Provided are an image exposure adjustment method and apparatus, a device, and a medium. The image exposure adjustment method includes performing human body detection on a collected image; in a case where a human body is detected, segmenting the image to determine a foreground region and a background region in the image; determining a mask image according to the foreground region and the background region; and determining an exposure weight table according to the mask image, and performing exposure value adjustment on the image according to the exposure weight table.

An x-ray microscopy method that obtains a classification of different particles by distinguishing between different material phases through a combination of image processing involving morphological edge enhancement and possibly resolved absorption contrast differences between the phases along with optional wavelet filtering.

An x-ray microscopy method that obtains a classification of different particles by distinguishing between different material phases through a combination of image processing involving morphological edge enhancement and possibly resolved absorption contrast differences between the phases along with optional wavelet filtering.

A method for detecting a defect on a substrate, including receiving a first image, generating a second image, by converting the first image to grayscale levels, calculating a first gray level value, having a maximum number of pixels in the second image, and second and third gray level values, having a number of pixels in the second image equal to a predetermined fraction of the first gray level value, from a histogram of the number of pixels respective to the grayscale levels of the second image, converting the second image into a third image having pixels at a level lower than that of the first gray level value and a fourth image having pixels at a level equal to or higher than the first gray level value, generating fifth and sixth images by detecting edges by applying a Canny algorithm to the third and fourth images, respectively.

A method for detecting a defect on a substrate, including receiving a first image, generating a second image, by converting the first image to grayscale levels, calculating a first gray level value, having a maximum number of pixels in the second image, and second and third gray level values, having a number of pixels in the second image equal to a predetermined fraction of the first gray level value, from a histogram of the number of pixels respective to the grayscale levels of the second image, converting the second image into a third image having pixels at a level lower than that of the first gray level value and a fourth image having pixels at a level equal to or higher than the first gray level value, generating fifth and sixth images by detecting edges by applying a Canny algorithm to the third and fourth images, respectively.

An image processing method performed by a terminal is provided. A molybdenum target image is obtained, and a plurality of candidate regions are extracted from the molybdenum target image. In the molybdenum target image, a target region is marked in the plurality of candidate regions by using a neural network model obtained by deep learning training, a probability that a lump comprised in the target region is a target lump being greater than a first threshold, a probability that the target lump is a malignant tumor being greater than a second threshold, and the neural network model being used for indicating a mapping relationship between a candidate region and a probability that a lump comprised in the candidate region is the target lump.

A method for identifying a dendritic pore is provided. Line and pore images are obtained from a digital image of a subject's skin. These line and pore images are overlaid to identify those pores having at least one line intersecting the pore as dendritic pores.

A method for identifying a dendritic pore is provided. Line and pore images are obtained from a digital image of a subject's skin. These line and pore images are overlaid to identify those pores having at least one line intersecting the pore as dendritic pores.

Disclosed herein is a method and system for automatically propagating segmentation in a medical image. In an embodiment, the method uses a segmented reference Region of Interest (RoI) in a reference image to determine segmentation parameters and a plurality of reference points. Further, method generates a plurality of translated points on a current image, in which a target RoI must be segmented, by translating the plurality of reference points onto the current image. Subsequently, relevant seeds among from the translated points are automatically selected based on the segmentation parameters. Finally, a multi-seed segmentation of the selected relevant seeds is performed for estimating and segmenting the target RoI in the current image, such that the target RoI is the propagated segmentation of the segmented RoI in the reference image.

Disclosed herein is a method and system for automatically propagating segmentation in a medical image. In an embodiment, the method uses a segmented reference Region of Interest (RoI) in a reference image to determine segmentation parameters and a plurality of reference points. Further, method generates a plurality of translated points on a current image, in which a target RoI must be segmented, by translating the plurality of reference points onto the current image. Subsequently, relevant seeds among from the translated points are automatically selected based on the segmentation parameters. Finally, a multi-seed segmentation of the selected relevant seeds is performed for estimating and segmenting the target RoI in the current image, such that the target RoI is the propagated segmentation of the segmented RoI in the reference image.