Chroma Keying MATLAB Implementation 3.0 In my previous articles on chroma key implementation on MATLAB (Chroma Keying MATLAB Implementation 1.0 and 2.0), I explained how to key two images using a simple image segmentation method called ‘Image thresholding’ and to key an image and a video. In this article I’ll try to extend above to implement on MATLAB simulink. There are two approaches to implement these on MATLAB, first is to implement chroma key using the MATLAB embedded function block and the second is to implement above functionality using basic simulink blocks. Papercrete construction.

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For the implementation arrange the Simulink blocks as below. Image from file block can be found in simulink video and image processing tool box under the source category and Video viewer under sink category. Matrix operation blocks can be found in Signal processing tool box and the Embedded MATLAB function can be found under Simulink user defined functions. And set its parameters as described below.

For Matrix Concatenate block, set parameters as below. • Finally for the Embedded MATLAB function block, add the code as below.

It is the same code I used in my previous articles, but I removed the code lines which I used to select chroma majoring colour and only used the operation on blue backgrounds. Input images are as below The resulting images will be as below. For the Chroma keying on Video you can just replace the Image From file blocks from From Multimedia flie blocks in Image Processing tool box or From Video Device in Image Acquisition tool box.

Resulting video is as below, you can also note how much slow is the process.

% Create a cascade detector object. FaceDetector = vision.CascadeObjectDetector();% Read a video frame and run the face detector. VideoFileReader = vision.VideoFileReader( 'tilted_face.avi'); videoFrame = step(videoFileReader); bbox = step(faceDetector, videoFrame);% Draw the returned bounding box around the detected face. VideoFrame = insertShape(videoFrame, 'Rectangle', bbox); figure; imshow(videoFrame); title( 'Detected face');% Convert the first box into a list of 4 points% This is needed to be able to visualize the rotation of the object. BboxPoints = bbox2points(bbox(1,:)). Vat 201 form download sars.

To track the face over time, this example uses the Kanade-Lucas-Tomasi (KLT) algorithm. While it is possible to use the cascade object detector on every frame, it is computationally expensive. It may also fail to detect the face, when the subject turns or tilts his head.

This limitation comes from the type of trained classification model used for detection. The example detects the face only once, and then the KLT algorithm tracks the face across the video frames. Identify Facial Features To Track The KLT algorithm tracks a set of feature points across the video frames. Once the detection locates the face, the next step in the example identifies feature points that can be reliably tracked. This example uses the standard, 'good features to track' proposed by Shi and Tomasi. Detect feature points in the face region.

Initialize a Tracker to Track the Points With the feature points identified, you can now use the vision.PointTracker System object to track them. For each point in the previous frame, the point tracker attempts to find the corresponding point in the current frame. Then the estimateGeometricTransform function is used to estimate the translation, rotation, and scale between the old points and the new points. This transformation is applied to the bounding box around the face. Create a point tracker and enable the bidirectional error constraint to make it more robust in the presence of noise and clutter.

OldPoints = points; while ~isDone(videoFileReader)% get the next frame videoFrame = step(videoFileReader);% Track the points. Note that some points may be lost.