A video target tracking method is provided to a computing device, the method includes: obtaining a partial detection map corresponding to a target image frame in a to-be-detected video; obtaining a relative motion saliency map corresponding to the target image frame; determining constraint information corresponding to the target image frame according to the partial detection map and the relative motion saliency map; adjusting a parameter of an image segmentation model by using the constraint information, to obtain an adjusted image segmentation model; and extracting a target object from the target image frame by using the adjusted image segmentation model.

An image processing method and a related apparatus are provided. The method is applied to an image processing device, and includes: obtaining an original image, the original image including a foreground object; extracting a foreground region from the original image through a deep neural network; identifying pixels of the foreground object from the foreground region; forming a mask according to the pixels of the foreground object, the mask including mask values corresponding to the pixels of the foreground object; and extracting the foreground object from the original image according to the mask.

A method of detecting an edge of a support surface in an imaging controller includes: obtaining image data captured by an image sensor and a plurality of depth measurements captured by a depth sensor, the image data and the plurality of depth measurements corresponding to an area containing the support surface; detecting preliminary edges in the image data; applying a Hough transform to the preliminary edges to determine Hough lines representing candidate edges of the support surface; segmenting the plurality of depth measurements to assign classes to each pixel, each class defined by one of a plurality of seed pixels, wherein the plurality of seed pixels are identified from the depth measurements based on the Hough lines; and detecting the edge of the support surface by selecting a class of pixels and applying a line-fitting model to the selected class to obtain an estimated edge of the support surface.

An image processing method includes: performing first segmentation processing on an image to be processed, and determining a segmentation region of a target in said image (S11); determining, according to the position of the center point of the segmentation region of the target, an image region where the target is located (S12); and performing second segmentation processing on the image region where each target is located, and determining the segmentation result of the target in said image (S13).

An image processing apparatus includes a processor configured to display boundaries each of which encloses a respective one of multiple candidate regions that correspond to foreground objects in an image, detect a single selecting operation, and extract a target region corresponding to one of the foreground objects from one or more candidate regions of the multiple candidate regions. The one or more candidate regions are selected by the single selecting operation.

A method and system for image reconstruction are provided. A projection image of a projection object may be obtained. A processed projection image may be generated based on the projection image through one or more pre-process operations. A reconstructed image including an artifact may be reconstructed based on the processed projection image. The artifact may be a detector edge artifact, a projection object edge artifact, and a serrated artifact. The detector edge artifact, the projection object edge artifact, and the serrated artifact may be removed from the reconstructed image.

Provided herein are techniques for automatically segmenting anterior segment of an eye in an optical coherence tomography (OCT) image. A method includes receiving an OCT image of an eye; cropping, based on one or more structures of the eye in the OCT image, the OCT image of the eye into one or more sub-images corresponding to the one or more structures; for each of the one or more sub-images of the OCT image of the eye: generating a background seed and a foreground seed of the sub-image; generating, based on the background and the foreground seeds, and an image segmentation model, a mask for a structure of the one or more structures of the eye included in the sub-image; generating, based on the mask for the structure, one or more contours of the structure included in the sub-image; and displaying the one or more contours on the sub-image.

A mechanism is provided in a data processing system for refining lesion contours with combined active contour and inpainting. The mechanism receives an initial segmented medical image having organ tissue including a set of object contours and a contour to be refined. The mechanism inpaints object voxels inside all contours of the set. The mechanism calculates an updated contour around the contour to be refined based on the in-painted object voxels to form an updated segmented medical image. The mechanism determines whether the updated segmented medical image is improved compared to the initial segmented medical image. The mechanism keeps the updated segmented medical image responsive to the updated segmented medical image being improved.

Methods and systems are provided for facilitating automated braces removal from individuals in images. In embodiments described herein, an indication to remove the braces from an individual wearing braces in an image is obtained. Based on receiving the indication to remove the braces, automatically, without user intervention, a teeth region is identified in the image that includes teeth of the individual, and a braces region is identified that includes braces visible in the teeth region. The teeth region and braces region are used to generate an edited image that includes the individual without braces.

The present disclosure may provide a method for segmenting an image. The method may include obtaining an image and related information. The image may include a tumor region. The method may also include determining a region of interest in the image. The region of interest may include the tumor region. The method may also include performing a first segmentation of the region of interest to obtain a first segmentation result. The first segmentation may include: determining tumor morphology relating to the tumor region; performing a second segmentation of the region of interest to obtain a second segmentation result; and optimizing, based on the tumor morphology, the second segmentation result to obtain the first segmentation result.