Systems and methods for automated stereology are provided. In some embodiments, an active deep learning approach may be utilized to allow for a faster and more efficient training of a deep learning model for stereology analysis. In other embodiments, existing deep learning models for stereology analysis may be re-tuned to develop greater accuracy for a given data set of interest, either with or without an active deep learning approach. A method can include: capturing a data set including a stack of images of a three-dimensional (3D) object; determining whether an existing deep learning model is appropriate for use on the stack of images (or for re-tuning); performing pre-processing on the data set; performing a training of a deep learning model; applying the deep learning model to obtain a confidence score for each label of the data set; reviewing, by a user, at least some labels in the active set to verify whether the label displays sufficient agreement with an expected result, and moving only those that display sufficient agreement to a training set; and performing a stereology analysis using the trained deep learning model.

A method and system is provided for detection of carbonate core features from core images. An input carbonate core image is separated into a plurality of first blocks, each of the plurality of first blocks having a first block size. An image of each of the separated plurality of first blocks is input into an artificial intelligence (AI) model. The AI model being trained to predict for each first block, one of a plurality of carbonate core features and a corresponding confidence value indicating a confidence of the predicted carbonate core feature being imaged in the first block. Any bounding boxes of a first set of bounding boxes are detected in the input core image based on the predicted one of the plurality of carbonate core features and the corresponding confidence values for each first block.

This application provides a semantic segmentation network structure generation method performed by an electronic device, and a non-transitory computer-readable storage medium. The method includes: generating a corresponding architectural parameter for cells that form a super cell in a semantic segmentation network structure; optimizing the semantic segmentation network structure based on image samples, and removing a redundant cell from a super cell to which a target cell pertains, to obtain an improved semantic segmentation network structure; performing, by an aggregation cell in the improved semantic segmentation network structure, feature fusion on an output of the super cell; performing recognition processing on a fused feature map, to determine positions corresponding to objects that are in the image samples; and training the improved semantic segmentation network structure based on the positions corresponding to the objects that are in the image samples and annotations corresponding to the image samples, to obtain a trained semantic segmentation network structure.

A method and non-transitory computer-readable medium capture an image of bulk grain and apply a feature extractor to the image to determine a feature of the bulk grain in the image. For each of a plurality of different sampling locations in the image, based upon the feature of the bulk grain at the sampling location, a determination is made regarding a classification score for the presence of a classification of material at the sampling location. A quality of the bulk grain of the image is determined based upon an aggregation of the classification scores for the presence of the classification of material at the sampling locations.

A training data generation method includes: obtaining a camera image, a labeled image generated by adding annotation information to the camera image, and an object image showing an object to be detected by a learning model; identifying a specific region corresponding to the object based on the labeled image; and compositing the object image in the specific region on each of the camera image and the annotated image.

An alignment method for manufacturing a mask integration framework is disclosed. The alignment method includes establishing an absolute coordinate system by taking a center of a metal framework as an origin of coordinates, the center of the metal framework coinciding with a center of an array substrate serving as a reference, controlling the array substrate to move, such that an offset of coordinates of a pixel point under the absolute coordinate system with respect to a predetermined theoretical value is smaller than or equal to a predetermined error value, and transmitting the coordinates of the pixel point under the absolute coordinate system, after the array substrate moves, to a tension device. An alignment system for manufacturing mask integration framework is also disclosed.

A structure-preserving composite model for skin lesion segmentation includes partitioning a dermoscopic image into superpixels at a first scale. Each superpixel is a vertex on a graph defined by color coordinates and spatial coordinates, and represents a number of pixels of the dermoscopic image according to the first scale. Further, constructing a plurality of k background templates by k-means clustering selected ones of the superpixels in space and color. Additionally, generating sparse representations of the plurality of superpixels based on the plurality of background templates. Also, calculating a reconstruction error for each superpixel by comparison of its sparse representation to its original color coordinates and spatial coordinates. Furthermore, outputting a confidence map that identifies each pixel of the dermoscopic image as belonging or not belonging to a skin lesion, based on the reconstruction errors of the representative superpixels.

The present disclosure provides a computer-implemented method of, and system for, characterizing the phenotype of a plant. The method includes: (i) obtaining mesh data representing a surface of the plant, said mesh data including data representing a plurality of polygons having respective sets of vertices, each vertex having a spatial coordinate; and (ii) applying at least two segmentations of progressively finer resolution to the mesh data to assign the vertices to distinct morphological regions of the plant.

Human Computer Interfaces (HCI) may allow a user to interact with a computer via a variety of mechanisms, such as hand, head, and body gestures. Various of the disclosed embodiments allow information captured from a depth camera on an HCI system to be used to recognize such gestures. Particularly, the HCI system's depth sensor may capture depth frames of the user's movements over time. To discern gestures from these movements, the system may group portions of the user's anatomy represented by the depth data into classes. This grouping may require that the relevant depth data be extracted from the depth frame. Such extraction may itself require that appropriate clipping planes be determined. Various of the disclosed embodiments better establish floor planes from which such clipping planes may be derived.

A subject detection method is disclosed. A moving object is detected on an image to obtain a moving object region, candidate regions are obtained by sliding a sliding box on the image, a first region is determined. The first region is one of candidate regions and contains a part of the moving object region with a largest area. A proportion of the first region is obtained, a size of the first region is adjusted based on the proportion to obtain a second region, a proportion of the second region is obtained. The first region is replaced with the second region, the proportion of the first region is replaced with the proportion of the second region, and it is returned to adjusting the size of the first region until a number of iterative adjustments reaches a threshold, a region obtained by the last adjustment is determined as a target region.