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Digital Image Processing AND Analysis


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Definitions

Image Processing
Image Analysis (Image Understanding)
Computer Vision


Low Level Processes: contrast manipulation
Mid-Level Processes: segmentation, recognition
High Level Processes: understanding groups of objects

Important Stages in Image Processing

Image Acquisition
Preprocessing
Segmentation
Representation and Description
Recognition and Interpretation
Knowledge base


Image Acquisition

Imaging sensor & capability to digitize the signal collected by the sensor

Video camera
Digital camera
Conventional camera & analog-to-digital converter


Segmentation

To partition the image into its constituent parts (objects)

Autonomous segmentation (very difficult)
Can facilitate or disturb subsequent processes

Output (representation):
Raw pixel data, depicting either boundaries or whole regions (corners vs. texture for example)
Need conversion to a form suitable for computer processing

(Description)

Digital Image Processing


Some Definitions

With reference to the following figure, we define a system as a unit that converts an input function f(x) into an output (or response) function g(x), where x is an independent variable, such as time or, as in the case of images, spatial position. We assume for simplicity that x is a continuous variable, but the results that will be derived are equally applicable to discrete variables.
It is required that the system output be determined completely by the input, the system properties, and a set of initial conditions. From the figure in the previous page, we write

System Characterization

is called the impulse response of H. In other words, h(x, ) is the response of the linear system to a unit impulse located at coordinate x (the origin of the impulse is the value of  that produces (0); in this case, this happens when  = x).
This expression is called the convolution integral. It states that the response of a linear, fixed-parameter system is completely characterized by the convolution of the input with the system impulse response. As will be seen shortly, this is a powerful and most practical result.
These results are the basis for all the filtering work done in Chapter 4, and some of the work in Chapter 5 of Digital Image Processing. Those chapters extend the results to two dimensions, and illustrate their application in considerable detail.