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6 different real multiple choice-based exams (735 answer sheets and 33,540 answer boxes) to evaluate computer vision techniques and systems developed for multiple choice test assessment systems. None 735 answer sheets and 33,540 answer boxes Images and .mat file labels Development of multiple choice test assessment systems 2017 [204] [205]
The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection.The technique counts occurrences of gradient orientation in localized portions of an image.
The Computer Vision and Image Processing Algorithm Test and Analysis Tool, CVIP-ATAT, creates human and computer vision applications. Its primary use is to execute algorithms for processing multiple images at a time, incorporating various algorithmic and parameter variations. The program determines a suitable algorithm for pre-processing ...
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.
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.
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 ...
GLOH (Gradient Location and Orientation Histogram) is a robust image descriptor that can be used in computer vision tasks. It is a SIFT-like descriptor that considers more spatial regions for the histograms. An intermediate vector is computed from 17 location and 16 orientation bins, for a total of 272-dimensions.
In computer vision and image processing, Otsu's method, named after Nobuyuki Otsu (大津展之, Ōtsu Nobuyuki), is used to perform automatic image thresholding. [1] In the simplest form, the algorithm returns a single intensity threshold that separate pixels into two classes, foreground and background.