Fundamentals of Digital Image Processing:
Welcome to our digiskills ! Today, I will focus on image processing. There are endless situations calling for image processing done by software. In general, they can be clustered into a few groups: Checking for presence, object detection and localization, measurement as well as identification and verification. In most cases, the acquired image from the camera is not directly processed within the application. Instead, it is reprocessed, to enhance the image according to the specific task. Examples of reprocessing are noise reductions well as brightness and contrast enhancement. Some of these steps can be done directly by the camera itself, and thus save CPU load on the host side. To use the camera as a measuring device, it must be calibrated to the physical world. Camera calibration can actually refer to two things: geometric calibration and color calibration. With geometric calibration we correct the lens distortion. Furthermore, we can also determine the relationship between the camera’s natural units - meaning pixels - and the real world units, like millimetre inches, for example. With color calibration we ensure an accurate reproduction of colors. The better the reprocessing, the better the image quality and the results of the image processing within your application.
Now let’s have a closer look at image processing. When it comes to locating parts, usually matching is involved. This means looking for regions that are similar to or the same as a predefined template. This template can either be also an image a geometric pattern which contains information regarding edges and geometric features. These methods are called correlation pattern matching and geometric pattern matching respectively. Let’s have a look at cookie inspection. Your template would be the image of a perfectly shaped cookie. A camera takes images of all cookies on the conveyor belt. As soon as there is a cookie that doesn't match the template, the cookie is rejected. The main use of measurement with the help of image processing is in alignment or inspection applications. Most measurement techniques rely on edge-detection algorithms. An edge is an area in an image displaying significant change in the image intensity --or in other words, a high local contrast.
This means your software analyzes the grey levels of the image and based on this, identifies shapes, measures distances and calculates the geometry. This measurement and calculation is made possible by the camera calibration that established the relationship between pixels and real world units. Take the label of a bottle, for example. With measurement, you can check if the label has been placed correctly. Typical applications for identifications bareback and 2D matrix code reading or optical character recognition, also called OCR. One way to manage optical character recognition's by separating the characters in the image and comparing them with a set of templates.
Afterwards, the software can convert the captured data into editable and searchable data. A popular example for OCR is automatic number plate recognition – also known as ANPR. Each of these processing techniques covers wide spectrum of machine vision applications, but combining them can give you even more possibilities. Imagine a car is entering a parking garage; a camera takes an image of the license plate and the car. When the car is exiting the garage, the camera takes another image. Then the software compares those two images. The gate only opens when the license plate and the car model are the same as in the first image.
This was only a short overview of the information you can gain from your images by using image processing, Fully Read Books but it offers a taste of the many different possibilities. Thanks for reading !
Categories:
Computers\\Organization and Data Processing
Year:
2011
Edition:
1
Publisher:
Wiley-Blackwell
Language:
English
Pages:
355
ISBN 10:
0470844736
ISBN 13:
9780470689776
File:
PDF, 5.09 MB
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