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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
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Moments that changed communities, movements, institutions and the nation.
In August 1908, a white mob attacked Springfield, Illinois’s Black community, destroying homes and businesses and lynching two Black men. National outrage over the violence helped spur the movement that created the NAACP the following year.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
Image color transfer is a function that maps (transforms) the colors of one (source) image to the colors of another (target) image. A color mapping may be referred to as the algorithm that results in the mapping function or the algorithm that transforms the image colors. The image changing process is sometimes called color transfer or, when grayscale images are involved, brightness transfer function (BTF); it may also be called photometric camera calibration or radiometric camera calibration.
The term image color transfer is a bit of a misnomer since most common algorithms transfer both color and shading. (Indeed, the example shown on this page predominantly transfers shading other than a small orange region within the image that is adjusted to yellow.)
There are two types of image color transfer algorithms: those that employ the statistics of the colors of two images, and those that rely on a given pixel correspondence between the images. In a wide-ranging review, Faridul and others [1] identify a third broad category of implementation, namely user-assisted methods.
An example of an algorithm that employs the statistical properties of the images is histogram matching. This is a classic algorithm for color transfer, but it can suffer from the problem that it is too precise so that it copies very particular color quirks from the target image, rather than the general color characteristics, giving rise to color artifacts. Newer statistic-based algorithms deal with this problem. An example of such algorithm is one that adjusts the mean and the standard deviation of each of the source image channels to match those of the corresponding reference image channels. This adjustment process is typically performed in the Lαβ or Lab color spaces.[2]
A common algorithm for computing the color mapping when the pixel correspondence is given is building the joint-histogram (see also co-occurrence matrix) of the two images and finding the mapping by using dynamic programming based on the joint-histogram values.[3]
When the pixel correspondence is not given and the image contents are different (due to different point of view), the statistics of the image corresponding regions can be used as an input to statistics-based algorithms, such as histogram matching. The corresponding regions can be found by detecting the corresponding features.[4]
Liu[5] provides a review of image color transfer methods. The review extends into considerations of video color transfer and deep learning methods including Neural style transfer.
Color transfer processing can serve two different purposes: one is calibrating the colors of two cameras for further processing using two or more sample images, the second is adjusting the colors of two images for perceptual visual compatibility.
Color calibration is an important pre-processing task in computer vision applications. Many applications simultaneously process two or more images and, therefore, need their colors to be calibrated. Examples of such applications are: Image differencing, registration, object recognition, multi-camera tracking, co-segmentation and stereo reconstruction.
Other applications of image color transfer have been suggested. These include the co-option of color palettes from recognised sources such as famous paintings and the use as a further alternative to color modification methods commonly found in commercial image processing applications such as ‘posterise’, ‘solarise’ and ‘gradient’.[6] A web application has been made available to explore these possibilities.
The use of the terms source and target in this article reflects the usage in the seminal paper by Reinhard et al.[2] However, others such as Xiao and Ma[7] reverse that usage and indeed it seems more natural to consider that the colors from a source image are directed at a target image. Adobe use the term source for the color reference image in the Photoshop Match Color function. Because of this confusion over terminology, some software releases may incorporate incorrect functionality.[8] To minimise further confusion, it may be good practice henceforth to utilise terms such as input image or base image or content image and color source image or color palette image respectively.
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Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
Image color transfer is a function that maps (transforms) the colors of one (source) image to the colors of another (target) image. A color mapping may be referred to as the algorithm that results in the mapping function or the algorithm that transforms the image colors. The image changing process is sometimes called color transfer or, when grayscale images are involved, brightness transfer function (BTF); it may also be called photometric camera calibration or radiometric camera calibration. The term image color transfer is a bit of a misnomer since most common algorithms transfer both color and shading. (Indeed, the example shown on this page predominantly transfers shading other than a small orange region within the image that is adjusted to yellow.)
In computer graphics, a palette is the set of available colors from which an image can be made. In some systems, the palette is fixed by the hardware design, and in others it is dynamic, typically implemented via a color lookup table (CLUT), a correspondence table in which selected colors from a certain color space's color reproduction range are assigned an index, by which they can be referenced. By referencing the colors via an index, which takes less information than needed to describe the actual colors in the color space, this technique aims to reduce data usage, including processing, transfer bandwidth, RAM usage, and storage. Images in which colors are indicated by references to a CLUT are called indexed color images.
Color grading is a post-production process common to filmmaking and video editing of altering the appearance of an image for presentation in different environments on different devices. Various attributes of an image such as contrast, color, saturation, detail, black level, and white balance may be enhanced whether for motion pictures, videos, or still images. Color grading and color correction are often used synonymously as terms for this process and can include the generation of artistic color effects through creative blending and compositing of different layer masks of the source image. Color grading is generally now performed in a digital process either in a controlled environment such as a color suite, and is usually done in a dim or dark environment. The earlier photochemical film process, referred to as color timing, was performed at a film lab during printing by varying the intensity and color of light used to expose the rephotographed image. Since, with this process alone, the user was unable to immediately view the outcome of their changes, the use of a Hazeltine color analyzer was common for viewing these modifications in real time. In the 2000s, with the increase of digital technology, color grading in Hollywood films became more common.
Color printing or colour printing is the reproduction of an image or text in color (as opposed to simpler black and white or monochrome printing).
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →The Greenwood District.
