The Red Channels

"I am a man with thousands of people, everyone is on the blacklist." - Zero channel comedian "distinctive" red channel: illegal and blind observation of an American unique small black book 213 pages Among them, the red channel has not found anything that is shocking, important, or suspicious. Published in 1950 by an American business consultant, this small book seems like an engaging resume that initially includes a comprehensive list of the person's name, profession, and the person's work.

The above picture is an example. The red channel is mainly in the highlight area, most of the red channel is on the right side of the histogram. This indicates that the bright part of the photo is mainly composed of red. The main highlight of this image is often red.

Figure 43: The image is composed of images of K (mapped to red channel), J (green channel), I (blue channel) taken with the VLT and the Subaru telescope. The red luminescence at the center of the image represents the far infrared (100 μm) of the galaxies in the cluster core as measured by Herschel's PACS instrument; in this picture a strict smoothing is applied. • Figure 44 was published by ESA on July 28, 2014, showing that the nearby M33 galaxy blossomed with the birth of the star. The spiral galaxy M33, also called the triangle galaxy, is one of our closest universe neighbors that is only 3 million light years apart. Next to the Andromeda Galaxy (M31) and our Milky Way Galaxy there are about 3 billion big cluster, about 4 billion stars. 56)

If necessary, you can distinguish between two images based on histogram differences. As expected, the total bin value of the R histogram 1 (red channel) of the blue car is larger than that of the blue car's R histogram. The strength of the entire box (blue channel) is larger than the function of B histogram 1 of the red car. In the HOG feature quantity descriptor, the distribution (direction gradient) (direction gradient) (histogram) in the gradient direction is used as the feature quantity. The gradation of the image (derivatives of x and y) is useful because it is known that the gradient around the edge and the corner (region of rapid intensity change) is large and there are more information on the shape of the object than the flat area is.