Systems, methods, and computer program products can be used for determining the amount of oil removed by a miscible gas flood. One of the methods includes identifying locations of oil within a volume representing a reservoir rock sample. The method includes identifying locations of gas within the volume. The method also includes determining the amount of oil removed based on locations within the volume where oil is either coincident with the gas or is connected to the gas by a continuous oil path.

Systems and methods are disclosed, including a non-transitory computer readable medium storing computer executable instructions that when executed by a processor cause the processor to identify a first image, a second image, and a third image, the first image overlapping the second image and the third image, the second image overlapping the third image; determine a first connectivity between the first image and the second image; determine a second connectivity between the first image and the third image; determine a third connectivity between the second image and the third image, the second connectivity being less than the first connectivity, the third connectivity being greater than the second connectivity; assign the first image, the second image, and the third image to a cluster based on the first connectivity and the third connectivity; conduct a bundle adjustment process on the cluster of the first image, the second image, and the third image.

Described here are systems and methods that utilize visual imagery and an optical flow-based computer vision algorithm to measure river velocity in streams or other flowing bodies of water. The systems and methods described in the present disclosure overcome the barriers of conventional flow measurement techniques by providing a fast, non-intrusive, remote method to measure peak flows.

Sparse phase unwrapping is disclosed. A first image and a second image are received. The first image and the second image are coregistered. The first image and the second image comprise respective phase data. An unwrapped interferogram is generated, including by solving an optimization problem using a nonconvex penalty function, where minimizing the penalty function produces sparse minimizers.

A geospatial modeling system may include a memory and a processor cooperating therewith to generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images, obtain a newly collected geospatial image, and determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the newly collected geospatial image. The processor may further align the newly collected geospatial image and the reference geospatial image to generate a predictively registered image, and update the 3D geospatial model based upon the predictively registered image.

The present disclosure relates to a computer-implemented method, devices and systems for determining a location associated with a gemstone. That method includes training a data model to detect a characteristic associated with a feature of interest pertaining to a gemstones surface. The training including the steps of providing a plurality of training images of gemstones, each training image associated with a label for a feature of interest pertaining to a given region of a gemstone type and an output indicating the specific gemstone type for the label. An input image of a gemstone for which a location is to be determined is then provided to the trained data model, wherein the data model detects at least one characteristic in the input image that corresponds to a feature of interest in the plurality of training images, determines that characteristic in the input image is associated with the label in the trained data model that corresponds to said feature of interest, identifies that the gemstone in the input image is of the specific gemstone type associated with said label, and finally provides an output representation of the respective feature of interest of the respective region for the identified specific gemstone type. Then, based on the output representation, a location on a corresponding region of the gemstone associated with the input image can be determined

The present technology relates to an image processing method and a data structure of metadata that allow for image processing on a plurality of captured images obtained by operation of a formation flight. A satellite cluster management device and an image analysis server as image processing apparatuses perform predetermined image processing on the basis of satellite specification information for specifying an artificial satellite associated as metadata with a captured image captured by the artificial satellite. The present technology can be applied to, for example, artificial satellites that perform satellite remote sensing by a formation flight.

A modulator of a distance measuring sensor unit 102 has a photographing pattern that includes a plurality of patterns different from each other, and modulates intensity of light. A gesture detecting unit divides image data obtained by receiving light transmitted through the modulator by the image sensor; restores an image by calculation based on a developing pattern corresponding to the photographing pattern in a divided unit; and obtains an image for detecting gesture of a user. This makes it possible to obtain an image of an object in an outside world without using a lens for forming an image. Therefore, it is possible to reduce a load on a user when the user wears the apparatus.

The present disclosure relates to a half-cast mark identification and damaged flatness evaluation and classification method for blastholes in tunnel blasting, including the following steps: S1-2: photographing first and second contrast images as well as a half-cast mark image after blasting; S3-6: performing denoising, gray-scale processing and binary processing on the above images, and identifying a boundary of a half-cast mark in each of the images; S7-9: determining a flatness damage variable, a quantitative relation among an area of a half-cast mark region, the damage variable and a fractal dimension, and a damage value of the half-cast mark image; S10-11: forming five-dimensional (5D) eigenvectors to obtain multi-dimensional digital information features of the images; and S12-13: selecting eigenvectors of 60 images as training data to input to a naive Bayes classifier (NBC), and taking eigenvectors of remaining 30 images as classification data to input the above well-trained NBC for classification.

System and methods of automatic digital rock segmentation are provided. A deep learning model may be trained to segment images of reservoir rock. The training may involve the use of first image data of reservoir rock samples and first segmentation data mapping an intensity of image elements of the first image data to one of a plurality of output channels that respectively represent a characterization of reservoir rock. Second image data of a new reservoir rock sample may be obtained, and an intensity of image elements of the second image data may be determined. Using the trained deep learning model, second segmentation data may be generated that maps the intensity of each image element in the second image data to a corresponding one of the plurality of output channels. The trained deep learning model may output a characterization of the new reservoir rock sample based on the second segmentation data.