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Images of public figures scrubbed from image searching. Name and m/f annotation. 107,818 Images, text Face recognition 2014 [92] [93] H. Ng et al. BioID Face Database Images of faces with eye positions marked. Manually set eye positions. 1521 Images, text Face recognition 2001 [94] [95] BioID Skin Segmentation Dataset
scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language. [2] It includes algorithms for segmentation , geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection , and more. [ 3 ]
In computer vision or natural language processing, document layout analysis is the process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non-textual ones and the arrangement in their correct reading order. [ 1 ]
The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.
Variability in Image Quality: Variations in image quality and resolution, including blurry images and incomplete details, can hinder uniform processing across a database. Object Detection and Recognition : Identifying and recognising objects within images, especially in complex scenarios with multiple objects and occlusions, poses a significant ...
Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]
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).
The affine transformation of a model point [x y] T to an image point [u v] T can be written as below [] = [] [] + [] where the model translation is [t x t y] T and the affine rotation, scale, and stretch are represented by the parameters m 1, m 2, m 3 and m 4. To solve for the transformation parameters the equation above can be rewritten to ...