kuvasegmendid

Kuvasegmendid refers to segments or portions of an image. In digital image processing, image segmentation is the process of partitioning a digital image into multiple segments or sets of pixels. The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze.

Common applications of image segmentation include object recognition, medical imaging, and autonomous driving. For instance, in medical imaging, segmentation can be used to delineate tumors or other anatomical structures from healthy tissue. In autonomous driving, it helps identify road boundaries, other vehicles, and pedestrians.

There are various techniques for image segmentation. These can be broadly categorized into several approaches, including thresholding, region-based methods, edge-based methods, and clustering. Thresholding involves selecting a gray-level threshold to separate pixels into different classes. Region-based methods group pixels into regions based on similarity criteria. Edge-based methods focus on detecting discontinuities in the image, such as edges or lines. Clustering algorithms group pixels based on their feature vectors.

More advanced techniques also leverage machine learning and deep learning, particularly convolutional neural networks (CNNs), to achieve highly accurate and robust segmentation results. These deep learning models can learn complex features and patterns within an image, enabling them to distinguish between intricate objects and backgrounds. The output of a segmentation process is typically a set of regions or masks, each corresponding to a specific object or area of interest within the original image.