‍_Computer vision is a complex interdisciplinary scientific field that uses various tools and algorithms to analyze images and videos, making sense of their content and context. It's an effective way to leverage computing power, software, and algorithms to understand visual data across various sectors and use cases. Computer vision helps us understand the content and context of images and videos in healthcare, sports, manufacturing, security, and many others. By providing annotated datasets, computer vision models learn and interpret, enabling AI, ML, and other neural networks to see, interpret, and understand visual data. The glossary provides 39 essential terms and definitions that impact video annotation and labeling projects, including image annotation, computer vision, COCO, YOLO, DICOM, NIfTI, active learning platforms, frames per second, greyscale, segmentation, object detection, image formatting, IoU, Dice loss, anchor boxes, non-maximum suppression, noise, blur techniques, edge detection, dynamic and event-based classifications, annotations, AI-assisted labeling, ghost frames, polygons, polylines, keypoints, bounding boxes, primitives, human pose estimation, interpolation, RSN, AlphaPose, MediaPipe, OpenPose, PACS, and native formatting. By understanding these terms and concepts, annotators and project leaders can improve the accuracy of their computer vision models and accelerate model development with data-driven insights._<|fim_end|>`‍_`‍_