Networking and Programming in One

There are various ways to recognize an image. One of the techniques that can be used in image processing are three basic features in the image, namely color, shape, and texture. Different imagery will have different color, shape, and texture features. In this experiment we will discuss one by one feature and how the three features can recognize the image. Color Features Color can distinguish objects in the image. Technically, color is a particular spectrum contained in a perfect light (white). Color is formed from a collection of waves with wavelengths of some basic elements of color. The way the presentation of a mixture of basic elements of color to produce a color is called the color space or color space. There are several kinds of color space in image processing, ie RGB, RG, Normalized RGB, HSV, CIE, CMYK, YCrb, HSL, and TSL. From the several kinds of color space already mentioned, the most often we hear is RGB (Red Green Blue). Based on the RGB value, we can know the values

This time we will try different types of filters for image processing. There are three filter principles in image processing, namely: Low Pass Filter (LPF) is used to remove a different point with its neighboring points, or in other words the noise reduction process. LPF will take data at low frequencies and discard data at high frequencies. High Pass Filter (HPF) is used to maintain a different point with its neighboring points, or in other words edge detection process. HPF will take data at high frequencies and dump data at low frequencies. Band Pass Filter (BPF) is used to maintain a point close to the neighboring points, and a point different from the neighboring points, in other words the sharperness process. BPF will maintain low and high frequencies that are not too low and high. Low Pass Filter for noise reduction process Based on the above three principles, LPF is used to reduce noise on the image. Noise itself has three types, namely gaussian noise, speckle noise,