What is Computer Vision and Machine Vision

Computer vision describes the ability of computers or subsystems to identify

objects, scenes and activities in images. To this end, technologies are used

with the help of which the complex image analysis tasks are divided among

as small sub-tasks as possible and then computed. These techniques are

applied to recognise individual edges, lines and textures of objects in one.

Classification, machine learning and other processes, for example, are used

to determine whether the features identified in an image probably represent

an object already known to the system.

Computer vision has multifaceted applications, among them the anal-

ysis of medical imaging to improve prognoses, diagnoses and treatment

of diseases or facial recognition on Facebook, which ensures that users are

automatically recognised by algorithms and are suggested for tags. Such sys-

tems are already used for security and surveillance purposes for the identi-

fication of suspects. In addition, e-commerce companies such as Amazon

are working on systems with which specific products can be identified on

images and subsequently be purchased directly online. Whilst researchers in

the field of computer vision are working on the aim of being able to utilise

systems independent of the environment, with machine vision, sensors are

used with the help of which relevant information can be captured within

restricted environments. This discipline is technically mature to the extent

that it is no longer part of ongoing informatics research, but part of sys-

tem technology today. At the same time, it is less a matter of recognising

the meaning or content of an image but of deriving information relevant for

action.

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Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

What is Machine Learning

The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

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