Segmentasjonsmasker

Segmentasjonsmasker, often referred to as segmentation masks, are a fundamental concept in computer vision and image processing. They are essentially binary images that highlight specific regions or objects of interest within a larger image. In a segmentation mask, pixels belonging to the target object are typically assigned one value (e.g., 1 or 255, representing white), while all other pixels are assigned another value (e.g., 0, representing black). This creates a clear distinction, effectively "masking" out the desired segment.

The primary purpose of a segmentation mask is to isolate and define the precise boundaries of an object or region. This is crucial for various downstream tasks. For instance, in medical imaging, segmentation masks can delineate tumors or organs for analysis and diagnosis. In autonomous driving, they help identify roads, pedestrians, and other vehicles, enabling the system to understand its environment. Object detection and recognition also heavily rely on segmentation masks to accurately locate and classify objects.

Creating segmentation masks can be achieved through several methods. Manual annotation, where a human annotator draws the boundaries of objects, is common for creating training data for machine learning models. However, this is time-consuming and labor-intensive. Automated or semi-automated methods, often employing deep learning techniques like convolutional neural networks (CNNs), are increasingly used to generate these masks more efficiently. These algorithms learn to identify patterns and features within images that correspond to specific objects, thereby producing pixel-level segmentation.