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ITIL (Information Technology Infrastructure Library) is a framework that IT professionals use to identify best practices for their IT service management. An ITIL process is typically drawn as a flowchart. The ITIL Diagram template in Visio provides functional shapes to create and enhance diagrams of ITIL processes. Introduction to ITIL and the ITIL Process Map This document contains a brief introduction to ITIL and explains how the ITIL Process Map for Visio was created from the ITIL books. ITIL implementation guide ITIL implementation projects are characterized by a typical course of action, independent of the size and type of the organisation. Introduction: ITIL® and the ITIL Process Map A brief introduction to ITIL, including a comparison between ITIL 2007 and 2011, and details on how the processes from the ITIL books are represented in the ITIL Process Map [PDF, 34 pages] Legal. General terms of IT Process Maps GbR and license conditions for the ITIL Process Map [PDF, 3 pages] Home. ITIL® translated into easy to read, customizable Visio process templates in BPMN format. Process diagrams in four levels of detail describe the process activities and the information flows between the ITIL processes. The ITIL® Process Map for Microsoft Visio is an officially 'ITIL® licensed product'.

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% 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,:)). 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. [points, isFound] = step(pointTracker, videoFrame); visiblePoints = points(isFound,:); oldInliers = oldPoints(isFound,:); if size(visiblePoints, 1) >= 2% need at least 2 points% Estimate the geometric transformation between the old points% and the new points and eliminate outliers [xform, oldInliers, visiblePoints] = estimateGeometricTransform(. OldInliers, visiblePoints, 'similarity', 'MaxDistance', 4);% Apply the transformation to the bounding box points bboxPoints = transformPointsForward(xform, bboxPoints);% Insert a bounding box around the object being tracked bboxPolygon = reshape(bboxPoints', 1, []); videoFrame = insertShape(videoFrame, 'Polygon', bboxPolygon.

'LineWidth', 2);% Display tracked points videoFrame = insertMarker(videoFrame, visiblePoints, '+'. 'Color', 'white');% Reset the points oldPoints = visiblePoints; setPoints(pointTracker, oldPoints); end% Display the annotated video frame using the video player object step(videoPlayer, videoFrame); end% Clean up release(videoFileReader); release(videoPlayer); release(pointTracker).