Determine whether video frames are in focus by using the ratio of the high spatial frequency content to the low spatial frequency content within a region of interest (ROI). When this ratio is
Use basic morphological operators to extract information from a video stream. In this case, the model counts the number of staples in each video frame. Note that the focus and lighting change
Inspect the concentricity of both the core and the cladding in a cross-section of optical fiber. Concentricity is a measure of how centered the core is within the cladding.
Recognize traffic warning signs, such as Stop, Do Not Enter, and Yield, in a color video sequence.
Process surveillance video to select frames that contain motion. Security concerns mandate continuous monitoring of important locations using video cameras. To efficiently record,
Create an image processing system which can recognize and interpret a GTIN-13 barcode. The GTIN-13 barcode, formally known as EAN-13, is an international barcode standard. It is a superset
Segment video in time. The algorithm in this example can be used to detect major changes in video streams, such as when a commercial begins and ends. It can be useful when editing video or when
Remove periodic noise from a video. In a video stream, periodic noise is typically caused by the presence of electrical or electromechanical interference during video acquisition or
Use the From Video Device block provided by Image Acquisition Toolbox™ to acquire live image data from a Hamamatsu C8484 camera into Simulink®. The Prewitt method is applied to find the edges
Remove the effect of camera motion from a video stream. In the first video frame, the model defines the target to track. In this case, it is the back of a car and the license plate. It also
Detect and track cars in a video sequence using optical flow estimation.
Track objects at a train station and to determine which ones remain stationary. Abandoned objects in public areas concern authorities since they might pose a security risk. Algorithms,
Implement a face detection and tracking algorithm in Simulink® by using a MATLAB® Function block. It closely follows the Face Detection and Tracking Using the KLT Algorithm MATLAB®
Use color information to detect and track road edges set in primarily residential settings where lane markings may not be present. The Color-based Tracking example illustrates how to use
Detect and track road lane markers in a video sequence and notifies the driver if they are moving across a lane. The example illustrates how to use the Hough Transform, Hough Lines and Kalman
Detect and count cars in a video sequence using Gaussian mixture models (GMMs).