Pengolahan Citra Digital – Digital Image Processing (Digital Image Processing) is a field of science that studies how images are created, processed, and analyzed to provide information that can be understood by humans.
Based on the shape of the location signals, images can be divided into two types, namely analog images and digital images. An analog image is an image created from a continuous analog signal, while a digital image is an image created from a discrete digital signal.
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Analog images are produced from analog image acquisition tools, for example the human eye and analog cameras. Images taken by the human eye and photos or movies captured by analog cameras are examples of analog images. The image has a very good quality and level of detail (resolution) but has weaknesses, including that it cannot be stored, processed, and copied on a computer.
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A digital image is a functional representation of light intensity in a unique form in a two-dimensional plane. An image is composed of a set of pixels
Which has coordinates (x, y) and amplitude f(x, y). The coordinates (x, y) indicate the location/position of the pixels in the image, while the amplitude f(x, y) indicates the color intensity value of the image. A representation of a digital image and its main dimensions is shown in Figure 1 below.
In general, according to the color combination of pixels, images are divided into three types, namely RGB images, grayscale images and binary images. The image in Figure 1 is included in the image category
In the red channel, pure red is represented by a value of 255 and pure black by a value of 0. In the green channel, pure green is represented by a value of 255 and pure black by a value of 0. Likewise in blue. channel, pure blue is represented by a value of 255 and pure black by a value of 0.
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Each pixel in an RGB image has a color intensity that is a combination of three intensity values in the R, G and B channels. For example, a pixel has a color value of 255 in the red channel, 255 in the red channel. green channel, and 0 in blue will produce yellow. In another example, a pixel that has a color value of 255 on the red channel, 102 on the green channel, and 0 on the blue channel will produce an orange color. The number of possible pixel color combinations in a truecolor 24-bit RGB image is 256 x 256 x 256 = 16,777,216. A representation of pixel size values and color combinations R, G, and B is shown in Figure 3.
The second type of image is a grayscale image. A grayscale image is an image in which the size of the pixels depends on the gray scale. In an 8-gray image, the degrees of black to white are divided into 256 degrees of gray where perfect black is represented by a value of 0 and perfect white by a value of 255. RGB images can be converted to grayscale images so that only one color channel is provided. The equation that is generally used to convert a 24-bit truecolor RGB image to an 8-bit grayscale image is:
The third type of image is the binary image. A binary image is an image whose pixels have a minimum bit depth of 1 so that it has two color intensity values, namely 0 (black) and 1 (white). Grayscale images can be converted into binary images through a thresholding process. In the thresholding process, the threshold value is required as the conversion limit value. Pixel size values that are greater than or equal to the threshold value will be changed to 1. Meanwhile, pixel size values that are less than the threshold value will be changed to 0. For example, the threshold value used is 128 . , then pixels that have a power less than 128 will be converted to 0. (black) and those greater than or equal to 128 will be converted to 1 (white).
Threshold is generally used in image segmentation process. This process is done to separate the foreground (something you want) from the background (something else you don’t want). In segment results, the foreground is represented by white (1) and the background is represented by black (0). In the case of segmentation on only one image, we can determine the threshold value by trial and error. But in the case of segmentation on a large number of images, a method is needed to determine the threshold value directly. The threshold value can be obtained directly using the method of Otsu (1979).
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The complete source code files along with images of the above program can be found through the following page: MATLAB Source Code
Posted on July 26, 2017, in Image Processing and tagged what is meant by digital image, matlab software, image processing software, how to use matlab, binary image, digital image, digital image is, examples of matlab programming, matlab programming examples, basics. basic digital image processing, image definition, analog image definition, digital image definition, digital image processing definition, digital image processing, image history, image processing, matlab coding, sample collection matlab programs, matlab programming, matlab introduction, binary image understanding, understanding digital image, understanding grayscale image, understanding rgb image, understanding image processing, understanding digital image processing, image processing, Image Processing Digital Images, image segmentation, digital image thresholding, thresholding using otsu method. Bookmark the permalink. Comment 32. What is digital image processing? A digital image is a functional representation of light intensity in a unique form in a two-dimensional plane. An image is composed of a set of pixels (image elements) that have coordinates (x, y) and amplitude f(x, y).
In general, according to the color combination of pixels, images are divided into three types, namely RGB images, grayscale images and binary images.
In the red channel, pure red is represented by a value of 255 and pure black by a value of 0. In the green channel, pure green is represented by a value of 255 and pure black by a value of 0. Likewise in blue. channel, pure blue is represented by a value of 255 and pure black by a value of 0.
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Each pixel in an RGB image has a color intensity that is a combination of the three intensity values in the R, G and B channels.
For example, a pixel that has a color value of 255 on the red channel, 255 on the green channel, and 0 on the blue channel will produce a yellow color.
In another example, a pixel that has a color value of 255 on the red channel, 102 on the green channel, and 0 on the blue channel will produce an orange color.
The second type of image is a grayscale image. A grayscale image is an image in which the size of the pixels depends on the gray scale.
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In an 8-gray image, the degrees of black to white are divided into 256 degrees of gray where perfect white is represented by a value of 255 and perfect black by a value of 0.
A binary image is an image whose pixels have a depth of 1 bit so that it has two color intensity values, namely 0 (black) and 1 (white).
For example, the threshold value used is 128, then pixels that are smaller than 128 will be changed to 0 (black) and those that are greater than or equal to 128 will be changed to 1 (white).
In MATLAB the threshold value is set in two data classes, to set the threshold value to 128, the value used is 128/256 = 0.5.
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But in the case of segmentation on a large number of images, a method is needed to determine the threshold value directly.
Although the concept of digital image processing is a field of science that studies how images are created, processed, and analyzed to produce information that can be understood by humans.
Digital image processing projects can be implemented by implementing and developing existing image processing techniques along with the latest image processing techniques to obtain an effective image processing system.