Abstract: An image processor includes a boundary line detection portion configured to detect a boundary line by using an image in accordance with an image signal acquired by an imaging device, a parking frame detection portion configured to detect a parking frame by using the detected boundary line, an in-frame scanning portion configured to acquire a dividing point that divides a pair of facing sides of the boundary lines that define the parking frame and to scan the image through the dividing point to detect an edge, a storage portion configured to store a state of an edge and an attribute of the parking frame by linking them, and a determination portion configured to determine an attribute of the parking frame by using a state of the detected edge and the stored attribute of the parking frame that is linked to the state of the edge stored in the storage portion.

Abstract: Provided are a depth image processing method, a depth image processing apparatus, an electronic device and a readable storage medium. The method includes: (101) obtaining consecutive n depth image frames; (102) determining a trusted pixel and determining a smoothing factor corresponding to the trusted pixel; (103) determining a time similarity weight; (104) determining a content similarity; (105) determining a content similarity weight based on the content similarity and the smoothing factor; and (106) performing filtering processing on a depth value of the trusted pixel based on all time similarity weights and all content similarity weights.

Abstract: In one implementation, a method includes: obtaining a first depth estimation characterizing a distance between the device and a surface in a real-world environment, wherein the first depth estimation is derived from image data including a representation of the surface; receiving, using the audio transceiver, an acoustic reflection of an acoustic wave, wherein the acoustic wave is transmitted in a known direction relative to the device; and determining a second depth estimation based on the acoustic reflection, wherein the second depth estimation characterizes the distance between the device and the surface in the real-world environment; and determining a confirmed depth estimation characterizing the distance between the device and the surface based on resolving any mismatch between the first depth estimation and the second depth estimation.

Abstract: A computer-implemented method for image capture by a mobile device, comprising: receiving, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device; identifying a specific frame of the video stream; generating a plurality of hypotheses defining image borders within the specific frame; selecting, by a neural network, a particular hypothesis among the plurality of hypotheses; producing a candidate image by applying the particular hypothesis to the specific frame; determining a value of a quality metric of the candidate image; determining that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream; wherein the image capture application is a zero-footprint application.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image having a plurality of object instances; encode the image to obtain image features; decode the image features to obtain object features; generate object detection information based on the object features using an object detection branch, wherein the object detection branch is trained based on a first training set using a detection loss; generate semantic segmentation information based on the object features using a semantic segmentation branch, wherein the semantic segmentation branch is trained based on a second training set different from the first training set using a semantic segmentation loss; and combine the object detection information and the semantic segmentation information to obtain panoptic segmentation information that indicates which pixels of the image correspond to each of the plurality of object instances.

Abstract: Embodiments of this disclosure include a training method for an image processing model. In the method, parameters of an encoder in the image processing model are updated according to a to-be-replaced face in an original image, to configure the encoder to encode the to-be-replaced face to obtain a visual feature of the to-be-replaced face. Parameters of a decoder in the image processing model are updated according to the to-be-replaced face in the original image, to configure the decoder to perform decoding based on the visual feature of the to-be-replaced face. The parameters of the decoder are further updated according to a target face in a target image without changing the parameters of the encoder, to configure the decoder to perform decoding based on the visual feature of the to-be-replaced face and obtain a target face having the same visual feature as the to-be-replaced face.

Abstract: A fingerprint image processing device includes a memory, and a processor coupled to the memory. The processor performs operations. The operations include reading a tenprint card image which includes a plurality of fingerprint patterns and at least one ruled line to separate one fingerprint imprint area from another fingerprint imprint area, and extracting from the tenprint card image a fingerprint image which includes at least one of the fingerprint patterns, apart of a fingerprint imprint area, and a part of a next fingerprint imprint area.

Abstract: Disclosed is method, a user interface, a computer program product and a system for predicting future development of a dental condition of a patient's set of teeth, wherein the method comprises:—obtaining two or more digital 3D representations for the teeth recorded at different points in time;—deriving based on the obtained digital 3D representations a formula expressing the development of the dental condition in terms of at least one parameter as a function of time; and—predicting the future development of the condition based on the derived formula.

Abstract: According to one or more embodiments, an image processing device includes a storage device and a processor. The storage device stores a plurality of time-series images. The processor extracts one or more feature points of a first image of the time-series images, sets a search range for one or more corresponding points of the feature points in a second image of the time-series images, searches for the one or more corresponding points in the search range of the second image, if there are a plurality of corresponding points searched, associates the searched corresponding points with the feature points based on a positional relationship between the corresponding points.

Abstract: Techniques for facilitating image color correction are provided. In one example, a method includes receiving an image. The method further includes determining, based at least on the image, a first scaling value and a second scaling value. The method further includes applying the first scaling value to the image to obtain a scaled image. The method further includes applying a color correction matrix (CCM) to the scaled image to obtain a CCM image. The method further includes applying the second scaling value to the CCM image to obtain a color corrected image. Related devices and systems are also provided.

Abstract: Disclosed herein are methods, systems, and apparatus for automated license plate recognition and methods for training a machine learning model to undertake automated license plate recognition. For example, a method can comprise dividing an image or video frame comprising a license plate into a plurality of image patches, determining a positional vector for each of the image patches, adding the positional vector to each of the image patches and inputting the image patches and their associated positional vectors to a text-adapted vision transformer. The text-adapted vision transformer can be configured to output a prediction concerning the license plate number of the license plate.

Abstract: Computer-implemented methods and systems for colour-based image tagging and colour-based searching. The method may include identifying, using image analysis, one or more dominant colours of a product based on an image of the product and receiving selection of at least one of the one or more dominant colours. In response to receiving the selection of the at least one of the one or more dominant colours, a search for products matching the at least one of the one or more dominant colours may be initiated to obtain one or more results of the searching, the one or more results including at least one product matching the at least one of the one or more dominant colours.

Abstract: A system and method for measuring distances related to a target object depicted in an image. A captured digital image is obtained containing a scene with a target object whose dimension is to be measured. The digital image may contain a target object dimension identified by one or more ancillary objects and a reference object in the same or different planes. Image processing is performed to find the reference object using known fiducial patterns printed on the reference object, metadata supplied by a user and/or by the detection of colored papers in the scene of the captured image. Once located and measured, the reference object is used to calculate a pixel scale factor used to measure the target object dimensions. Target object dimensions are provided to an automated or semi-automated measurement process, design and manufacturing system such that customized parts for a supplemental fenestration are provided to end users.

Abstract: Embodiments of this disclosure include a method and an apparatus for segmenting a medical image. The method may include obtaining a slice pair comprising two slices and performing feature extraction on each slice in the slice pair, to obtain high-level feature information and low-level feature information of the each slice in the slice pair. The method may further include segmenting a target object in the each slice according to the low-level feature information and the high-level feature information of the slice, to obtain an initial segmentation result of the each slice and fusing the low-level feature information and the high-level feature information of the slices to obtain a fused feature information. The method may further include determining correlation information between the slices according to the fused feature information and generating a segmentation result of the slice pair based on the correlation information and the initial segmentation results of the slices.

Abstract: A device to image registration method includes obtaining image data of a device, the device including a plurality of fiducials arranged as a fiducial frame on the device; detecting fiducial objects within the image data; cropping the image data to generate cropped image data; applying a feature enhancement to the cropped image data to enhance the fiducial objects; generating a candidate list of candidate objects based on the feature enhancement, and determining a common plane based on at least three points in the candidate list; determining a representative point for each candidate object; determining outlier candidate objects based on the common plane; determining resultant objects by extracting the outlier candidate objects from the candidate list; and registering the resultant objects to the device by matching the representative points of the resultant objects with a model of the fiducial frame.

Abstract: Techniques are described for the inpainting of an image with a missing region. In an embodiment, the process transforms an input image tensor of the image into a spectral tensor of the image. A machine learning-generated filter is applied to the spectral tensor of the image. Additionally or alternatively, a spectral tensor is spectrally split into at least high-frequency and low-frequency sub-tensors. The output tensor(s) are then inverse-transformed into the spatial domain, yielding pixel data for the unknown region that accurately regenerates both low-level detail information as well as high-level detail information.

Abstract: Techniques for texture-based authentication of digital identity documents are disclosed. The disclosed techniques include authentication based on global texture information and/or on local texture information. Global texture-based authentication may include generating a global texture profile for an identity document image and comparing the global texture profile with a stored profile associated with a jurisdiction class of the identity document. Local texture-based authentication may include generating one or more local texture patches representative of texture information of one or more select local blocks of the ID. The one or more local texture patches are provided as input to one or more local detectors each trained to detect the presence of a forgery based on a target manipulation space.

Abstract: A system for determining candidate answers during an interview. The system may include a memory storing executable instructions, and at least one processor configured to execute the instructions to perform operations. The operations may include capturing, by an image sensor, a plurality of images comprising a plurality of candidate answers; detecting, based on the captured images, a first bounding box comprising the plurality of the candidate answers; detecting, based on the captured images, a second bounding box comprising a plurality of alphanumeric characters, a first one of the alphanumeric characters being located on a line of software code; calculating a distance from a side of a first bounding box to a side of a second bounding box; and determining, based on the calculated distance, a source code alignment.

Abstract: Systems and methods of the present disclosure can facilitate determining a three-dimensional surface representation of an object. In some embodiments, the system includes a computer, a calibration module, which is configured to determine a camera geometry of a set of cameras, and an imaging module, which is configured to capture spatial images using the cameras. The computer is configured to determine epipolar lines in the spatial images, transform the spatial images with a collineation transformation, determine second derivative spatial images with a second derivative filter, construct epipolar plane edge images based on zero crossings of second derivative epipolar planes image based on the epipolar lines, select edges and compute depth estimates, sequence the edges based on contours in a spatial edge image, filter the depth estimates, and create a three-dimensional surface representation based on the filtered depth estimates and the original spatial images.

Abstract: The present disclosure describes systems and methods to correct for perspective calibration variations of a variable thickness specimen with a single camera extensometer in a video extensometer system. In some examples, the systems and methods compensate for a change between a reference characteristic, such as a calibration plane, and an actual physical characteristic, such as a testing plane associated with a surface of a test specimen, during a testing operation. In some examples, a correction value is applied to an output (e.g., measured dimensions of the imaged test specimen) to compensate for the difference between the reference characteristic and the physical characteristic.