![]() ![]() Image segmentation procedures are generally followed by this step, where the task for representation is to decide whether the segmented region should be depicted as a boundary or a complete region. Image segmentation allows for computers to put attention on the more important parts of the image, discarding the rest, which enables automated systems to have improved performance. This step involves partitioning an image into different key parts to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. For example, erosion and dilation operations are used to sharpen and blur the edges of objects in an image, respectively. Morphological Processing provides the tools (which are essentially mathematical operations) to accomplish this. Image components that are useful in the representation and description of shape need to be extracted for further processing or downstream tasks. This process saves bandwidth on the servers. Only when you click on the image is it shown in the original resolution. This is also important in displaying images over the internet for example, on Google, a small thumbnail of an image is a highly compressed version of the original. Image Compressionįor transferring images to other devices or due to computational storage constraints, images need to be compressed and cannot be kept at their original size. Images subdivision successively into smaller regions for data compression and for pyramidal representation. Wavelets are the building blocks for representing images in various degrees of resolution. This step aims at handling the processing of colored images (16-bit RGB or RGBA images), for example, peforming color correction or color modeling in images. For example, removing noise or blur from images. This step deals with improving the appearance of an image and is an objective operation since the degradation of an image can be attributed to a mathematical or probabilistic model. Image enhancement is highly subjective in nature. Such techniques are primarily aimed at highlighting the hidden or important details in an image, like contrast and brightness adjustment, etc. In this step, the acquired image is manipulated to meet the requirements of the specific task for which the image will be used. The image is captured by a camera and digitized (if the camera output is not digitized automatically) using an analogue-to-digital converter for further processing in a computer. The fundamental steps in any typical Digital Image Processing pipeline are as follows: 1. Notice that the shapes of the histograms for each of the channels are different.Įxample of changing the “alpha” parameter in RGBA images Phases of Image Processing Similarly, (0, 255, 0) is green and (0, 0, 255) is blue.Īn example of an RGB image split into its channel components is shown below. For example, (255, 0, 0) is the color red (since only the red channel is activated for this pixel). Any combination of numbers in between gives rise to all the different colors existing in nature. Thus, a pixel in an RGB image will be of color black when the pixel value is (0, 0, 0) and white when it is (255, 255, 255). Now, three equal-sized matrices (called channels), each having values ranging from 0 to 255, are stacked on top of each other, and thus we require three unique coordinates to specify the value of a matrix element. That is, two coordinates could have defined the location of any value of a matrix. Up until now, we had images with only one channel. “RGB” represents the Red, Green, and Blue “channels” of an image. That is, 65,536 different colors are possible for each pixel. The images we are used to in the modern world are RGB or colored images which are 16-bit matrices to computers. ![]()
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