Image Segmentation
The task of a computerized view that distinguishes a image into meaningful areas at pixel level.
Image division (image segmentation) is a computerized vision that distinguishes the image into meaningful areas by assigning objects or categories of each pixel in a image. Unlike object detection only a rough limiting box boot, the exact limits of the split object in pixel precision. There are two main types: semantic dividends, groups under a single label of all the pixels in the same category, but does not distinguish individual objects; the example (instance) dividends, while separateing different objects in the same category, throws a unique identity to each.
This technique has revolutionized especially in medical imaging with the development of architectures such as U-Net: removal of tumor, organ or full limits of the vein at a MR or tomography image, is vital in diagnostic and treatment planning. It is also widely used in the process of decomposing the road, sidewalk and obstacles in autonomous driving, mapping the use of land in satellite images and rear plan lifting in photo editing applications. Up-to-date models such as Meta Segment Anything Model (SAM) have significantly improved this area by making dividends can be generalized for almost any object.
This technique has revolutionized especially in medical imaging with the development of architectures such as U-Net: removal of tumor, organ or full limits of the vein at a MR or tomography image, is vital in diagnostic and treatment planning. It is also widely used in the process of decomposing the road, sidewalk and obstacles in autonomous driving, mapping the use of land in satellite images and rear plan lifting in photo editing applications. Up-to-date models such as Meta Segment Anything Model (SAM) have significantly improved this area by making dividends can be generalized for almost any object.