Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.
Concept
Image gradient
Summary
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 . For example, the Canny edge detector uses image gradient for edge detection. In graphics software for , the term gradient or color gradient is also used for a gradual blend of color which can be considered as an even gradation from low to high values, as used from white to black in the images to the right. Another name for this is color progression.
Mathematically, the gradient of a two-variable function (here the image intensity function) at each image point is a 2D vector with the components given by the derivatives in the horizontal and vertical directions. At each image point, the gradient vector points in the direction of largest possible intensity increase, and the length of the gradient vector corresponds to the rate of change in that direction.
Since the intensity function of a digital image is only known at discrete points, derivatives of this function cannot be defined unless we assume that there is an underlying continuous intensity function which has been sampled at the image points. With some additional assumptions, the derivative of the continuous intensity function can be computed as a function on the sampled intensity function, i.e., the digital image. Approximations of these derivative functions can be defined at varying degrees of accuracy. The most common way to approximate the image gradient is to convolve an image with a kernel, such as the Sobel operator or Prewitt operator.
Image gradients are often utilized in maps and other visual representations of data in order to convey additional information. GIS tools use color progressions to indicate elevation and population density, among others.
Computer vision
Image gradients can be used to extract information from images. Gradient images are created from the original image (generally by convolving with a filter, one of the simplest being the Sobel filter) for this purpose.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related publications (1)
An Interactive App for Color Deficient Viewers
Sabine Süsstrunk, Cheryl Shaulya Lau, Nicolas Perdu, Gaurav Sharma
Color deficient individuals have trouble seeing color contrasts that could be very apparent to individuals with normal color vision. For example, for some color deficient individuals, red and green ap
2015Related concepts (3)
Edge detection
Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a at which the image brightness changes sharply or, more formally, has discontinuities. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in , machine vision and computer vision, particularly in the areas of feature detection and feature extraction.
Image 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 . For example, the Canny edge detector uses image gradient for edge detection. In graphics software for , the term gradient or color gradient is also used for a gradual blend of color which can be considered as an even gradation from low to high values, as used from white to black in the images to the right. Another name for this is color progression.
Digital image processing
Digital image processing is the use of a digital computer to process s through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over . It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.
Related courses (1)
CS-442: Computer vision
Computer Vision aims at modeling the world from digital images acquired using video or infrared cameras, and other imaging sensors.
We will focus on images acquired using digital cameras. We will int
Related lectures (16)
Seam Carving: Energy Minimization
Introduces seam carving for image resizing through energy minimization techniques.
Image Processing Basics
Introduces image processing basics in Python, covering manipulation, grayscale conversion, edge detection, and convolution with kernels.
Image Filtering: Classic Filters
Explores classic image filters like Moving Average, Gaussian Blur, Sobel filter, and Laplacian operator.