Image processing involves treating a two-dimensional image as the input of a system and outputting a modified image or a set of defining parameters related to the image. Modern image processing tends to refer to the digital domain where the color of each pixel is specified by a string of binary digits. But many techniques are common to analog and even optical images.
Image processing involves many transformations and techniques, usually derived from the field of signal processing. There are standard geometric transformations such as enlargement, size reduction, linear translation and rotation. It is possible to modify the colors in images such as enhancing contrasts or even transforming the image into an entirely different color palette according to some specific mapping system. Compositions of images are frequently conducted to merge portions from multiple images. Another area of interest involves interpolation. Basically, images retrieved in some contexts are sparse with missing pixels. Standard techniques involve simply estimating the missing pixels based on the color of the nearest known pixels. More sophisticated techniques may involve using algorithms to judge the missing pixels usually by factoring in the relative colors of all surrounding pixels. Techniques to align images are also quite straightforward. Segmentation tends to involve decomposing images into smaller sections based on some common quality such as color or light intensity. It is possible to extend the dynamic range of photos by combining images that have variation in light exposure. Some of the most sophisticated techniques include morphology and flybys. Morphs involve images literally decomposing and then re-emerging with a different look, like a portrait in which the subject keeps changing. Flybys re-create three dimensional imagery by rotating two dimensional landscapes around. The Holy Grail of image processing tends to be object recognition where software is trained to be able to recognize and categorize the parts of an image based on colors and outlines. Authorities are particularly interested in facial recognition technology.
Image processing is most commonly done in Matlab which allows the input of strings of binary digits for manipulation with pre-defined commands. More powerful software is available for larger data files and more sophisticated applications. Many consumer-oriented products such as Photoshop have built-in functions that allow users to edit images through a graphical user interface. Popular features include cropping of photos to discard unwanted areas and red eye removal which allows the darkening of eyeballs that are distorted by exposure during photos.
There are many application areas of image processing. Perhaps the one most familiar to most of us is in security and surveillance applications. Regulatory authorities have streams of video feed from cameras in public areas. It is not practical to sort through all this data manually to identify suspicious behavior. Police and detective agencies use intelligent software that is able to zoom in on suspicious behavior usually triggered by sounds, the presence of packages for protracted periods of time or clustering of many people. Image processing allows the comparison of people on video surveillance images to suspected rogues. There have been several successful implementation cases where criminals have been identified within large crowds such as sports stadiums through the use of image processing techniques.
Another critical research area is the use of image processing for medicine. Images obtained from medicine include photographing suspected tumors, aberrations in blood flow and fractured areas. Techniques such as magnetic resonance imaging and computer tomography allow the generation of raw images. Traditionally such images had to be painstakingly scoured through by skilled practitioners who were likely to make mistakes or miss subtle variations in the image. Image processing techniques allow the automation of this study to identify sources of malignancy reliably and efficiently. They enable doctors to perform guided surgery by planning their incisions and insertions through the maze of the human body. They allow the setting up of complicated procedures such as blasting radiation at malignant tumors by providing complete information on the presence of both the target as well as innocuous materials surrounding it that need to be avoided.
A third area of significance is dealing with images obtained through remote imagery such as satellites. We are in possession of a wealth of redundant data on the surfaces of planets but need to use image processing to highlight areas of interest for further study. Successful techniques have allowed scientists to judge the presence of craters, soil and atmospheric characteristics.
Image processing involves many transformations and techniques, usually derived from the field of signal processing. There are standard geometric transformations such as enlargement, size reduction, linear translation and rotation. It is possible to modify the colors in images such as enhancing contrasts or even transforming the image into an entirely different color palette according to some specific mapping system. Compositions of images are frequently conducted to merge portions from multiple images. Another area of interest involves interpolation. Basically, images retrieved in some contexts are sparse with missing pixels. Standard techniques involve simply estimating the missing pixels based on the color of the nearest known pixels. More sophisticated techniques may involve using algorithms to judge the missing pixels usually by factoring in the relative colors of all surrounding pixels. Techniques to align images are also quite straightforward. Segmentation tends to involve decomposing images into smaller sections based on some common quality such as color or light intensity. It is possible to extend the dynamic range of photos by combining images that have variation in light exposure. Some of the most sophisticated techniques include morphology and flybys. Morphs involve images literally decomposing and then re-emerging with a different look, like a portrait in which the subject keeps changing. Flybys re-create three dimensional imagery by rotating two dimensional landscapes around. The Holy Grail of image processing tends to be object recognition where software is trained to be able to recognize and categorize the parts of an image based on colors and outlines. Authorities are particularly interested in facial recognition technology.
Image processing is most commonly done in Matlab which allows the input of strings of binary digits for manipulation with pre-defined commands. More powerful software is available for larger data files and more sophisticated applications. Many consumer-oriented products such as Photoshop have built-in functions that allow users to edit images through a graphical user interface. Popular features include cropping of photos to discard unwanted areas and red eye removal which allows the darkening of eyeballs that are distorted by exposure during photos.
There are many application areas of image processing. Perhaps the one most familiar to most of us is in security and surveillance applications. Regulatory authorities have streams of video feed from cameras in public areas. It is not practical to sort through all this data manually to identify suspicious behavior. Police and detective agencies use intelligent software that is able to zoom in on suspicious behavior usually triggered by sounds, the presence of packages for protracted periods of time or clustering of many people. Image processing allows the comparison of people on video surveillance images to suspected rogues. There have been several successful implementation cases where criminals have been identified within large crowds such as sports stadiums through the use of image processing techniques.
Another critical research area is the use of image processing for medicine. Images obtained from medicine include photographing suspected tumors, aberrations in blood flow and fractured areas. Techniques such as magnetic resonance imaging and computer tomography allow the generation of raw images. Traditionally such images had to be painstakingly scoured through by skilled practitioners who were likely to make mistakes or miss subtle variations in the image. Image processing techniques allow the automation of this study to identify sources of malignancy reliably and efficiently. They enable doctors to perform guided surgery by planning their incisions and insertions through the maze of the human body. They allow the setting up of complicated procedures such as blasting radiation at malignant tumors by providing complete information on the presence of both the target as well as innocuous materials surrounding it that need to be avoided.
A third area of significance is dealing with images obtained through remote imagery such as satellites. We are in possession of a wealth of redundant data on the surfaces of planets but need to use image processing to highlight areas of interest for further study. Successful techniques have allowed scientists to judge the presence of craters, soil and atmospheric characteristics.
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