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Have a look at computer vision and image processing & see the difference between them
When a human eye hits the photons of light it transmits the signals to the brain, so making this whole process in the machine is challengeable. The motivation behind the modern-day machine vision system lies at the core of emulating human vision for recognizing patterns, faces and rendering 2D imagery from a 3D world into 3D.
Computer Vision and Image processing both are very exciting fields of Computer Science. Computer vision is a field of artificial intelligence that deals with computers and systems to derive high-level understanding from digital images, videos, and other visual inputs. Digital image processing is the use of a digital computer to process digital images through an algorithm. if the goal is to enhance the image for later use, it’s image processing. And if the goal is to recognize objects, defect for automatic driving, then its computer vision.
Differences between image processing and computer vision:
Computer vision: Computer vision comes from modeling image processing using the techniques of machine learning. it is focused on extracting information from the input images or videos to have a proper understanding of them to predict the visual input like the human brain. Here can distinguish between objects, classify them, sort them according to their size. Image processing is one of the methods that is used for computer vision along with other Machine learning techniques, CNN, etc. Computer Vision is a superset of Image Processing. Computer Vision applications will be on Object detection, Face detection, Handwriting recognition, etc. Computer vision uses image processing algorithms to solve some of its tasks. The ultimate goal here is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs.
Image Processing: Digital image processing was pioneered at NASA in the 1960s, to convert analog signals from the Ranger spacecraft to digital images with computer enhancement. Anisotropic diffusion, Hidden Markov models, independent component analysis, Different Filtering, etc are the methods used in this. It can distinguish between objects, classify them, sort them according to their size. Image Processing is a subset of Computer Vision. Image Processing has a wide range of applications some of them are Rescaling image Correcting illumination, Changing tones, etc. Image Processing is mostly related to the usage and application of mathematical functions and transformations are applied to an input image and an output image is returned. The transformation used depends on the context and issue to be solved.
Image processing algorithms transform images like sharpening, smoothing, filtering, enhancing, restoration, blurring, and so on. Computer vision focuses on making sense of what the machines see. A computer vision system inputs an image and outputs images based on some specific tasks, such as object labels and coordinates. The terms computer vision and image processing are often used interchangeably in many contexts as they both involve computations on images. But they are not the same thing. Both of them work together in many cases. computer vision systems rely on image processing algorithms. Image processing involves the processing of raw input images and enhancing them, or preparing them to do some specific tasks.