Week 0 Post 0: Introduction to Image Processing — IP Series

Source: https://hindi.theindianwire.com/wp-content/uploads/2018/07/image-processing.jpg

Do you wish to understand how are images analyzed, infact, how are even images stored, the various file formats, etc.? Then you’ve come to the right place. Here is the beginning of “IP Series” short for Image Processing Series which includes a series of Blog Posts explaining the concepts of Image Processing along with some code to implement the same on a computer. So let’s not wait anymore and dive right in.

So… What is Image Processing?

Image processing is a field of computer science that aims to improve the quality, accuracy, and efficiency of digital images. It involves the use of algorithms and techniques to transform, analyze, and extract useful information from images. Image processing has numerous applications in various fields, including computer vision, medical imaging, robotics, and even the entertainment industry.

“Well, now that you’ve given me an overview of it, I understood what it means but I still don’t get why we need to learn this…” or something along those lines is what you’re probably thinking right now. And, if so, then you’re right, Why… do we need to learn this?

Applications of Image Processing in the real world

Well, as I’d already mentioned, there are a plethora of applications for Image Processing. This might even be an understatement. Some of the main applications include:

1. Medical imaging: Image processing is widely used in the medical field for tasks such as diagnosis, treatment planning, and surgery guidance. For example, CT (Computed Tomography) scans and MRI (Magnetic Resonance Imaging) are commonly used to produce detailed images of the inside of the body. Image processing techniques are used to enhance the quality of these images, such as removing noise and improving contrast.
2. Computer vision: Computer vision is the field of artificial intelligence that deals with how computers can interpret and understand the visual world. Image processing is a crucial part of computer vision, as it is used to extract features and patterns from images and video. This can be used for tasks such as object recognition, facial recognition, and autonomous vehicle navigation.
3. Robotics: Image processing is also used in robotics for tasks such as object detection and tracking. For example, a robot may use image processing to identify and pick up specific objects from a conveyor belt.
4. Entertainment industry: Image processing is also used in the entertainment industry, such as in the production of movies and video games. Image processing techniques can be used to create special effects, such as adding realistic looking explosions or changing the weather in a scene.
5. Surveillance: Image processing is also used in surveillance systems to automatically detect and track objects or people. This can be used in applications such as security and traffic monitoring.

“WOAH!! Now I see why the hype for this field!” is what you’re probably thinking right now, or maybe even saying it out loud! Well, I am getting excited about this, as I am writing this blog post and reminding myself of the beauties about Image Processing.

Now let us understand the Image Processing Pipeline.

The Image Processing Pipeline

The following steps describe the basic steps in the image processing pipeline:

1. Acqusition and storage: The image needs to be captured using a photosensitive sensor (ex., camera) and stored on same device (such as a hard disk) as a file(ex., JPEG File).
2. Load into memory and save to disk: Here we will convert the image to a numerical representation for applying various Image Processing algorithms and then saved post processing of the image in a specific file format.
3. Manipulation, enhancement, and restoration: Here we discuss about running some preprocessing functions to provide better analysis of the image. Preprocessing is the first step in any image processing workflow. It involves cleaning and preparing the images for further analysis. Preprocessing can include tasks such as cropping, resizing, and color space conversion.
   Filtering is a technique used to enhance the quality of an image by removing unwanted features or noise. There are various types of filters, such as low-pass filters, high-pass filters, and edge detection filters.
   Edge detection is another important image processing technique. Edges are points in an image where there is a significant change in pixel intensity. Edge detection filters are used to identify and highlight these edges, which can be useful for tasks such as object recognition and segmentation. One popular edge detection filter is the Canny edge detector. The Canny edge detector uses gradient magnitude and direction to detect edges.
4. Segmentation: Segmentation is the process of dividing an image into multiple regions or segments. Each segment corresponds to a different object or background in the image. Segmentation can be useful for tasks such as object recognition and tracking. There are various techniques for image segmentation, including thresholding, clustering, and contour detection. Thresholding involves setting a threshold value to separate the pixels in an image into foreground and background. Clustering involves grouping pixels into clusters based on their features. Contour detection involves identifying the outlines of objects in an image.
5. Information extraction/representation: The image needs to be represented in some alternative form; for example, one of the following:

• Some hand-crafted feature-descriptor can be computed (for example, HOG descriptors, with classical image processing) from the image.
• Some features can be automatically learned from the image (for example, the weights and bias values learned in the hidden layers of a neural net with deep learning)
• The image is going to be represented using that alternative representation
• And much more…

1. Image understanding/interpretation: This representation will be used to understand the image better with the following:

• Image Classification
• Object Recogniton

From my original published post at https://blog.gadityaramesh.com

Well that’s it for this post!

Well, well, well, we’ve covered a part of the fundamental concpets of Image Processing. And if you’re reading this part (without skipping the above, that is), then consider yourselves equipped with concepts of conversational acceptance. (And Thank you so much for reading till the end).

That’s it folks for this post. Look forward for post “Week 0 Post 1 …” (the content will be a suprise, for me as well, as I haven’t decided on the title yet) 🙂.

Originally published at https://blog.gadityaramesh.com on January 4, 2023.