Difference Between Similar Terms and Objects

Difference Between Computer Vision and Deep Learning

Over the last few decades or so, the then-technologies of the future like AI and machine vision have now become mainstream embracing many applications, ranging from automated robot assembly to automatic vehicle guidance, analysis of remotely sensed images and automated visual inspection. Computer vision and deep learning are among the hottest topics these days with every tech industry and even start-ups rushing to head on the competition.

What Is Computer Vision?

Computer Vision is an interdisciplinary field of artificial intelligence that enables computers to process, analyze and interpret the visual world. There is a massive number of objects exists in the real world and while different objects might have similar visual appearance, it’s the subtle details that separate them from each other. Image recognition is regarded as the most common application in computer vision. Well, the idea is to make computers to identify and process images the same way as the human vision does. The ease with which human vision processes and interprets images is truly impeccable. Computer visions aims to pass this characteristic trait of humans onto computers so that computers would understand and analyze complex systems just like humans would do or even better.

What is Deep Learning?

Deep learning is a subset of machine learning and AI based on artificial neural networks that seeks to mimic the functioning of the human brain so that computer would learn what comes naturally to humans. Deep learning is concerned with algorithms inspired by the structure of the human brain that enables machines to gain some level of understanding and knowledge just the way human brain filters information. It defines model parameters for decision making process mimicking the understanding process in the human brain. It is a way of data inference in machine learning and together, they are among the major tools of modern AI. It was initially developed as a machine learning approach to deal with complex input-output mappings. Today, deep learning is a state of the art system used across many industries for various applications.

Difference between Computer Vision and Deep Learning

Concept

 – Computer vision is a subset of machine learning that deals with making computers or machines understand human actions, behaviors, and languages similarly to humans. The idea is to get machines to understand and interpret the visual world so that they make sense out of it and derive some meaningful insights. Deep learning is a subset of AI that seeks to mimic the functioning of the human brain based on artificial neural networks.

Purpose 

– The purpose of computer vision is to program a computer to interpret visual information contained within image and video data in order to make better sense of the digital data. The idea is to translate this data into meaningful insights, using contextual information provided by humans in order to make better business decisions and solve complex problems. Deep learning has been introduced with the objective of moving machine learning closer to AI. DL algorithms have revolutionized the way we deal with data. The goal is to extract features from raw data based on the notion of artificial neural networks.

Applications

 – The most common real world applications of computer vision include defect detection, image labeling, face recognition, object detection, image classification, object tracking, movement analysis, cell classification, and more. Top applications of deep learning are self driving cars, natural language processing, visual recognition, image and speech recognition, virtual assistants, chatbots, fraud detection, etc.

Computer Vision vs. Deep Learning: Comparison Chart

Summary

Deep learning has achieved remarkable progress in various fields in a short span of time, particularly it has brought a revolution to the computer vision community, introducing efficient solutions to the problems that had long remained unsolved. Computer vision is a subfield of AI that seeks to make computers understand the contents of the digital data contained within images or videos and make some sense out of them. Deep learning aims to bring machine learning one step closer to one of its original goals, that is, artificial intelligence.

Is computer vision part of deep learning?

The link between computer vision and machine learning is very fuzzy, as is the link between computer vision and deep learning. In a short span of time, computer vision has shown tremendous progress, and from interpreting optical data to object modeling, the term deep learning has started to creep into computer vision, as it did into machine learning, AI, and other domains.

What is computer vision with deep learning?

Many traditional applications in computer vision can be solved through invoking deep learning methods. Computer vision seeks to guide machines and computers toward understanding the contents of digital data such as images or videos. 

Is computer vision machine learning?

Computer vision is a subset of machine learning, and machine learning is a subfield of AI. Computer vision trains computers to make sense of the visual world as the human vision does. While computer vision uses machine learning algorithms such as neural networks, it is more than machine learning applied. They are closely related to each other, but they are not the same.

Why is computer vision so hard?

Computer vision is a challenge because it is limited by hardware and the way machines see objects and images is very different from how human see them and interpret them. Machines see them as numbers representing individual pixels, making it difficult to make them understand what and how we see things.

What is the role of computer vision?

Computer vision is the science of making computer or machines understand human actions, behaviors, and languages similarly to humans. Computer vision has an amazing diversity of real world applications such as autonomous driving, biometric systems, pedestrian protection system, video surveillance, robotics, medical diagnosis, and more. 

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References :


[0]Gad, Ahmed Fawzy. Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy. New York, United States: Apress, 2018. Print

[1]Hassaballah, Mahmoud and Ali Ismail Awad. Deep Learning in Computer Vision: Principles and Applications. Florida, United States: CRC Press, 2020. Print

[2]Davies, E.R. Computer Vision: Principles, Algorithms, Applications, Learning. Massachusetts, United States: Academic Press, 2017. Print

[3]Bhattacharyya, Siddhartha, et al. Deep Learning: Research and Applications. Berlin, Germany: Walter de Gruyter, 2020. Print

[4]Image credit: https://commons.wikimedia.org/wiki/File:AI-ML-DL.png

[5]Image credit: https://live.staticflickr.com/8573/16361602656_cdc76b2b8f_b.jpg

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