A Bounding Box, in the context of computer vision and image processing, refers to a rectangular box that can be digitally implemented to recognize, localize, and distinguish objects found in an image or video. This box encompasses the object within its boundaries, serving as the area of focus for further processing. Bounding boxes are a standard tool in many applications, including object detection and tracking, image recognition, and augmented reality among others.
Bounding Box operates based on coordinates. The dimensions of the box are determined by two points: the upper-left corner and the lower-right corner of the box. The coordinates of these two corners define the placement and size of the bounding box on an image. These two points can adequately define a box because, in standard computer vision tasks, images are processed and annotated in two dimensions: height (y-axis) and width (x-axis).
Bounding boxes serve as a fundamental technique in AI-powered image analysis and computer vision tasks. Their primary purpose is to specify the location and scale of objects in images; that information enables further analysis, such as identifying the object within the box or tracking its movement across a series of images or video frames. They are simple and efficient tools that drive the accuracy and effectiveness of object detection and recognition within visual data.
