Digital images are electronic snapshots taken of a scene or scanned from documents, such as photographs, manuscripts, printed texts, and artwork. The digital image is sampled and mapped as a grid of dots or picture elements (pixels). Each pixel is assigned a tonal value (black, white, shades of gray or color), which is represented in binary code (zeros and ones). The binary digits ("bits") for each pixel are stored in a sequence by a computer and often reduced to a mathematical representation (compressed). The bits can then be interpreted and read by the computer to produce an analog version for printing, or subjected to image analysis to extract further information. When image acquisition and analysis are done in real time to provide decision-making data for an electromechanical system, we refer to the process as machine vision.
Detailed article:Image capture
Digital image capture (digitization) is the process of creating a digital image file directly using a camera or scanner. An original image can also be digitized indirectly via an analogue intermediary such as a photograph. The digitization process requires both hardware and software. The choice of hardware will be primarily dependent on the nature of the source image and the intended quality of capture. Components required for image capture include:
- A digital or analog camera (black-and-white or color) with suitable optics for acquiring images
- Camera interface for digitizing images (widely known as a frame grabber)
- A digital signal processor (DSP), often a PC or embedded processor.
- (Often all of the above are combined within a single device, sometimes called a smart camera).
- Suitable light source(s) (ambient light, LED illuminators, fluorescent or halogen lamps etc.)
- Input/Output hardware or communication links (e.g. firewire, network connection or RS-232)
Detailed article:Image files
Digital image file formats are standardized means of organizing and storing images. Image files are composed of either pixel or vector (geometric) data that are rasterized to pixels when displayed. The pixels that compose an image are ordered as a grid (columns and rows); each pixel consists of numbers representing magnitudes of brightness and colour. There are five main types of image files; JPG, GIF, TIFF, PNG, BMP, plus many others. Part of the reason for the plethora of file types is the need for compression. Image files can be quite large, and larger file types mean more disk usage and slower downloads. Compression is a term used to describe ways of cutting the size of the file. Another reason for the many file types is that images differ in the number of colors they contain. If an image has few colors, a file type can be designed to exploit this as a way of reducing file size.
Detailed article:Image analysis
Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. The classical problem in computer vision, image processing and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity. This task can normally be solved robustly and without effort by a human, but is still not satisfactorily solved in computer vision for the general case: arbitrary objects in arbitrary situations.
Digital Imaging Applicatons
Detailed article:Digital Imaging Applications
There are many types of applications of digital imaging in laboratory automation. Automatic Identification, Machine vision and guidance, Error detection and Assay detection technologies to name just a few.
- Automated Imaging Association (AIA)
- SPIE - The International Society for Optical Engineering
- Wikipedia:Machine Vision Glossary
Online machine vision glossary repositories:
- Image Labs International's Glossary
- Navitar Glossary of Terms
- RoboRealm Machine Vision Glossary
- MachineVisionOnline.org Glossary
- Prosilica Glossary of Terms
- Visionary: A dictionary for the study of vision
- Computervision wiki
- The Computer Vision Homepage
- Keith Price's Annotated Computer Vision Bibliography
- British Machine Vision Association
- European Machine Vision Association
- Machine Vision Online
Computer vision laboratories
- Oak Ridge National Laboratory Image Science & Machine Vision Group
- ETH Zürich Computer Vision Laboratory
- MMVL MediaWiki
- Kingston University's Digital Imaging Research Centre (DIRC)
- Probilistic and Statistical Inference Group @ University of Toronto
- MVL UWE Bristol UK
- On-Line Compendium of Computer Vision
- Tutorial to Image Processing
- Introduction to computer vision (464KB pdf file)
- iKnow Vision interactive tutorial
|Click [+] for other articles on||The Market Place for Lab Automation & Screening||Imaging Systems Imaging Software|
|Click [+] for other articles on||The Market Place for Lab Automation & Screening||Analytical Equipment, Measuring, Testing Detection Devices|