Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

Systems and methods for generating a segmentation mask of an anatomical structure, along with a measure of uncertainty of the segmentation mask, are provided. In accordance with one or more embodiments, a plurality of candidate segmentation masks of an anatomical structure is generated from an input medical image using one or more trained machine learning networks. A final segmentation mask of the anatomical structure is determined based on the plurality of candidate segmentation masks. A measure of uncertainty associated with the final segmentation mask is determined based on the plurality of candidate segmentation masks. The final segmentation mask and/or the measure of uncertainty are output.

Systems and methods for generating a segmentation mask of an anatomical structure, along with a measure of uncertainty of the segmentation mask, are provided. In accordance with one or more embodiments, a plurality of candidate segmentation masks of an anatomical structure is generated from an input medical image using one or more trained machine learning networks. A final segmentation mask of the anatomical structure is determined based on the plurality of candidate segmentation masks. A measure of uncertainty associated with the final segmentation mask is determined based on the plurality of candidate segmentation masks. The final segmentation mask and/or the measure of uncertainty are output.

A method and system for detecting plane information are provided. The method includes: obtaining point cloud information of a physical environment of a user; performing an iterative regressing operation on the point cloud information to fit all plane information corresponding to the physical environment; merging all the plane information according to a preset rule to obtain a merged plane information set; performing plane segmentation on the plane information set based on a pre-trained plane segmentation model to obtain segmented plane information; and filtering the segmented plane information to determine all target plane information corresponding to the physical environment.

A method of barrier detection in an imaging controller includes: obtaining an image of a support structure configured to support a plurality of items on a support surface extending between a shelf edge and a shelf back; extracting frequency components representing pixels of the image; based on the extracted frequency components, identifying a barrier region of the image, the barrier region containing a barrier adjacent to the shelf edge; and detecting at least one empty sub-region within the barrier region, wherein the empty sub-region is free of items between the barrier and the shelf back.

A method of barrier detection in an imaging controller includes: obtaining an image of a support structure configured to support a plurality of items on a support surface extending between a shelf edge and a shelf back; extracting frequency components representing pixels of the image; based on the extracted frequency components, identifying a barrier region of the image, the barrier region containing a barrier adjacent to the shelf edge; and detecting at least one empty sub-region within the barrier region, wherein the empty sub-region is free of items between the barrier and the shelf back.

A system for controlling a physical system via segmentation of an image includes a controller. The controller may be configured to receive an image of n pixels from a first sensor, and an annotation of the image from a second sensor, form a coupling matrix, k class vectors each of length n, and a bias coefficient based on the image and the annotation, generate n pixel vectors each of length n based on the coupling matrix, class vectors, and bias coefficient create a single segmentation vector of length n from the pixel vectors wherein each entry in the segmentation vector identifies one of the k class vectors, output the single segmentation vector; and operate the physical system based on the single segmentation vector.

A system for controlling a physical system via segmentation of an image includes a controller. The controller may be configured to receive an image of n pixels from a first sensor, and an annotation of the image from a second sensor, form a coupling matrix, k class vectors each of length n, and a bias coefficient based on the image and the annotation, generate n pixel vectors each of length n based on the coupling matrix, class vectors, and bias coefficient create a single segmentation vector of length n from the pixel vectors wherein each entry in the segmentation vector identifies one of the k class vectors, output the single segmentation vector; and operate the physical system based on the single segmentation vector.

Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.