Introduction to embedded vision and the OpenCV library
OpenCV Examples
Two sets of OpenCV example programs are included in the BDTI Quick-Start OpenCV Kit. The first set is command-line based, the second set is Eclipse IDE based.
The command line examples include example makefiles to provide guidance for your own projects. The Eclipse examples are the same as the command-line examples but configured to build in the Eclipse environment. The source code is identical, but the makefiles are specialized for building OpenCV applications in an Eclipse environment.
The same examples are also provided in the previously mentioned BDTI OpenCV Executable Demo Package. Embedded vision involves, among other things, classifying groups of pixels in an image or video stream as either background or a unique feature.
Each of the examples demonstrates various algorithms that accomplish this goal using different techniques. Some of them use code derived from the book OpenCV 2 Computer Vision Application Programming Cookbook by Robert Laganiere (ISBN-10: 1849513244, ISBN-13: 978-1849513241).
A Motion Detection Example
As the name implies, motion detection uses the differences between successive frames to classify pixels as unique features (Figure 2 below). The algorithm considers pixels that do not change between frames as being stationary and therefore part of the background. Motion detection or background subtraction is a very practical and easy-to-implement algorithm.
In its simplest form, the algorithm looks for differences between two frames of video by subtracting one frame from the next. In the output display in this example, white pixels are moving, black pixels are stationary.
Click on image to enlarge.
Click on image to enlarge.
This example adds an additional element to the simple frame subtraction algorithm: a running average of the frames.The number of frames in the running average represents a length in time.
The LearnRate sets how fast the accumulator “forgets” about earlier images. The higher the LearnRate, the longer the running average. By setting LearnRate to 0, you disable the running average and the algorithm simply subtracts one frame from the next.[JB2] Increasing the LearnRate is useful for detecting slow moving motion.
The Threshold parameter sets the change level required for a pixel to be considered moving. The algorithm subtracts the current frame from the previous frame, giving a result. If the result is greater than the threshold, the algorithm displays a white pixel and considers that pixel to be moving.
Variables:
* LearnRate: Regulates the update speed (how fast the accumulator "forgets" about earlier images).
* Threshold: The minimum value for a pixel difference to be considered moving.
The Line Detection Example
Line detection classifies straight edges in an image as features (Figure 3 below). The algorithm relegates to the background anything in the image that it does not recognize as a straight edge, thereby ignoring it. Edge detection is another fundamental function in computer vision.
Click on image to enlarge.
Click on image to enlarge.
Image processing determines an edge by sensing close-proximity pixels of differing intensity. For example, a black pixel next to a white pixel defines a “hard” edge. A gray pixel next to a black (or white) pixel defines a “soft” edge.
The Threshold parameter sets a minimum on how hard an edge has to be in order for it to be classified as an edge. A Threshold of 255 would require a white pixel be next to a black pixel to qualify as an edge. As the Threshold value decreases, softer edges in the image are detected.
After the algorithm detects an edge, it must make a difficult decision: is this edge part of a straight line? The Hough transform, employed to make this decision, attempts to group pixels classified as edges into a straight line.
It uses the MinLength and MaxGap parameters to decide ("classify" in computer science lingo) a group of edge pixels into either a straight continuous line or ignored background information (edge pixels not part of a continuous straight line are considered background, and therefore not a feature).
Variables:
* Threshold: Sets the minimum difference between adjacent pixels to be classified as an edge.
* MinLength: The minimum number of "continuous" edge pixels required to classify a potential feature as a straight line.
* MaxGap: The maximum allowable number of missing edge pixels that still enable classification of a potential feature as a "continuous" straight line.
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