Segmentation
Process in AI that subdivides an image or dataset into multiple parts to simplify and/or change the perspective of comprehension.
Segmentation in the context of Artificial Intelligence (AI) and Machine Learning (ML) refers to a process by which an image or a dataset is divided into multiple segments, or sets. This process aids in pattern recognition, object detection, image recognition, and other ML tasks, as it can dramatically simplify or change the understanding of the image or dataset. For instance, in the field of Computer Vision, image segmentation helps to identify and distinguish different objects in an image by dividing the image into segments, where each segment consists of a set of pixels sharing certain characteristics.
Historically, the term 'Segmentation' has been used in the field of computer science and information technology for a long time. However, it's use specifically in the realms of AI and ML began to take off in the late 1990s and early 2000s with the advent and growing popularity of image processing and computer vision techniques.
Key contributors to the development of segmentation in AI are hard to pinpoint due to the broad usage and application of the concept, but several researchers and professionals across disciplines have significantly advanced the understanding and functionality of segmentation. These include professors and computer scientists, expert teams across academia and industry, and dedicated AI research organizations.