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Segmentation masks are a fundamental concept in digital image processing and computer vision. They are essentially binary images or arrays that highlight specific regions of interest within a larger image. The pixels within the mask are typically assigned a value of one (or white) to indicate they belong to the object or region being segmented, while pixels outside the region are assigned a value of zero (or black). This creates a clear delineation between the foreground and background, or between different objects in a scene.

The process of creating segmentation masks is known as image segmentation. This can be achieved through various techniques, ranging from simple thresholding methods that separate pixels based on intensity values, to more complex algorithms like region growing, edge detection, and watershed transformations. In recent years, deep learning-based approaches, particularly convolutional neural networks (CNNs), have become highly effective for generating accurate segmentation masks, often outperforming traditional methods.

Segmentation masks have a wide range of applications. They are crucial for object detection and recognition, enabling systems to isolate and identify specific items in an image. In medical imaging, they are used to delineate tumors, organs, or other anatomical structures for diagnosis and treatment planning. Autonomous driving systems utilize segmentation masks to identify roads, pedestrians, and other vehicles. Furthermore, they play a role in image editing software, allowing for precise selection and manipulation of image elements. The output of many image analysis tasks is often a segmentation mask, which can then be used for further processing or analysis.