Capturing and optimising digital images for research

1 / 15

# Capturing and optimising digital images for research - PowerPoint PPT Presentation

Capturing and optimising digital images for research. Gilles Couzin. Introduction. Who are you? What experience do you have of creating Web sites? What are your reasons for attending this course?. Learning objectives. By the end of this course, you will be able to:

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## Capturing and optimising digital images for research

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Capturing and optimising digital images for research

Gilles Couzin

Introduction
• Who are you?
• What experience do you have of creating Web sites?
• What are your reasons for attending this course?
Learning objectives

By the end of this course, you will be able to:

• Explain the digitisation process
• Set scanning parameters to the purpose of your scan
• Use Serif PhotoPlus to:
• Crop images and correct perspective problems
• Sharpen images
• Scale images
• Use four different graphic file formats for the right purpose
Introduction to digital imaging
• The binary counting system
• From analogue to digital
• The digital image
• Spatial resolution
• Colour resolution (bit depth)
• Colour representation
The binary counting system
• A system that has only two variables, 0 and 1
• Each digit is called a bit (binary digit)
• All computer input is converted into strings of binary code of variable lengths
• An eight-bit number such as 11011101 = 1 byte
From analogue to digital (1)
• Traditional photographic images are analogue – i.e. they have continuous tones and in theory may contain an infinite number of colours and brightness.
• Digitisation is the process of converting the information contained in an analogue image into binary data.
• Scanners and digital cameras are the most common method for ‘capturing’ digital images.
From analogue to digital (2)

Continuous bright- ness curve of an analogue image

Same curve after digitisation into 16 discrete levels (4-bit)

The digital image
• A digital image is a rectangular grid of pixels (or picture elements)
• A pixel refers to the dots of light on a computer monitor and to the smallest, basic component of a digital image
• Each pixel is part of a mosaic of many thousand or millions of pixels that form the image
Spatial resolution (1)
• The quantity of pixels in a defined area (dpi or ppi).
• The frequency at which samples are taken during the scanning process from the analogue image (spi).
• The number of pixels is the only attribute that counts; physical size expressed in inches/cm is irrelevant.
• Increasing the number of samples improves the visual quality of the image but…
• …there is a point at which adding more samples has little visual benefit and some distinct disadvantages.
• Digital cameras: one mega pixel = one million pixels (in the whole image, not per inch).
Spatial resolution (2)

50 x 35 pixels = 1750 total; 5:1

250 x 175 pixels = 43,750 total; 1:1

Colour resolution (bit depth)
• Measures how much colour information is available to display or print each pixel in an image:
• A pixel with a colour depth of 1 bit has 2 (21) levels (B&W)
• A pixel with a colour depth of 4 bit has 16 (24) levels
• A pixel with a colour depth of 8 bit has 256 (28) levels

256 greys (8-bit/pixel)

Black & white (1-bit/pixel)

16 greys (4-bit/pixel)

Colour representation (1)
• What are colours?
• the way our brain, by use of our eyes, interprets electromagnetic radiation originating from sun light (white light).
• the part of the electromagnetic spectrum that our eyes can actually detect ("visible light") stretches from between 380 and 780 nanometres in wavelength.
Colour representation (2)
• Two models of colour representation:
Colour representation (3)
• Colours on a computer monitor:
• Use the RGB model
• 8-bit (256 colours) per channel
• Any colour can be represented by a specific combination of 3 numbers comprised between 0 and 255 – for example:R255 + G255 + B0 = Yellow
• A 24-bit pixel (8 bit per channel) can display up to roughly 16.7 million, possible colours!
Colour representation (3)
• RGB colour workspaces:
• Refers to the gamut (range ofcolours that can be displayedor printed by a specific device
• sRGB: standard colour spacefor computer monitors, webbrowsers, etc.
• Adobe RGB (1998):recommended RGB editingspace for print output, beforeconversion to CMYK