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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 channels studied were histogram of oriented gradients (HOG), gradient histogram channel (Hist), gradient magnitude (Grad), color channels (RGB, HSV, LUV) and grayscale channel. The performance was evaluated in terms of pedestrian detection rates at the reference point of 10 - 4 fppw (false positive per window).
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 neighborhood ...
Value: oriented gradients of pixel intensities Further, the design of Haar features allows for efficient computation of f w , b ( I ) {\displaystyle f_{w,b}(I)} using only constant number of additions and subtractions, regardless of the size of the rectangular features, using the summed-area table .
The gradient region is sampled at 39×39 locations, therefore the vector is of dimension 3042. The dimension is reduced to 36 with PCA. Gradient location-orientation histogram is an extension of the SIFT descriptor designed to increase its robustness and distinctiveness. The SIFT descriptor is computed for a log-polar location grid with three ...
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated.
Detectors are trained to search for pedestrians in the video frame by scanning the whole frame. The detector would “fire” if the image features inside the local search window meet certain criteria. Some methods employ global features such as edge template , [1] others uses local features like histogram of oriented gradients [2] descriptors ...
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.