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In image processing, the input is an image and the output is an image as well, whereas in computer vision, an image or a video is taken as an input and the output could be an enhanced image, an understanding of the content of an image or even behavior of a computer system based on such understanding.
It is widely used in computer vision tasks such as image annotation, [2] vehicle counting, [3] activity recognition, [4] face detection, face recognition, video object co-segmentation. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video.
Optical Recognition of Handwritten Digits Dataset Normalized bitmaps of handwritten data. Size normalized and mapped to bitmaps. 5620 Images, text Handwriting recognition, classification 1998 [147] E. Alpaydin et al. Pen-Based Recognition of Handwritten Digits Dataset Handwritten digits on electronic pen-tablet.
This is the same sense as feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem at hand.
Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. [1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face .
General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.
Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s, at Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone ...