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.

Use a combination of basic morphological operators and blob analysis to extract information from a video stream. In this case, the example counts the number of E. Coli bacteria in each video

Compress a video using motion compensation and discrete cosine transform (DCT) techniques. The example calculates motion vectors between successive frames and uses them to reduce

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,

Use the 2-D normalized cross-correlation for pattern matching and target tracking.

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

Use sum of absolute differences (SAD) method for detecting motion in a video sequence. This example applies SAD independently to four quadrants of a video sequence. If motion is detected in a

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 Hough Transform and Polyfit blocks to horizontally align text rotating in a video sequence. The techniques illustrated by this example can be used in video stabilization and optical

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

Use the From Video Device block provided by Image Acquisition Toolbox™ to acquire live image data from a Point Grey Flea® 2 camera into Simulink®. The example uses the Computer Vision System

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

Create a mosaic from a video sequence. Video mosaicking is the process of stitching video frames together to form a comprehensive view of the scene. The resulting mosaic image is a compact

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).

Track a person's face and hand using a color-based segmentation method.