Kelvin's profile - activity

Thanks @sturkmen, so, these are the 'new format cascades'?

Ok, I see that the flags parameter is only being used by the Old Format Cascade, but I still don't know if I'm using the

Flags parameter in the detectMultiScale function Can someone explain to me what does the flags parameter do in the detec

It only says: flags Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is no

It only says: flags Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is no

It only says: flags Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is no

@sturkmen, what is a 'new cascade'? I don't get it.

It only says: flags Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is no

Flags parameter in the detectMultiScale function Can someone explain to me what does the flags parameter do in the detec

Yes, I understand that the '58' values are representing non-uniform patterns. It seems to make sense when we have a limi

The LBPH implementation provided by OpenCV uses the uniform patterns approach (as described here) (e.g. 59 patterns) or it uses all patterns (e.g. 256)?

Thanks in advance

LBPH uses uniform patterns approach? The LBPH implementation provided by OpenCV uses the uniform patterns approach (as d

Doubt about Speeded-Up Robust Features paper In the paper entitled "Speeded-Up Robust Features (SURF)" Bay et. al. menti

Doubt about Speeded-Up Robust Features paper In the paper entitled "Speeded-Up Robust Features (SURF)" Bay et. al. menti

Doubt about Speeded-Up Robust Features paper In the paper entitled "Speeded-Up Robust Features (SURF)" Bay et. al. menti

Doubt about Speeded-Up Robust Features paper In the paper entitled "Speeded-Up Robust Features (SURF)" Bay et. al. menti

As can be seen in the Eigenfaces section from the OpenCV documentation, the Eigenfaces algorithm uses the default value 0 to the num_components parameter which represents the number of components kept for this Principal Component Analysis.

I just want to know what happens when using the default parameter, will it use all components?

Ok, thanks @berak.

So, 'blobs' means 'color stains'?

Hi, I have a doubt about the SIFT algorithm.

In the OpenCV documentation it says:

For this, scale-space filtering is used. In it, Laplacian of Gaussian is found for the image with various σ values. LoG acts as a blob detector which detects blobs in various sizes due to change in σ.

I don't quite understand what means 'detects blobs', can someone explain to me?

Thanks in advance

Hello, I am testing the LBPH face recognition method varying its parameters, but I don't quite understand the "effect" of each parameter.

The documentation says:

• radius – The radius used for building the Circular Local Binary Pattern. The greater the radius, the

• neighbors – The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost.

• grid_x – The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

• grid_y – The number of cells in the vertical direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

1. The documentation says: "The greater the radius, the". What happens when I use a greater radius?

2. As the documentation says in the neighbor's parameter: "the more sample points you include, the higher the computational cost", but what is the benefit of a greater "sample points"?

3. What is the benefit of using higher grid_x and grid_y? It will increase the computational cost, right? The documentation says: "The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.". It means that the vector will be more accurate to represent that face?

Thanks @berak. So, the value for the rectangle area is calculated based on the integral image, and each value from the integral image, in turn, is generated from the sum of all pixels (intensity) in the up-left from the original image. Is that right? If it is right, the answer to my question is that it uses the intensity of the pixels to calculate the rectangles value.

Now I have another doubt, about the integral image. It uses the intensity of the current pixel to generate the corresponding value (x,y) for the integral image?

For example:

|10|15|

|18|20|

Original

|10|25|

|28|63|

Integral

Is that correct?

I don't quite understand how the pixels are summed in each rectangle area described here: Face Detection using Haar Cascades

It says:

Each feature is a single value obtained by subtracting sum of pixels under white rectangle from sum of pixels under black rectangle.

What are summed in each region (rectangle)? The number of pixels or the intensity of all pixels in that region?

Thank you so much @ThorbjornSomod. Then, the LBP operation represents the operation that calculates a new decimal value for a specific pixel based on its neighbors (as you described) and the LBPH just measures the occurrence of each decimal value (after applying the LBP operation) creating a histogram. If I understood correctly the histogram is used to decrease the data dimension, is that correct?

For example:

Thinking of a grayscale image after applying the LBP we can get the following pixels:

pixels: 20, 20, 12, 15, 20, 12, 15, 13, 20, 12, ...

Applying the histogram approach we can get:

pixels: 12, 13, 15, 20, ...
occurrence: 3, 1, 2, 4, ...

Is there a difference between Local Binary Patterns (LBP) and Local Binary Patterns Histograms (LBPH) or is it the same thing?

I'm just a little confused because in the documentation it appears sometimes as LBP and other times as LBPH.

Note: this is just a conceptual doubt.