CitationDownload as .RIS
Emerald Group Publishing Limited
Copyright © 2011, Emerald Group Publishing Limited
Image processing and pattern recognition in industrial engineering
Article Type: Viewpoint From: Sensor Review, Volume 31, Issue 2
Along with the information superhighway, digital globe concept’s statement and the internet’s widespread application, image information has become an important source, and an important means, of human access to information. As a result, the demands on image processing and pattern recognition technology grows day by day.
Currently, image processing and pattern recognition have become an object of study and research in areas such as the engineering, computer science, information science, statistics, physics, biology, chemistry, medicine and even in the fields of social science. Therefore, image processing and pattern recognition technology use by other disciplines are inevitably increasing.
Recently, there is a growing demand for image processing and pattern recognition in various application areas, such as remote sensing, multimedia computing, secured image data communication, biomedical imaging, texture understanding, content-based image retrieval, image compression, and so on. As a result, the challenge to scientists, engineers and business people is to quickly extract valuable information from raw image data. This is the primary purpose of image processing and pattern recognition.
In electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. A digital image is composed of a grid of pixels and stored as an array. A single pixel represents a value of either light intensity or color. Images are processed to obtain information beyond what is apparent given the image’s initial pixel values.
Image-processing tasks can include any combination of the following: modifying the image view, adding dimensionality to image data, working with masks and calculating statistics, warping images, specifying regions of interest, manipulating images in various domains, enhancing contrast and filtering, extracting and analyzing shapes, and so on.
Pattern recognition techniques are concerned with the theory and algorithms for putting abstract objects, e.g. measurements made on physical objects, into categories. Methods of pattern recognition are useful in many applications such as information retrieval, data mining, document image analysis and recognition, computational linguistics, forensics, biometrics and bioinformatics.
Pattern recognition is the science and art of giving names to the natural objects in the real world. It is often considered part of artificial intelligence. However, the problem here is even more challenging because the observations are not in symbolic form and often contain much variability and noise: another term for pattern recognition is artificial perception. Typical inputs to a pattern recognition system are images or sound signals, out of which the relevant objects have to be found and identified. The pattern recognition solution involves many stages such as making the measurements, processing and segmentation, finding a suitable numerical representation for the object we are interested in, and finally classifying them based on these representation.
Image processing and pattern recognition technology is also a closely linked with the national economy and science; it has brought huge economy and social efficiency to humanity. In the near future, image processing and pattern recognition technology will have not only a more thorough development theoretically, but will also be an indispensable and powerful tool in the application of scientific research, and for our everyday lives. In our information-based society, image processing and pattern recognition have huge potential, both in theory and in practice.
Zhenyu DuProfessor at the Information Technology and Industrial Engineering Research Center (ITTE), Hong Kong, China