The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. Accurate intensity mappings facilitate accurate and robust normalization of sets of multiple digital images which, in turn, facilitates many additional types of operations carried out on sets of multiple normalized digital images, including change detection, quantitative enhancement, synthetic enhancement, and additional types of digital-image processing, including processing to remove artifacts and noise from digital images.

Systems and methods for generating a digital model of a space and modifying the digital model are described. In one aspect, a system includes one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations including: obtaining a point cloud representation of the space, the point cloud including multiple points; segmenting the point cloud into: (i) an inlier point cloud including multiple inlier points, and (ii) an outlier point cloud including multiple outlier points, where the segmenting includes: identifying, as the inlier points, the points of the point cloud that have at least a specified likelihood of being measurements of any of multiple planes of the space; and identifying, as the outlier points, all other points of the point cloud that are not identified as the inlier points; processing the inlier point cloud to generate a segmented inlier point cloud that includes, for each measured plane of the space, a respective plane point cloud representing the plane; and processing, using a neural network, the outlier point cloud to generate a segmented outlier point cloud that includes, for each of one or more objects detected in the space, a respective object point cloud representing the object.

Systems and methods for generating a digital model of a space and modifying the digital model are described. In one aspect, a system includes one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations including: obtaining a point cloud representation of the space, the point cloud including multiple points; segmenting the point cloud into: (i) an inlier point cloud including multiple inlier points, and (ii) an outlier point cloud including multiple outlier points, where the segmenting includes: identifying, as the inlier points, the points of the point cloud that have at least a specified likelihood of being measurements of any of multiple planes of the space; and identifying, as the outlier points, all other points of the point cloud that are not identified as the inlier points; processing the inlier point cloud to generate a segmented inlier point cloud that includes, for each measured plane of the space, a respective plane point cloud representing the plane; and processing, using a neural network, the outlier point cloud to generate a segmented outlier point cloud that includes, for each of one or more objects detected in the space, a respective object point cloud representing the object.

Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

System and techniques for detecting a crop related row from an image are described herein. An image that includes several rows—where the several rows including crop rows and furrows—can be obtained. The image can be segmented to produce a set of image segments. A filter can be shifted across respective segments of the set of image segments to get a set of positions. A line can be fit members of the set of positions, the line representing a crop row or furrow.

System and techniques for detecting a crop related row from an image are described herein. An image that includes several rows—where the several rows including crop rows and furrows—can be obtained. The image can be segmented to produce a set of image segments. A filter can be shifted across respective segments of the set of image segments to get a set of positions. A line can be fit members of the set of positions, the line representing a crop row or furrow.

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

To improve user convenience as to adjustment for vibration correction of a captured image captured by an image capturing apparatus, a feature portion evaluation unit refers to feature portions selected by a feature portion selection unit and determines whether feature portions necessary for vibration isolation have been acquired from a reference image. The feature portion evaluation unit has the function of notifying information about the feature portions of the reference image in a case where the acquired feature portions do not satisfy a predetermined condition, that is, in a case where the reliability of the acquired feature portions does not reach a predetermined level.

To improve user convenience as to adjustment for vibration correction of a captured image captured by an image capturing apparatus, a feature portion evaluation unit refers to feature portions selected by a feature portion selection unit and determines whether feature portions necessary for vibration isolation have been acquired from a reference image. The feature portion evaluation unit has the function of notifying information about the feature portions of the reference image in a case where the acquired feature portions do not satisfy a predetermined condition, that is, in a case where the reliability of the acquired feature portions does not reach a predetermined level.