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Circular HOG blocks (C-HOG) can be found in two variants: those with a single, central cell and those with an angularly divided central cell. In addition, these C-HOG blocks can be described with four parameters: the number of angular and radial bins, the radius of the center bin, and the expansion factor for the radius of additional radial bins.
The Hough transform is a feature extraction technique used in image analysis, computer vision, pattern recognition, and digital image processing. [1] [2] The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.
Integral Channel Features (ICF), also known as ChnFtrs, is a method for object detection in computer vision.It uses integral images to extract features such as local sums, histograms and Haar-like features from multiple registered image channels.
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.
The circle Hough Transform (CHT) is a basic feature extraction technique used in digital image processing for detecting circles in imperfect images. The circle candidates are produced by “voting” in the Hough parameter space and then selecting local maxima in an accumulator matrix.
In computer vision, speeded up robust features (SURF) is a local feature detector and descriptor, with patented applications. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction.
The problem of finding the object (described with a model) in an image can be solved by finding the model's position in the image. With the generalized Hough transform, the problem of finding the model's position is transformed to a problem of finding the transformation's parameter that maps the model into the image.
Its impulse response is defined by a sinusoidal wave (a plane wave for 2D Gabor filters) multiplied by a Gaussian function. [6] Because of the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function (sinusoidal function) and the Fourier transform of the Gaussian ...