Table of contents Introduction 2 Understanding what is computer vision 4



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Computer Vision: history and applications

Albert Alemany Font

Helsinki Metropolia University of Applied Sciences

Media Engineering

April 2014

Table of contents


1. Introduction 2

2. Understanding what is computer vision 4

2.1. What is “vision”? 4

2.2. Computer vision and its related disciplines 4



3. History of computer vision 6

4. Applications of computer vision 6

4.1. Face and smile detection 6

4.2. Optical character recognition (OCR) 7

4.3. Smart cars 7

4.4. Medical imaging 8

4.5. Video-based interaction: gaming 8

4.6. Computer vision as a barrier 8

5. Conclusions 9

6. References 10


1. Introduction


According to Aristotle, Vision is knowing what is where by looking, which is essentially valid. Our vision and brain identify, from the information that arrive to our eyes, the objects we are interested in and their position in the environment, which is very important for a lot of our activities. Computer Vision, somehow tries to emulate that capacity in computers, so that by means of the interpretation of the acquired images, for example with a camera, the different objects can be recognized in the environment as well as their position in the space.

The easiness with which we “see”, brought the first artificial intelligence researchers to start thinking, around 1960, that making a computer interpret images was relatively easy, but it turned out to be different [4]. Many years of investigation have proven that it is a very complex subject. However, over the last few years there have been considerable progresses.

Computer vision brings together different fields such as mathematics, physics, biology and engineering. It provides us a better understanding of human vision, how we perceive and interpret things. Our world is surrounded by images and movies, and every time more useful applications are being developed; applications that are touching our lives, making them easier, safer and more fun.

The goal of this thesis is to investigate how computer vision has evolved over the years since it first appeared, and to explore the different applications that have been developed and how they have helped us, improving our lives. Also, in this thesis I will reflect on where computer vision is going to go in the next years and discuss how we should address it from an ethical point of view.



2. Understanding what is computer vision

2.1. What is “vision”?


Vision is the window to the world of many organisms. Its main function is to recognize and localize objects in the environment through image processing. Computational vision is the study of these processes, in order to understand them and to build machines with similar capacities.
There are different definitions of vision. The following ones are among the most important:

“Vision is knowing what is where by looking” (Aristotle)

“Vision is to get from the information of our senses, valid properties from the external world”, Gibson [3].

“Vision is a process that, from images of the external world, it produces a description that is useful to the observer and that doesn’t contain irrelevant information”, Marr [7].



All of these definitions are essentially valid, but maybe the one that is closer to the current idea about computer vision is the definition of Marr. In his definition there are three important aspects that we have to consider: (i) vision is a computational process, (ii) the description obtained depends on the observer and (iii) it is necessary to remove the information that is not useful (information reduction).

2.2. Computer vision and its related disciplines


The term “Computer Vision” has been used a lot in the last few years and it is often mistaken for other concepts. In the Figure 1 the different disciplines and fields related to computer vision are shown.



Figure 1: Computer vision related disciplines

Digital image processing is the process by which taking an image, a modified version of it is produced. In the Figures 1.1 and 1.2 two examples are illustrated. In the first one, the segmentation can be observed, where the goal is to identify from an image the pixels that belong to an object. In that case, the output is a binary image formed by white and black pixels, which means “object” or “no-object”. The second example is about restoration of an image. In that case, a blurry image becomes clearer.






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