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Histogram equalization is a popular example of these algorithms. Improvements in picture brightness and contrast can thus be obtained. In the field of computer vision, image histograms can be useful tools for thresholding. Because the information contained in the graph is a representation of pixel distribution as a function of tonal variation ...
YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities 8 million Video, text Video classification 2016 [195] [196] S. Abu-El-Haija et al. YFCC100M Large and diverse labeled image and video dataset Flickr Videos and Images and associated description, titles, tags, and other metadata (such as EXIF and geotags)
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.
Multi-block LBP: the image is divided into many blocks, a LBP histogram is calculated for every block and concatenated as the final histogram. Volume Local Binary Pattern(VLBP): [ 11 ] VLBP looks at dynamic texture as a set of volumes in the (X,Y,T) space where X and Y denote the spatial coordinates and T denotes the frame index.
For example, if applied to 8-bit image displayed with 8-bit gray-scale palette it will further reduce color depth (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images.
An example of histogram matching. In image processing, histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [1] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed. [2]
In more general fields of data processing, such as digital signal processing, it is referred to as dynamic range expansion. [ 1 ] The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization.