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Wikipedia-based Image Text Dataset 37.5 million image-text examples with 11.5 million unique images across 108 Wikipedia languages. 11,500,000 image, caption Pretraining, image captioning 2021 [11] Srinivasan e al, Google Research Visual Genome Images and their description 108,000 images, text Image captioning 2016 [12] R. Krishna et al.
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).
Fiji (software), an image processing package based on ImageJ; KNIME - an open-source data mining environment supporting image analysis developed in close collaboration with the next generation of ImageJ; List of free and open-source software packages; Microscope image processing
The Image Processing Handbook by John C. Russ, ISBN 0-8493-7254-2 (2006) Image Processing and Analysis - Variational, PDE, Wavelet, and Stochastic Methods by Tony F. Chan and Jianhong (Jackie) Shen, ISBN 0-89871-589-X (2005) Front-End Vision and Multi-Scale Image Analysis by Bart M. ter Haar Romeny, Paperback, ISBN 1-4020-1507-0 (2003)
Computational imaging is a set of imaging techniques that combine data acquisition and data processing to create the image of an object through indirect means to yield enhanced resolution, additional information such as optical phase or 3D reconstruction.
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.
Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration. It originated in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition.
Methods used are based on machine learning, building a discriminative classifier based on numeric features computed from the image. Features are either generic features from computer vision , such as Haralick texture features or features specially designed to capture biological factors (e.g., co-localization with a nuclear marker being a ...