Abstract: An image processing device is provided that can easily generate label data used for an object recognition technology using three-dimensional point cloud data. The image processing device (1) includes a three-dimensional point cloud obtaining unit (2), a two-dimensional label obtaining unit (4), and a label conversion unit (6). The three-dimensional point cloud obtaining unit (2) obtains three-dimensional point cloud data that represents a three-dimensional point cloud of an object. The two-dimensional label obtaining unit (4) obtains two-dimensional label data corresponding to a two-dimensional image of the object. The label conversion unit (6) associates the two-dimensional label data with the three-dimensional point cloud data, and converts the two-dimensional label data into three-dimensional point cloud label data that indicates a label of the three-dimensional point cloud.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that trains (and utilizes) an image color editing diffusion neural network to generate a color edited digital image(s) for a digital image. In particular, in one or more implementations, the disclosed systems identify a digital image depicting content in a first color style. Moreover, the disclosed systems generate, from the digital image utilizing an image color editing diffusion neural network, a color-edited digital image depicting the content in a second color style different from the first color style. Further, the disclosed systems provide, for display within a graphical user interface, the color-edited digital image.

Abstract: A system for monitoring surroundings of a vehicle includes a first imaging device provided on a side of a host vehicle to generate a first image of side and rear directions of the host vehicle; a second imaging device provided on a rear of the host vehicle to generate a second image of the rear direction of the host vehicle; and a control device configured to generate a synthesized image by synthesizing a reference image extracted from the first image and a modified image extracted from the second image. The control device is configured to generate the synthesized image in response to a distance between a counterpart vehicle corresponding to an object commonly included in the first image and the second image and the host vehicle becoming less than or equal to a first threshold distance.

Abstract: A method for refining virtually-stained image annotations. The method includes separating an image of a stained tissue section into an image of a counterstain and an image of immunostaining visual signals via linear transformation. The method includes identifying and segmenting nuclei in the image of the counterstain to produce an image of a refined counterstain with single-cell segments. The method includes comparing immunostaining visual signals in the single-cell segments of the image of the refined counterstain to a user-defined threshold to determine true positive cells. The method includes refining the image of the immunostaining visual signals to produce an image of refined immunostaining visual signals. The refining is based on the stained tissue section protein localization or morphology and the determined true positive cells. The method includes overlaying the image of the counterstain and the image of refined immunostaining visual signals to produce a refined virtually-stained image annotation.

Abstract: Systems and methods are provided for determining data associated with a computing record using a probabilistic model. An exemplary method includes: receiving at least one computing record; determining image data associated with the at least one computing record; generating, based on the image data, a relational graph; determining a rule associated with processing the at least one computing record; parsing the first rule; generating, based on the parsed rule, an abstract syntax tree; analyzing, based on a first computing record, the abstract syntax tree; generating, based on a computing analysis of the abstract syntax tree, a rule result associated with the rule; applying the rule to the at least one computing record using a probabilistic model; determining, based on the applying the rule to the at least one computing record, an indicator associated with rule results; determining whether the probabilistic model requires an update; and updating the probabilistic model.

Abstract: A method for model training, an image edge detection method, a multi-sensor calibration method, a computer program product, and a computer device is disclosed. The model is used to generate occlusion relationships between pixel pairs. The model training method comprises: constructing an initial model; obtaining multiple training images and reference annotation results for each training image, wherein each training image comprises RGB features and depth features, and the reference annotation results for each training image are annotation results of occlusion relationships between adjacent pixel pairs in said training image generated based on the depth features of said training image; respectively using the RGB features of the multiple training images as inputs to the initial model, using the reference annotation results corresponding to the input training images as outputs of the initial model, and training the initial model to obtain a well-trained model.

Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.

Abstract: The method and system for labeling 2D data using a 3D model provides processes that include generating Neural Radiance Fields (NeRF) of a desired scene, converting the NeRF into a mesh, rendering images of the mesh in a simulator from a user-chosen new camera pose including position and orientation, labeling the newly rendered images where bounding boxes for the images are created, rendering images using the NeRF from the same user-chosen new camera pose, and overlaying the bounding boxes from the simulator onto the imagery from the NeRF. The method and system provide capability of augmenting a set of real images to create a more diverse training dataset that captures the scene and its objects from various new camera positions, while maintaining high qualities of images.

Abstract: A method of environment mapping includes the steps of receiving a plurality of images from respective different viewpoints; using a simultaneous localisation and mapping ‘SLAM’ process, detect feature points in the plurality of images; classifying one or more objects within the plurality of images as being objects of interest; determining the position of the or each object of interest using feature points corresponding to the objects of interest in the images; and providing an indication of the position of one or more objects of interest to a user of a virtual display; and where the step of determining the position of the or each object of interest using their feature points is repeated, more frequently than for other feature points in the images.

Abstract: A model generation method for generating a video extraction model for extracting a matching interval in a video that matches contents of an input sentence is provided. In the model generation method, a base matching interval and a sub matching interval in a training video are extracted by inputting a base sentence and a sub sentence to the video extraction model. Next, a loss for each of ground truth interval, the base matching interval, and the sub matching interval is calculated by processing a learning task of reconstructing the base sentence based on a feature value of the training video corresponding to each of them. Then, a machine learning is performed such that the first loss related to the ground truth interval is smaller than the second loss related to the base matching interval, and the second loss is smaller than the loss related to the sub matching interval.

Abstract: This application provide an image processing method, and relate to the field of computer vision technologies, to perform repair processing on a color image in a fog/haze/smoke extreme weather condition. The image processing method includes: obtaining a first depth map and a color image, where the first depth map and the color image are images respectively shot by a depth sensor and a camera for a same scene; determining an atmospheric light value; performing depth completion on the first depth map to obtain a second depth map, and determining a transmittance based on the second depth map; and performing repair processing on the color image based on the atmospheric light value and the transmittance. The second depth map is used to determine the transmittance, and therefore, a transmittance of higher precision is obtained, thereby improving an image processing effect.

Abstract: An image processing apparatus is to: detect a first edge point group comprising a set of edge points between a document background area and a document area in a read image of a document; calculate feature amounts from a plurality of edge points in the first edge point group and compose an edge point feature amount group; calculate a distribution of the feature amounts of the plurality of edge points from the edge point feature amount group and calculate a feature amount corresponding to a maximum distribution; select edge points corresponding to the maximum distribution from the first edge point group and select a second edge point group using a range of the feature amount corresponding to the maximum distribution; and calculate an edge straight line approximating the second edge point group.

Abstract: An area of Barrett's oesophagus in a subject's oesophagus is quantified from a video image signal representing a video image of the subject's oesophagus captured using a camera of an endoscope. Depth estimation on the frames to derive depth maps in respect of frames of the video image. Regions of the frames corresponding to an area of Barrett's oesophagus in the subject's oesophagus are segmented. A value of a geometrical measure of the area of Barrett's oesophagus is calculated using the depth map and segmented region in respect of at least one of the frames.

Abstract: A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for selectively identifying image frames from an input video to provide to an image processing model based on camera statistics. For example, systems disclosed herein include receiving an input video and associated camera statistics from a video capturing device. The systems disclosed herein further include identifying select image frames to provide to the image processing model based on the camera statistics and based on an application of the image processing model. The systems disclosed herein further include selectively identifying and providing camera statistics to the image processing model. By selectively providing data to the image processing model based on camera statistics, the systems disclosed herein can leverage capabilities of video capturing devices to significantly reduce the expense of processing resources when utilizing a variety of image processing models.

Abstract: A method includes: receiving data signals from a plurality of pixels of an array of pixels; generating a plurality of signal-to-noise ratios by determining signal-to-noise ratios for each respective pixel of the plurality of pixels on the basis of the data signals received from the respective pixel; and filtering the data signals received from each pixel of the plurality of pixels by using an adaptive filter configured on the basis of the plurality of the signal-to-noise ratios to generate filtered data signals.

Abstract: According to various embodiments, an electronic device may comprise: a memory; a display; a plurality of cameras disposed below the display; and at least one processor operatively connected to the memory, the display, and the plurality of cameras, wherein the at least one processor is configured to, while controlling a plurality of pixel areas of the display, which correspond to the plurality of cameras, acquire a plurality of images of a user through the plurality of cameras, obtain a final image based on at least one image among the plurality of images, and authenticate the user based on the final image.

Abstract: Provided are an electronic devices and methods for processing an image through a canonical space, the method including receiving training images of a scene obtained from a first sensor, generating first conversion images by converting the training images into images corresponding to a scene obtained through a second sensor, generating second conversion images by converting a color value of the training images and a color value of the first conversion images, and training a canonical image generative model configured to convert the training images, the first conversion images, and the second conversion images into canonical space images of a canonical color space and a canonical illumination space.

Abstract: The present subject matter refers a method of image processing through an artificial neural network implemented in an adapter card in a host-computing system.

Abstract: A robotic system is configured to automatically identify surgical instruments used during a bronchoscopy procedure. The robotic system can include a video capture device, a robotic manipulator, sensors configured to detect a configuration of the robotic manipulator, and control circuitry communicatively coupled to the robotic manipulator. The control circuitry is configured to perform, using a machine learning classifier, a first analysis of a bronchoscopy video of a patient site to track a medical instrument in the bronchoscopy video. The control circuitry can then identify a set of possible instrument identifications for the medical instrument in the bronchoscopy video based on the first analysis and an identified phase of the bronchoscopy procedure. The control circuitry can then track a motion of the medical instrument in the bronchoscopy video and select an identification from the set of possible instrument identification for the medical instrument based at least on the tracked motion.