement technique.

Abstract: Provided is an artificial intelligence (AI) decoding apparatus including a memory storing one or more instructions; and a processor configured to execute the one or more instructions to, when an image is input to a second DNN including a plurality of layers, obtain first result values based on an operation between the image and a first filter kernel and obtain second result values based on an operation between the image and a second filter kernel, from a first layer including the first and second filter kernels from among the plurality of layers, perform normalization by transforming the first result values into first values by using a first scale factor, and, perform normalization by transforming the second result values into second values by using a second scale factor, transform the first values and the second values into integer values included in a preset range.

Abstract: The present disclosure provides a region of interest (ROI) detection system. The system may be configured to acquire a target image and an ROI detection model, and perform ROI detection on the target image by applying the ROI detection model to the target image. The ROI detection model may be a trained cascaded neural network including a plurality of sequentially connected trained models. The plurality of trained models may include a trained first model and at least one trained second model downstream to the trained first model in the trained cascaded neural network. The plurality of trained models may be sequentially trained. Each of the trained second model may be trained using a plurality of training samples determined based on one or more trained models of the plurality of trained models generated before the generation of the trained second model.

Abstract: A computer-implemented method for detecting fraudulent behavior in a facial recognition process includes: receiving, by a computing device, a facial recognition request from a user; collecting bypass information of the user, in which the bypass information includes user device information and user behavior information; inputting the bypass information into at least one decision model to obtain a bypass decision result; and determining, based on the bypass decision result, whether fraudulent behavior is present in the facial recognition process.

Abstract: Subject matter regards improving image segmentation or image annotation. A method can include receiving, through a user interface (UI), for each class label of class labels to be identified by the ML model and for a proper subset of pixels of the image data, data indicating respective pixels associated with the class label, partially training the ML model based on the received data, generating, using the partially trained ML model, pseudo-labels for each pixel of the image data for which a class label has not been received, and receiving, through the UT, a further class label that corrects a pseudo-label of the generated pseudo-labels.

Abstract: Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to

Abstract: A terminal for receiving streaming data may receive information of a plurality of different quality versions of an image content; request, based on the information, a server for a version of the image content from among the plurality of different quality versions of the image content; when the requested version of the image content and artificial intelligence (AI) data corresponding to the requested version of the image content are received, determines whether to perform AI upscaling on the received version of the image content, based on the AI data; and based on a result of the determining whether to perform AI upscaling, performs AI upscaling on the received version of the image content through a upscaling deep neural network (DNN) that is trained jointly with a downscaling DNN of the server.