Chapter Teacher: Remah W. Al-Khatib

This lecture will cover:  The human visual system  Light and the electromagnetic spectrum  Image representation  Image sensing and acquisition  Sampling, quantisation and resolution

 The best vision model we have!  It is one of the most sophisticated image processing and analysis systems.  Knowledge of how images form in the eye can help us with processing digital images  Its understanding would also help in the design of efficient, accurate and effective computer/machine vision systems.

In the following slides we will consider what is involved in capturing a digital image of a real world scene:  Image sensing and representation  Sampling and quantisation  Resolution

 A typical image formation system consists of an illumination” source, and a sensor.  Energy from the illumination source is either reflected or absorbed by the object or scene, which is then detected by the sensor.  Depending on the type of radiation used, a photo converter (e.g., a phosphor screen) is typically used to convert the energy into visible light.  Sensors that provide digital image as output, the incoming energy is transformed into a voltage waveform by a sensor material that is responsive to the particular energy radiation.  The voltage waveform is then digitized to obtain adiscrete output.

 Incoming energy is transformed into a voltage by the combination of input electrical power and sensor material.

Continuous image to be converted into digital :form Sampling: digitize the coordinate values Quantization: digitize the amplitude values Issues in sampling and quantization, related to.sensors

 Conventions  Origin at the top  left corner  x increases from left to right  y increases from top to bottom  Each element of the matrix array is called a pixel, for picture element

 Matrix form bits to store the image = M x N x k gray level = 2k

 L-level digital image of size MxN  = digital image having a spatial resolution MxN pixels a gray-level resolution of L levels  Spatial resolution determined by sampling Smallest discernible detail in an image  Gray-level resolution determined by number of gray scales Smallest change in gray level

 Down-sampling Up-sampling

 2k-level digital image of size NxN  How K and N affect the image quality

 How many samples and gray levels are required for a good approximation?  Quality of an image depends on number of pixels and gray-level number  i.e. the more these parameters are increased, the closer the digitized array approximates the original image.  But: Storage & processing requirements increase rapidly as a function of N, M, and k

Operations applied to digital images:  Zoom: up-sampling Pixel duplication Bi-linear interpolation  Shrink: down-sampling