Search results
Results From The WOW.Com Content Network
Two types of gradients, with blue arrows to indicate the direction of the gradient. Light areas indicate higher pixel values A blue and green color gradient. An image gradient is a directional change in the intensity or color in an image. The gradient of the image is one of the fundamental building blocks in image processing.
The gradient is obtained from an existing image and modified for image editing purposes. Various operators, such as finite difference or Sobel , can be used to find the gradient of a given image. This gradient can then be manipulated directly to produce several different effects when the resulting image is solved for.
Sobel and Feldman presented the idea of an "Isotropic 3 × 3 Image Gradient Operator" at a talk at SAIL in 1968. [1] Technically, it is a discrete differentiation operator , computing an approximation of the gradient of the image intensity function.
This flooding process is performed on the gradient image, i.e. the basins should emerge along the edges. Normally this will lead to an over-segmentation of the image, especially for noisy image material, e.g. medical CT data. Either the image must be pre-processed or the regions must be merged on the basis of a similarity criterion afterwards.
You are free: to share – to copy, ... Uploaded while editing "Image gradient" on en.wikipedia.org: File usage. The following page uses this file: Image gradient;
In mathematical morphology and digital image processing, a morphological gradient is the difference between the dilation and the erosion of a given image. It is an image where each pixel value (typically non-negative) indicates the contrast intensity in the close neighborhood of that pixel.
The resulting gradient field HOG (GF-HOG) descriptor captured local spatial structure in sketches or image edge maps. This enabled the descriptor to be used within a content-based image retrieval system searchable by free-hand sketched shapes. [14]
Let (,) be a point in the original image and (,) be a point in an image formed by convolving with the first kernel and (,) be a point in an image formed by convolving with the second kernel. The gradient can then be defined as: