Computer vision camera calibration is the process of estimating the intrinsic and extrinsic parameters of a camera, which are used to correct for lens distortion and project 3D points onto the image plane. In MATLAB, the Computer Vision Toolbox provides functions for camera calibration using a set of images of a calibration object with known dimensions, such as a checkerboard pattern. The calibration process involves detecting the corners of the calibration object in the images, computing the camera parameters from the detected corners, and evaluating the calibration accuracy. Once the camera is calibrated, the camera parameters can be used to undistort images and perform other camera-related tasks.
Computer vision color detection is the process of identifying and localizing objects of a specific color within an image or video stream. For example, red color detection involves identifying pixels or regions in an image or video that have a high red component. In computer vision, this can be accomplished using color spaces such as RGB, HSV, or LAB, and thresholding or clustering techniques to segment the image based on color. Once the red objects are detected, they can be further processed or analyzed for various applications, such as object tracking, traffic monitoring, or quality control in manufacturing.
Computer vision AprilTag detection is the process of recognizing and localizing AprilTag markers within an image or video stream. AprilTags are 2D barcodes that encode a unique identifier and position information, which can be used for robotic localization and mapping, augmented reality, or human-robot interaction. In computer vision, AprilTag detection involves detecting and decoding the marker within the image using feature extraction and matching techniques. The detection process typically involves detecting edges and corners in the image, and then matching them to the predefined AprilTag template using pose estimation algorithms. Once the AprilTag is detected, its position and orientation in the 3D world can be estimated, allowing for accurate robot navigation or virtual object placement.
Computer vision 3D reconstruction is the process of creating a 3D model of an object or scene from one or more 2D images. In computer vision, this can be accomplished using techniques such as stereo vision, structured light, or photogrammetry. Stereo vision involves using two or more cameras to capture images of the same scene from different angles, and then triangulating the corresponding points in the images to reconstruct the 3D structure. Structured light involves projecting a known pattern onto the scene and then analyzing the distortion of the pattern in the captured images to infer the 3D shape. Photogrammetry involves analyzing the perspective and position of objects in multiple images to estimate their 3D positions. Once the 3D model is reconstructed, it can be used for various applications, such as virtual reality, product design, or architectural visualization.
To recognize pennies in pictures or videos using a computer vision, we can teach the computer to look for specific features of pennies, like their round shape and copper color.