Image Processing
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Learn about Digital Image Processing

What is Digital Image Processing?

Image processing is a way to convert images to a digital aspect and perform certain actions like improving an image. The input for image processing is a video frame or image and the output is a processed image.

Image processing involves the following three steps:-

  • Capturing an image with an optical scanner or image sensor.
  • Analysis and image management including image compression and enhancement.
  • Visual detection patterns and algorithms used in cases such as satellite imagery.

Image processing is a way by which an individual can enhance the quality of an image or gather alerting insights from an image and feed it to an algorithm to predict things later.

The development of digital image processing is mainly affected by three factors: first is the development of computers, second is the development of mathematics and related algorithms. Finally, the demand for a wide range of applications in the environment, agriculture, medical science and military.

Implications of Image Processing

Image processing is a way to do something working on an image to get an enhanced image or to cut out some useful information from it. Image processing mainly involves the following three steps:

  • Importing an image with image detection tools
  • Exploring and manipulating the image
  • An outcome where it can be improved or reported that based on image analysis.

The data collected is mainly unprocessed raw data, which means it should not be used for applications directly. Therefore, we need to analyse it first and do the necessary processing in advance before applying it.

Steps involved in Image Processing

Image Processing steps
  1. Image Acquisition: This is the first digital step in image processing. Then, the image is converted into digital form by scaling and colour conversion.
  2. Image Enhancement: Image enhancement is the process of switching digital images to more results suitable for display or multiple image analysis. It is easier to extract subjective details in an image.
  3. Image Restoration: Image Restoration is the process of taking a noisy image and restoring it based on the degree of degradation.
  4. Colouring Image Processing: Colour Image Processing requires an understanding of the physics of light as well as colour vision.
  5. Image compression: It involves developing some functions and operations by rescaling image size and resolution.
  6. Morphological processing: It brings out the components and tools for extracting useful shapes and representations.
  7. Segmentation: It is the breaking apart of an image into smaller constituents for processing. It is one of the hardest processes in it.
  8. Object recognition: It detects and identifies each subject in the image and gives them a label suitable for it, assigned by the descriptor algorithm.

First, you should have a basic knowledge of the program in any language. Second, you need to know what reading materials are and how they work, as we will be using other machine learning algorithms for this image processing. As a bonus, it may help if you have experienced exposure or basic Open CV information before continuing with this course. But this is not necessary.

If you are working with a colour image, you should know that there will be three channels – Red, Green, and Blue(RGB). Therefore, there will be three such matrices for that one image.

To get started we need some Python knowledge and a small OpenCV domain.

  • Python – Although there are many courses available online, I found dataquest.io to be the best python learning platform, for beginners and experienced alike.
  • OpenCV – Similar to python, OpenCV also has many online tutorials. The only site I find that I often talk about is the official documents.
  • HaaR Cascades – OpenCV unveils special ways to train our custom algorithms to find anything interesting in image capture. HaaR cascade is those files that contain that trained model.

Applications of Digital Imaging

  • Facial Detection – Everybody must have seen cameras auto-focussing on people’s faces while even tracking motion. This is one of the biggest example of image processing in our daily life.
Live focussing seen in cameras
  • Intelligent Transportation Systems – This method can be used for autonomous number identification and identification of traffic signs.
  • Remote Sensing – In this application, the sensors take pictures of the earth’s surface on remote sensing satellites or a scanner mounted on an aircraft. The images are processed and transferred to a global channel later used in planning the resources and terrain.
Remote sensing using satellite imagery detecting the river stream
  • Moving a tracking object – This app enables you to measure movement limits and get a visual record of a moving object. The different types of tracking are:
    • Active tracking .
    • Awareness based on awareness.
  • Error Identification – This application identifies faulty items in electrical or electronic systems. A high amount of heat energy is caused by these faulty components. Infra red images are produced by the distribution of heat energy in the assembly. Errors can be detected by analysing infrared red images.

Conclusion

Image processing is a vast topic with a variety of applications in day-to-day life. Interfacing to make a working model can be achieved with the aforementioned modules. OpenCV has various example codes supported by many programming languages that can be implemented with the right tools. Try visiting this site to get started with the basics of image processing coding with a cool face detection application to impress your friends.

To take it one step further and recognize individual faces – perhaps to detect and recognize your face amongst many strangers – the task is surprisingly difficult. This is mainly due to a large amount of image pre-processing involved. But if you are willing to tackle the challenge, it is possible by using machine learning algorithms as described here.

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