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Artificial Intelligence
Machine Learning
Deep Learning
Computer
Vision
Computer Vision Vs. Image Processing
It’s a common misconception that Computer Vision and Image Processing are the same because both deal with
visual data such as images and videos. However, they serve different purposes within the realm of technology and
Artificial Intelligence (AI).
Let us learn the difference between Computer Vision and Image Processing.
Computer Vision Image Processing
Computer Vision enables machines to understand and Image processing involves manipulating and
interpret visual information, such as identifying objects, enhancing images to improve their quality or
recognising patterns, or making decisions based on visual extract specific features. It focuses on the technical
inputs. It focuses on extracting high-level information to manipulation of raw image data.
mimic human vision. For example, Rescaling Images, Correcting
For example, Object Detection, Handwriting Recognition, Brightness, Changing Tones, Enhancing Edges, etc.
Facial Recognition, etc.
Computer Vision is a superset of image processing. Image processing is a subset of Computer Vision.
It means that Computer Vision encompasses image It means that image processing is one component
processing as one of its components or foundational within the broader domain of Computer Vision.
steps, but extends beyond it to achieve higher-level tasks Image processing provides foundational tools and
like recognition and decision-making. techniques often used in Computer Vision tasks.
It operates at a higher level of abstraction, focusing It operates at a lower level, primarily working with
on deriving meaning from visual inputs and enabling pixel-level data to transform or analyse images
machines to perform tasks like decision-making. without necessarily understanding their content.
Real-world examples Real -world examples
• Self-driving cars recognising road signs and • Enhancing satellite images for better resolution.
pedestrians. • Removing red-eye effects in photos.
• Augmented Reality (AR) and Virtual Reality (VR) • Converting colour images to grayscale.
environments.
• Automated surveillance systems detecting suspicious
activities.
Applications of Computer Vision
Computer Vision is a technology that started in the 1970s. At that time, it was a new and exciting idea, but the
technology wasn’t good enough for everyone to use. People could only dream, how it could change the world.
Computer Vision (Theory) 307

