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Computer vision vs Image Processing

Computer Vision and Image Processing are both technologies that involve the use of computer programming on images. They can be thought of as two different methods of using computational power to process visual data. While they both carry the same name, their uses and scientific functions are very different.

In this article, we will discuss Computer Vision, Image Processing and the differences between them.

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Computer Vision

In the field of Artificial Intelligence (AI), Computer vision is the ability of computers to process visual information with some automated function, such as object recognition or tracking. Nowadays, people use computer vision for a variety of applications, including:

  • Automatic targeting systems
  • Automated image analysis
  • Quality control on products (machines)
  • Identifying faces in photographs
  • Scanning barcodes and QR codes

Computer vision is a branch of computer science that involves the study of computer systems and their interactions with the physical world. It is also used in image processing but serves a different purpose. Imagine if you were walking down the street and an object was moving towards you. You would perceive it as an object moving towards you. A computer vision program can already recognise this motion and put into words what it is seeing.

Though computers and programming are used in these technologies, their functions are very different. Computer vision is used for identifying objects based on object models or feature recognition. Computers can be programmed to see an object in many ways, making them helpful in finding out what is going on in the world around us.

Computer vision requires a lot of data that it repeatedly analyses until it finally starts to recognise images. Two technologies play a significant role in the working of Computer vision – Deep learning and Convolutional Neural Networks (CNN). CNN is generally used for single images, and in the case of videos, a Recurrent Neural Network (RNN) is used.

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Image Processing

Image processing is a technology that has existed for quite some time now, though it has come a long way since then. Image processing has many uses, one of them being the creation of a program that can recognise certain features of an image. It is used in many places today, from security cameras to ATMs.

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Image Processing is when an image is transformed into a digital format to extract information from it by performing specific operations on it. Image processing systems apply a predetermined signal processing method to all 2D images.

The image can be compared to the object visually (visually) or mathematically (mathematically). Visually, it is a comparison of the original image to an enhanced superimposed view of the same image. Algebraically, it is a comparison of mathematical representations of images and signatures –

  • Digitisation
  • Registration and Visual Comparison
  • Mathematical comparison
  • Object search and retrieval

There are multiple applications of image processing:

1) Processing of different types of images: There are various types of medical images like CT scan (conventional CT Scan, dual-source CT), MRI (Magnetic Resonance Imaging), Ultrasound scan, PET scan, SPECT scan etc.
2) Retrieve abnormal images: This can be done by comparing the 2D images of a normal and abnormal person.
3) Cluster analysis: This is workable in the case of a large dataset or when you want to compare similar image patches (like blobs).
4) Video processing: We can detect abnormalities in multi-frame CT scans by comparing the frames.
5) Fuzzy logic based segmentation using normalised cuts is a popular method for segmenting medical images.

Most computer vision algorithms are linear filters that take some form of an image as input, modify it, and produce an output. When applying certain predetermined filtering methods, these operations usually treat all images as two-dimensional signals.

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Computer vision vs Image processing

A combination of both image processing and computer vision would help us better understand our surroundings. Our eyes and perception are not up to par with today’s technology, which accounts for the fact that we see many things as moving objects or as non-existent objects, such as ghosts or even images of people who aren’t in the room. In order to perceive the world around us, we need computer vision programs that can identify shapes and recognise the movement in an already existing picture. Images can then be processed and stored to help us recognise what we are seeing.

It almost seems like both perform very similar tasks; the differences between Computer vision and Image processing are:

ParameterComputer VisionImage Processing
FocusProcessing images and videos to extract information from them. Processing raw images and enhancing them for other tasks.
Methods/Technologies usedImage Processing, Machine Learning, CNN, RNN.Anisotropic diffusion, Different Filtering, Hidden Markov models, Independent component analysis.
Input vs OutputInput – The image
Output – Understanding the image and contents.
Input – Image
Output – Image after transformations such as stretching, sharpening, smoothing, and contrasting.
Real-life applicationsObject detection, Handwriting recognition, Face recognition, Image classification, and Object tracking. Rescaling images, Correcting illumination, Changing tones, Comparing images.

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