Abstract: An image processing method includes obtaining an input image, the input image includes object regions, and each object region includes at least one object; identifying the object regions to obtain object region labeling frames; establishing a first coordinate system based on the input image, the object region labeling frames are located in the first coordinate system; mapping the object region labeling frames from the first coordinate system to a second coordinate system according to a reference value to obtain first region labeling frames corresponding to the object region labeling frames, the first region labeling frames are located in the second coordinate system; performing expansion processing on the first region labeling frames to obtain expanded second region labeling frames; and mapping the second region labeling frames from the second coordinate system to the first coordinate system according to the reference value to obtain table region labeling frames.

Abstract: Methods for improving object recognition using depth data are disclosed. An image is captured of a 3-D scene along with depth data, such as in the form of a point cloud. The depth data is correlated with the image of the captured scene, such as by determining the frame of reference of each of the image and the depth data, thereby allowing the depth data to be mapped to the correct corresponding pixels of the image. Object recognition on the image is then improved by employing the correlated depth data. The depth data may be captured contemporaneously with the image of the 3-D scene, such as by using photogrammetry, or at a different time.

Abstract: Disclosed is a display condition analysis device which is capable of analyzing the display conditions of products. This display condition analysis device is provided with: a product recognition means for recognizing, from a display image taken of products on display, the products in the display image; and a display condition analysis means for analyzing, on the basis of the positions of the recognized products, the display conditions of the products on display.

Abstract: The present application discloses a method and apparatus for identifying an updated road, a device and a computer storage medium, and relates to the field of big data technologies. A specific implementation solution is as follows: comparing a road area extracted based on the latest satellite image with a road area extracted based on a historical satellite image, to obtain a candidate updated road; mapping the candidate updated road into road network data according to a coordinate position of the candidate updated road; acquiring a user trajectory set corresponding to the candidate updated road within a recent preset period; and identifying, based on a matching result between the user trajectory set and the road network data, whether the candidate updated road is an actual updated road. Updated roads can be more accurately identified through the method according to the present application.

Abstract: The present invention provides an unsupervised domain adaptive segmentation network comprises a feature extractor configured for extracting features from a 3D MRI scan image; a decorrelation and whitening module configured for preforming decorrelation and whitening transformation on the extracted features to obtain whitened features; a domain-specific feature translation module configured for translating domain-specific features from a source domain into a target domain for adapting the unsupervised domain adaptive network to the target domain; and a classifier configured for projecting the whitened features into a zonal segmentation prediction. By implementing the domain-specific feature translation module for transferring the knowledge learned from the labeled source domain data to unlabeled target domain data, domain gap between the source and target data can be narrowed.

Abstract: A computing system for generating predicted images along a trajectory of unseen viewpoints. The system can obtain one or more spatial observations of an environment that may be captured from one or more previous camera poses. The system can generate a three-dimensional point cloud for the environment from the one or more spatial observations and the one or more previous camera poses. The system can project the three-dimensional point cloud into two-dimensional space to form one or more guidance spatial observations. The system can process the one or more guidance spatial observations with a machine-learned spatial observation prediction model to generate one or more predicted spatial observations. The system can process the one or more predicted spatial observations and image data with a machine-learned image prediction model to generate one or more predicted images from the target camera pose. The system can output the one or more predicted images.

Abstract: The present invention is related to, among other things, the devices and methods that improve the accuracy and reliability of an assay, even when the assay device and/or the operation of the assay device has certain errors, and in some embodiments the errors are random.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: Medical software tools platforms utilize a surgical display to provide access to specific medical software tools, such as medically-oriented applications or widgets, that can assist surgeons or surgical team in performing various procedures. In particular, an endoscopic camera may register the momentary rise in the optical signature reflected from a tissue surface and in turn transmit it to a medical image processing system which can also receive patient heart rate data and display relevant anomalies. Changes in various spectral components and the speed at which they change in relation to a source of stimulus (heartbeat, breathing, light source modulation, etc.) may indicate the arrival of blood, contrast agents or oxygen absorption. Combinations of these may indicate various states of differing disease or margins of tumors, and so forth. Also, changes in temperatures, physical dimensions, pressures, photoacoustic pressures and the rate of change may indicate tissue anomalies in comparison to historic values.

Abstract: A system and a method for automatically identifying mitosis in H&E stained breast cancer pathological images are provided, belonging to the technical field of digital image processing, and including an input image preprocessing module: cutting an original picture according to a predetermined patch size, and performing a data enhancement by means of picture flipping, rotation, and the like; and a segmentation module: training a segmentation network by cutting patches in a training set, cutting data of a test set according to a corresponding size and sending to the segmentation network to obtain a patch-level segmentation result, and then reconstructing a segmented result into an image belonging to an original size according to patch coordinate information intercepted in a preprocessing stage of it.

Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.

Abstract: A system and method for authoring and implementing context-aware applications (CAPs) are disclosed. The system and method enables users to record their daily activities and then build and deploy customized CAPs onto augmented reality platforms in which automated actions are performed in response to user-defined human actions. The system and method utilizes an integrated augmented reality platform composed of multiple camera systems, which allows for non-intrusive recording of end-users' activities and context detection while authoring and implementing CAPs. The system and method provides an augmented reality authoring interface for browsing, selecting, and editing recorded activities, and creating flexible CAPs through spatial interaction and visual programming.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate sparse depth completion with semantic mesh deformation optimization in an augmented reality environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a depth completion component that generates a first depth map from an image and sparse depth samples. The computer executable components further comprise a semantic mesh deformation component that performs a semantic mesh deformation process, using the sparse depth samples, to generate a second depth map comprising a defined image accuracy.

Abstract: A joint training network including a multi-heard module comprising a network input, a feature network coupled to the network input and including a feature detector decoder outputting interest points and a descriptor generator decoder outputting descriptors, the feature detector decoder and the descriptor generator decoder coupled in parallel, a depth network including a monocular depth prediction decoder and outputting a depth map, a flow network including an image segmentation decoder and outputting a segmented image, a segmentation network including a warping module outputting a rotation and translation and an input warp signal to a segmentation decoder outputting a residual flow and a pose network including a fully connected pose estimator coupled to an adder that receives input from the pose estimator and the residual flow from the segmentation decoder, the adder outputting an optical flow.

Abstract: Various embodiments a method and apparatus for unmanned driving behavior decision-making and model training, and an electronic device. The method includes: acquiring sample data, wherein the sample data includes a sample image; extracting a sample feature vector corresponding to the sample data, wherein a feature vector of the sample image is extracted by manifold dimension reduction; and based on the sample feature vector, training by semi-supervised learning to obtain a target decision-making model, wherein the target decision-making model is used for decision-making classification.

Abstract: A control system for positioning a marker over a pre-existing roadway surface mark. The control system has one or more imagers having a field of view for imaging an area of roadway mark surface encompassing the roadway mark and a computer having a machine learning network to process the roadway mark image and position the marker over the pre-existing roadway mark.

Abstract: Methods, non-transitory computer-readable storage media, and computer or computer systems directed to detecting, analyzing, and tracking new residential or commercial construction activity using satellite or aerial imagery in combination with a machine learned model are described.

Abstract: A computer-implemented system and method for template-based imaging are disclosed. A first image of a first slide, a second image of a second slide, and a third image of a third slide are received, wherein the third image includes a representation of a sample disposed on the third slide. Values of pixels of a template image that are associated with a plurality of features common to the first and second slides and represented in the first and second images are set to a non-background value and values of pixels of the template image not associated with the plurality of features are set to a background value. An offset between the template image and the third image is developed and coordinates of pixels in the third image that are associated with the representation of the sample are determined in accordance with the offset.

Abstract: Systems and methods are provided for: receiving an image containing a code that has one or more visual qualities that fail to satisfy respective thresholds; applying a trained machine learning model to find a rough location of the code by generating a bounding box and cropping out the portion of the image; applying another trained machine learning model to the portion of the image to estimate key point locations of the code depicted in the portion of the image, aligning the portion of the image that depicts the code based on the estimated key point locations; and decoding, by the other trained machine learning model, the aligned portion of the image that depicts the code.

Abstract: Systems, methods of and computer program products for predicting and detecting the onset of glaucoma are provided.