Abstract: An image processing apparatus, according to one embodiment of the present invention, comprises: a receiver for receiving a first image and a second image; a first processor for outputting a third image by using at least one of the first image and the second image; a signal line bypassing at least one of the first image and the second image; a switch for connecting a signal output from the receiver to the first processor or the signal line; and a transmitter for transmitting at least two of the first to third images.

Abstract: A false video detection method is provided. The method includes: retrieving N video segments in a target video, wherein each video segment includes M image frames; performing a feature retrieving procedure on the N×M image frames to obtain feature maps corresponding to the N×M image frames; inputting the feature maps into a time-attention model to obtain concentrated feature; multiplying the concentrated feature with the feature maps to obtain first enhanced feature maps, adding the first enhanced feature maps with the feature maps to obtain second enhanced feature maps, and inputting the second enhanced feature maps into a fully connected layer to obtain a determination result outputted by the fully connected layer, wherein the determination result indicates that the target video is true or false.

Abstract: The present invention provides an image processing apparatus that inspects an image formed on a printing medium. The image processing apparatus reads an image formed on a printing medium and obtains a target image to be inspected, and corrects a position of control point among control points disposed on the target image, at or near an edge of the printing medium, and performs alignment using the corrected position of the control point so as to align the target image to a reference image using a non-rigid-body alignment, and inspects for a defect in the target image on a basis of the aligned target image and the reference image. In the correcting, a position of a control point at or near the edge is corrected on a basis of an interval between adjacent control points at or near the edge.

Abstract: A method of image correlation includes: obtaining a first image frame with a set of distinct features; obtaining a second image frame at least partially overlapping the first image frame; carrying out a cross-correlation operation of the first and second image frames to obtain a cross-correlation result indicating correlation values for different offset positions of the second image frame with respect to the first image frame, the highest correlation value with its associated offset position in the cross-correlation result indicating the most likely offset position of the second image frame with respect to the first image frame; and carrying out a reaggregation operation of the cross-correlation result to generate a distinct global maximum correlation value from a plurality of non-distinct correlation values in an aggregated cross-correlation result. The reaggregation operation involves carrying out a convolution of the cross-correlation result with a 1×2, 2×1, or 2×2 array of 1's.

Abstract: An image processing apparatus, according to one embodiment of the present invention, comprises: a receiver for receiving a first image and a second image; a first processor for outputting a third image by using at least one of the first image and the second image; a signal line bypassing at least one of the first image and the second image; a switch for connecting a signal output from the receiver to the first processor or the signal line; and a transmitter for transmitting at least two of the first to third images.

Abstract: Region-of-interest (ROI)-based image enhancement using a residual network, including: generating, based on an input image and a residual path of a residual network, a first output corresponding to a region-of-interest of the input image; generating, based on the input image and a skip path of the residual network, a second output; and generating an output image based on the first output and the second output.

Abstract: Embodiments of this application disclose an image processing method and apparatus, and a computer-readable storage medium. The method includes acquiring an image pair to be processed, and performing image feature extraction on an image to be processed in the image pair to be processed to obtain an image; extracting an association feature from the image feature, the association feature characterizing mutual information between images in the image pair to be processed; identifying a common view image of a common view region in the image to be processed according to the association feature, and calculating a scale difference between common view images; adjusting a size of the common view image based on the scale difference to obtain an adjusted common view image; and extracting at least one common view feature point from each of the adjusted common view images, and processing the image based on the common view feature point.

Abstract: A method of compensating for focus drift in a microscopy system includes receiving data representing a reference light signal reflected from a sample and received by an image sensor, identifying from the data a peak of the reference light signal, the peak having a maximum intensity value for the reference light signal, extracting a sub-region having a center from the data, the center being located at the peak, calculating a first harmonic of a Fourier transform of the extracted sub-region, estimating a peak position of the reference light signal based on a phase of the first harmonic, and moving the lens system of the microscopy system or the sample in the axial direction using the translation stage based on the estimated position. Also, a system compensating for focus drift in a microscopy system according to the above method.

Abstract: An apparatus for estimating a road edge includes a processor configured to estimate a trajectory of a vehicle, estimate the position of an edge of a road from images generated during travel of the vehicle by a camera, identify an undetected section when an image from which the edge of the road is not detected was generated, determine whether the road in the undetected section has a structure with a road edge, estimate the position of the edge of the road in the undetected section, based on the positions of the edge of the road in the sections in front of and behind the undetected section, when the road in the undetected section has a structure with an edge, and omit to estimate the position of the edge of the road in the undetected section when the road in the undetected section has a structure without an edge.

Abstract: In a feature data generation system, an edge pattern indicating a boundary of a target feature existing around a vehicle is extracted from position data of a large number of measurement points surrounding the vehicle measured using a LIDAR technology, on the basis of image data of surroundings of the vehicle obtained by imaging so as to reduce the amount of information, positions in a terrestrial reference frame are assigned to the extracted edge pattern, and a shape characteristic vector and a feature characteristic vector to be used for generating or updating a feature in a feature database, are generated from the edge pattern to which the positions in the terrestrial reference frame are assigned.

Abstract: Disclosed is an image processing apparatus including a receiving section that receives a captured image of a target object, an instruction section that gives a user an instruction on which direction to capture an image of the target object, an inferencing section that performs an inference of an imaging direction on the basis of the received captured image, and a verifying section that performs predetermined verification processing related to the target object on the basis of an imaging direction as the instructed direction and the imaging direction as a result of the inference.

Abstract: Systems and methods for vision test and uses thereof are disclosed. A method may be implemented on a mobile device having at least a processor, a camera and a display screen. The method may include capturing at least one image of a user using the camera of the mobile device; interactively guiding the user to a predetermined distance from the display screen of the mobile device based on the at least one image; presenting material on the display screen upon a determination that the user is at the predetermined distance from the display screen; and receiving input from the user in response to the material presented on the display screen. The material presented on the display screen may be for assessing at least one characteristic of the user's vision. Mobile devices and non-transitory machine-readable mediums having machine-executable instructions embodied thereon for assessing a user's vision also are disclosed.

Abstract: This disclosure enables various technologies for analyzing behaviors of objects or with respect to objects based on stereo imageries thereof. For example, such analysis may be useful in enforcement of certain actions by objects or with respect to objects, surveillance of objects or with respect to objects, or other situations involving analyzing behaviors of objects or with respect to objects.

Abstract: This application discloses a face image processing method performed by an electronic device. The method includes: acquiring a face image of a source face and a face template image of a template face; performing three-dimensional face modeling on the face image and the face template image to obtain a three-dimensional face image feature of the face image and a three-dimensional face template image feature of the face template image; fusing the three-dimensional face image feature and the three-dimensional face template image feature to obtain a three-dimensional fusion feature; performing face replacement feature extraction on the face image based on the face template image to obtain an initial face replacement feature; transforming the initial face replacement feature based on the three-dimensional fusion feature to obtain a target face replacement feature; and replacing the template face with the source face based on the target face replacement feature to obtain a target face image.

Abstract: A first frame of a video stream is obtained. The first frame is defined by a plurality of pixels associated with a set of color data. A determination is made that a pixel of the plurality of pixels comprises high-frequency information. Responsive to the determination that the pixel comprises high-frequency information, a pixel lock is generated for the pixel such that color data associated with the pixel is maintained during a color accumulation process for at least one of the first frame or a second frame of the video stream that is subsequent to the first frame.

Abstract: Aspects of the subject technology relate to systems, methods, and computer-readable media for identifying roll, pitch, and azimuth angles for an object based on surface normal vectors. Image data tracking a surface as an object moves relative to the surface can be accessed. A reference normal vector of the surface can be identified. A dynamic normal vector of the surface associated with the object moving over the surface can be identified from the image data. One or a combination of roll, pitch, and azimuth of the object with respect to the surface can be determined based on the reference normal vector and the dynamic normal vector.

Abstract: An image signal indicating an inference target image in which a detection target appears is acquired when a domain of the inference target image is different from a domain of a training image or a recognition task of the inference target image is different from a pre-learned task. The image signal is provided to a trained learning model, and from the learning model, an inference time feature amount obtained by combining feature amounts of the detection target in the inference target image after the feature amounts is blurred is acquired. The detection target in the inference target image is recognized on the basis of a representative feature amount that is a registered feature amount of the detection target in an image for conversion in which a domain and a recognition task of the image are the same as those of the inference target image, and the inference time feature amount.

Abstract: A system, method and non-transitory computer readable storage medium for aligning a set of medical images. The operations for aligning the medical images include receiving a set of medical images, selecting anchor organs from the medical images, training a segmentation model to identify the anchor organs in the medical images based on a training dataset and generating, based on the segmentation model, a segmentation mask for the anchor organs. The operations also include computing image coordinates for the anchor organs in each of the medical images based on a center of mass of each of the anchor organs, determining a correlation between the image coordinates for the anchor organs in each of the medical images and corresponding anatomical coordinates for the anchor organs in the training dataset and aligning each of the set of medical images based on the correlation between the image coordinates and the anatomical coordinates.

Abstract: Provided is an object detection method including detecting a first object by using an object detection filter in an original image and images of which resolutions are downscaled and detecting a second object by using a neural network-based object detection model of which an input is a target image that is determined to be one of the original image and the images of which resolutions are downscaled.

Abstract: The present disclosure relates to systems and methods for object recognition. The systems may obtain image data captured by an imaging device. The image data may include one or more objects. The systems may determine a centerline of a target object in the one or more objects based on the image data. The systems may determine a recognition result of the target object using a trained neural network model based on at least one feature parameter of the centerline of the target object. The recognition result may include a name of the target object. The systems may perform an anomaly detection on the target object based on the recognition result of the target object.