| rdfs:comment
| - Computer vision is a branch of artificial intelligence that attempts to let computers recognize and “understand” an image. Some common techniques to advance the field of computer vision include various methods such as pattern recognition, image processing, and graph theory. An example of a practical usage of computer vision is the camera tracking, used in cinema. Medical machines that utilize computer vision are also valued highly.
- In the broadest sense, image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.
- Image processing is the manipulation of digitally encoded image data. Two of the most common categories of image processing include image data compression and image enhancement. Typical processes include resizing, cropping, sharpening, rotating, and adjusting color or contrast.
- Image processing is the application of signal processing techniques to the domain of images — two-dimensional signals such as photographs or video. Most of the signal processing concepts that apply to one-dimensional signals — such as resolution, dynamic range, bandwidth, filtering, etc. — extend naturally to images as well. However, image processing brings some new concepts — such as connectivity and rotational invariance — that are meaningful or useful only for two-dimensional signals. Also, certain one-dimensional concepts — such as differential operators, edge detection, and domain modulation — become substantially more complicated when extended to two dimensions.
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| abstract
| - Image processing is the application of signal processing techniques to the domain of images — two-dimensional signals such as photographs or video. Most of the signal processing concepts that apply to one-dimensional signals — such as resolution, dynamic range, bandwidth, filtering, etc. — extend naturally to images as well. However, image processing brings some new concepts — such as connectivity and rotational invariance — that are meaningful or useful only for two-dimensional signals. Also, certain one-dimensional concepts — such as differential operators, edge detection, and domain modulation — become substantially more complicated when extended to two dimensions. A few decades ago, image processing was done largely in the analog domain, chiefly by optical devices. Optical methods are inherently parallel, and for that reason they are still essential to holography and a few other applications. However, as computers keep getting faster, analog techniques are being increasingly replaced by digital image processing techniques — which are more versatile, reliable, accurate, and easier to implement.
- Computer vision is a branch of artificial intelligence that attempts to let computers recognize and “understand” an image. Some common techniques to advance the field of computer vision include various methods such as pattern recognition, image processing, and graph theory. An example of a practical usage of computer vision is the camera tracking, used in cinema. Medical machines that utilize computer vision are also valued highly.
- In the broadest sense, image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.
- Image processing is the manipulation of digitally encoded image data. Two of the most common categories of image processing include image data compression and image enhancement. Typical processes include resizing, cropping, sharpening, rotating, and adjusting color or contrast.
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