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63

The color burst is also an indicator that there is a color signal. This is for compatibility with black and white signals. No color burst means B&W signal, so only decode the luminance signal (no croma). No signal, no color burst, so the decoder falls back to B&W mode. Same idea goes to FM stereo/mono. If there is no 19 kHz subcarrier present, ...

24

In the absence of a valid color burst signal, the "color killer" circuit disables the color difference signals, otherwise you would indeed see colored noise. This is mainly intended for displaying weak signals in B/W without the colored noise. One step further is to mute the entire signal, substitute stable sync signals, and display a blue or black field ...

10

This paper (pdf download) gives the following formulae for calculating Correlated Color Temperature (CCT). They do not explicitly say (or I missed it), but their example leads me to infer that they are assuming RGB values in the range of 0-255. 1. Convert the RGB values to CIE tristimulus values (XYZ) as follows: X = (-0.14282)(R) + (1.54924)(G) + (-0....

8

Probably you've noticed that primarities are $\mathbf{X}$, $\mathbf{Y}$, $\mathbf{Z}$, not $\mathbf{R}$, $\mathbf{G}$, $\mathbf{B}$ (which are corresponding to the color values $R$,$G$,$B$). This is the aftermath of original work conducted by Wright and Guild yielded the Color Matching functions: $r(\lambda)$, $g(\lambda)$, and $b(\lambda)$ having a negative ...

5

When you take an RGB Image matrix and convert the color into HSV Color Model the color is represented on Cylinder. Now, the intensity (Lightness / Value) is the height on this Cylinder which is going from black to white and basically sets the Gray Color of the neutral color (One which blends RGB in the same intensity). Saturation is the Radius and ...

4

You can try first the following (no segmentation): Process the signal in small chunks (say 10ms to 50ms in duration) - if necessary with a 50% overlap between them. Compute the spectral centroid on each chunk. Apply a non-linear function to the spectral centroid value to get a uniform distribution of the palette color used. Logarithm is a good start. ...

4

Okay I found some ideas about using Fourier Transforms in Object Recognition. What I am unable to understand is however, its downsides. Why was the method discarded. For those who are interested in reading about how Fourier Transforms were applied in Object Recognition , look for Fourier Mellin Transforms. The Fourier-Mellin transform is a useful ...

4

HSV is one of the color space transforms that separate color from intensity. Depending on your application (my understanding is contrast enhancement is somehow related to image segmentation in your OP), there are several advantages of HSV: Histogram equalization of a color image is suggested only on the intensity component; The Saturation component is ...

4

There are method which are called Contrast Preserving Decolorization. Those methods are built to keep the contrast in the Color image in the converted image. They are based on Optimization Problem Solving which creates a Contrast Model in the color space and in the gray scale space. For instance, have a look on that:

4

edit: to be clear this answer describes why Lab can be described as a decorrelated color space. This does not imply that decorrelation is the main benefit of using Lab (see many answers on why Lab is useful) If you plot all the RGB colors of a standard RGB image of the natural world you will notice something (see below), the values tend to fall on a ...

3

You should read about LAB Color Space. Its main advantage on RGB is by having 1 channel dedicated to the Luminosity of the Image and 2 others dedicated to the color information. This means if you need to process only Luminosity / Color you can do that using LAB without having any effect on Color / Luminosity.

3

Generally speaking, a change of color space is essentially a nonlinear transform that stretches and distorts the coordinates. The transform is also continuous, except maybe on a singularity line or half-plane. It does not preserve the distances, but preserves the neighborhoods. In the case of RGB > Lab, you can see it as a linear transform (change of basis, ...

3

For 24-bit RGB colors: R=1,G=0,B=0 would be maximally saturated (100%) red, but at the lowest intensity. R=255,G=254,B=254 would be very high intensity, but very low saturation (most people wouldn't even notice the slight red-ness of the near white).

3

This is a crucial question. You are evoking the standard linear transformations. There is a rich literature on the topic, you can for instance with: Ford and Roberts, 1998: Colour Space Conversions Poynton, 1997: Color FAQ Poynton, 1997: Gamma FAQ

3

Colors specified in CIE LMS don't have more or less meaning than colors specified in other CIE based color spaces. CIE LMS coordinates can be readily transformed between CIE XYZ, CIE LAB etc using standard matrices. However some applications are more suited to some color spaces than others, and LMS has its uses. In LMS color space, the color matching ...

3

In PAL, the colour information (chrominance or chroma) is modulated onto the black and white (luminance or luma) baseband signal. The chroma is at ~4.4MHz offset from DC and is about 1.3 MHz wide. Assuming that your noise is centered around DC then, if it is less than ~3.5MHZ wide then it won't appear in the chroma spectrum and will only be in the luma. ...

3

Focus on the first equation for EY. Back in the day when color television was being developed, the color signal had to be compatible with black and white TVs and vice versa. So the compatible brightness signal (luma Y) has to be calculated from the three primary color signals (R, G B) for transmission. Human visual system does not perceive brightnesses of ...

2

What you're looking for is a color map. As its name implies, this process maps an input (single-component, or grayscale) value within a predetermined range to some other color based upon some mapping. The implementations I've seen typically use linear RGB interpolation between a number of control points specified throughout the allowed input range. The color ...

2

As you're probably starting to realise this is a very big field with a number of different methods which you can try. If you want to try to use colour, you can try transforming from RGB to HSV space, which attempts to decouple the colour from the "lightness", this may improve your lighting inconsistencies. The next problem is how to track your object. If ...

2

Stationary processes have a spectrum that's time-independent. As the Wikipedia link shows, the colored noises each have a characteristic spectrum, are therefore not time-dependent, and thus stationary.

2

Here you have two options. 1) Color calibrate your cameras (radiometric calibration) to the same reference. Then try matching. There are many academic works on this topic, one of them being: http://research.microsoft.com/en-us/um/people/yasumat/papers/rankcalib_PAMI12_preprint.pdf 2) Use a vignetting invariant (or color invariant) feature descriptor. For ...

2

Other than searching for a color space, why don't you just first apply a color correction and then get the hue space? If illumination is an issue, you might consider an illumination invariant color correction: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.108.6859&rep=rep1&type=pdf Then simple convert your image to HSV and get H channel. ...

2

HSV has very intuitive axes (hue, saturation, value) and is useful for manual choosing and adjustment of colors. It is defined in relation to an arbitrary RGB color space. In CIELAB, only the lightness (L) axis has an intuitive meaning (in my opinion). Euclidean distances in CIELAB coordinates approximate perceptual distances of colors, which can be useful. ...

2

Intuitively, HSV is the place to easily define Skin Color Hues. Yet there is a broad work on that and even articles about the optimal Color Space for Skin Detection. Yet, you should have a look at OPTIMUM COLOR SPACES FOR SKIN DETECTION (Alternative at IEEE - Optimum color spaces for skin detection). According to them there is no difference in the ...

2

Say you have an object that is blue, and you adjust the brightness of a lamp in the room, or you move the object so that it does not get as much light. In RGB, all the three components (red, green, blue) will change but in HSV mostly the V (value) component changes. So to recognize pixels belonging to the object by its color, you only need the H (hue) and S(...

2

HSV decomposes colors into hue, saturation and value components. This representation allows us to select color ranges in more natural way. For example, you can select wider range for the value component, making your application less sensitive to the light conditions. In the example from your link they define lover_blue and upper_blue as the bounds and use ...

2

Saturation is usually one property of three when used to determine a certain color and measured as percentage value. Saturation defines a range from pure color (100%) to gray (0%) at a constant lightness level. A pure color is fully saturated. From a perceptional point of view saturation influences the grade of purity or vividness of a color/image. A ...

2

Saturation mixes the original color with white to make varying degrees of pastel color. Intensity/Value mixes the above color with black to make varying degrees of brightness.

2

Yes, the "Whitening" is basically filtering the signal using an LTI System. The Result would be the signal $\mathbf{s}$ filtered by the system which whitens the noise. In the framework of Matched Filter this is OK since you will be able to filter the Reference Signal as well and hence have the same performance. Yet for other usages you must take under ...

2

Unfortunately no signal processing method could provide you what you wanted. Instead you must use a hardware optical IR filter that would block the incoming IR waves before they reach into your sensor. A little physical insight would help you understand why this is so: An optical image is an array (matrix) of intensity values captured by the sensor pixels ...

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