Introduction

Types of Digital Image Processing

History

Basic Concepts

Image functions

Digital image properties

Uses

Conclusion

computer converts the analogue image, in this case a videotape, to a digital image by dividing it into a microscopic grid and numbering each part by its relative brightness.

Specific image processing programs can then radically improve the contrast, for example by stretching the range of brightness throughout the grid from black to white, emphasizing edges, and suppressing random background noise that comes from the equipment rather than the document.

In order to simplify the task of computer vision understanding, two levels are usually distinguished; low-level image processing and high level image understanding

Many of the techniques of digital image processing, or digital picture processing as it was often called, were developed in the 1960s at the Jet Propulsion Laboratory, MIT, Bell Labs, University of Maryland, and few other places, with application to satellite imagery, wire photo standards conversion, medical imaging, videophone, character recognition, and photo enhancement.

In the 1970s, digital image processing proliferated, when cheaper computers Creating a film or electronic image of any picture or paper form.

A signal is a function depending on some variable with physical meaning.

Signals can be ◦ One-dimensional (e.g., dependent on time),

◦ Two-dimensional (e.g., images dependent on two co-ordinates in a plane),

◦ Three-dimensional (e.g., describing an object in space),

Or higher dimensional.

Pattern recognition aims to classify data(patterns) based on either a prioriknowledge or on statistical information extracted from the patterns.

The image can be modeled by a continuous function of two or three variables.

Arguments are co-ordinates x, y in a plane, while if images change in time a third variable t might be added.

The image function values correspond to the brightness at image points.

The function value can express other physical quantities as well (temperature, pressure distribution, distance from the observer, etc.).

Metric properties of digital images

Topological properties of digital images

Distance:

The distance between two pixels in a digital image is a significant quantitative measure.

Pixel adjacency:

4-neighborhood 8-neighborhood Border and edge:

The border is a global concept related to a region, while edge expresses local properties of an image function

Topological properties of images are invariant to rubber sheet transformations.

Convex hull is used to describe topological properties of objects.

The convex hull is the smallest region which contains the object, such that any two points of the region can be connected by a straight line, all points of which belong to the region.

A scalar function may be sufficient to describe a monochromatic image.

vector functions are to represent color images consisting of three component colors.

Further, surveillance by humans is dependent on the quality of the human operator and lot off actors like operator fatigue negligence may lead to degradation of performance.

These factors may can intelligent vision system a better option.

As in systems that use gait signature for recognition in vehicle video sensors for driver assistance.