Coding Viola-Jones Algorithm in C#: Q&A

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In summary: The same principle can be applied to find other facial features such as the nose. You can set a threshold to determine when the feature has been found. OpenCV also uses a similar approach in its implementation. In summary, when coding the Viola-Jones algorithm in C#, you can use a 24 X 24 Rectangle to pass over the integral Image and calculate the white and black areas to determine when a facial feature, such as an eye or nose, has been found. You can set a threshold to determine when the feature has been found.
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btb4198
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I am coding the Viola-Jones algorithm in C# and I have a few questions. For finding an eye, after I have my integral Image, I am passing a 24 X 24 Rectangle across the whole image from left to right and top to bottom. I subtract the black area from the white. So right now, I am solving for the area of A, B, C and D as I move across the integral Image and then I do
whiteRectangleArea = values.B + values.A;
blackRectangleArea = values.D + values.C;
double value = Math.Abs(whiteRectangleArea - blackRectangleArea);

1) For finding an eye would it be: white = A + B and Black = D + C?

2)How do I know when I have found the Eye? Like is there a specific value I am looking for? I watched a simulator that stopped right when it got to the object it was looking for, but I am not sure how it knows it found it.

3) Because the eyes are a two rectangles feature you only need to solve for the area of A, B, C and D. but for the Nose with is a 3 rectangles features you need to solve for the areas of A,B,C,D,E and F right?

Therefore, to find a nose you would do this.
double value = Math.Abs((values.A + values.D) - (values.B + values.E) - ( values.C + values.F));

Is this right?

Also, Yes I do know opencv does this, but I would like to learn how it code it.
 
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1) Yes, for finding an eye, white = A + B and Black = D + C.2) You can set a threshold to determine when the eye has been found. If the value computed from subtracting the white area from the black area exceeds the threshold, then it is considered that the eye has been found.3) For a 3-rectangle feature such as the nose, you would need to solve for the areas of A, B, C, D, E and F. To find the nose, you would calculate the value as follows:double value = Math.Abs((values.A + values.D) - (values.B + values.E) - ( values.C + values.F));This is correct.
 

Related to Coding Viola-Jones Algorithm in C#: Q&A

1. What is the Viola-Jones algorithm?

The Viola-Jones algorithm is a popular object detection algorithm used for face detection in images. It was developed by Paul Viola and Michael Jones in 2001 and has since been used for various applications such as face recognition, pedestrian detection, and gesture recognition.

2. How does the Viola-Jones algorithm work?

The Viola-Jones algorithm works by using a combination of Haar-like features and a cascade of classifiers to identify objects in an image. These Haar-like features are rectangular regions of an image with intensity values that are calculated and compared to a threshold. The cascade of classifiers then uses these features to determine whether the object is present or not.

3. Why is the Viola-Jones algorithm popular?

The Viola-Jones algorithm is popular because it is fast, accurate, and can detect multiple objects in an image. It also has a low computational cost compared to other object detection algorithms, making it suitable for real-time applications.

4. How can I code the Viola-Jones algorithm in C#?

To code the Viola-Jones algorithm in C#, you can use the OpenCV library, which provides a pre-trained Haar cascade classifier for face detection. You can also implement the algorithm from scratch by following tutorials and using libraries such as AForge.NET or Emgu CV.

5. What are the limitations of the Viola-Jones algorithm?

Some of the limitations of the Viola-Jones algorithm include its sensitivity to changes in lighting and orientation, as well as its performance on complex images with cluttered backgrounds. It also requires a large number of negative and positive training samples for optimal performance.

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