Image Processing
Description
Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. 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. The generation and development of digital image processing are mainly affected by three factors: first, the development of computers; second, the development of mathematics (especially the creation and improvement of discrete mathematics theory); third, the demand for a wide range of applications in environment, agriculture, military, industry and medical science has increased.
Problems Within Domain
- (3-Dimensional, i.e. project onto a 2D plane)
- 2-dimensional Convex Hull
- 2-dimensional Convex Hull, Dynamic
- 2-dimensional Convex Hull, Online
- 2-dimensional array representation
- 2-dimensional space, $l_m$ (or $l_\infty$) norm
- 2-dimensional space, Euclidean metric
- 3-dimensional Convex Hull
- Blob Detection
- Convex Polygonal Window
- Convex Polyhedral Window
- Corner Detection
- Counting number of intersection points, line segments
- Culling
- Diffuse Reflection
- Image Compositing
- Image Segmentation
- Line Drawing
- Line Simplification
- Lossless Compression
- Lossy Compression
- Mesh Parameterization
- Mesh Simplification
- Point-in-Polygon
- Polygon Clipping with Arbitrary Clipping Polygon
- Rasterization
- Ray Tracing
- Rectangular Window
- Reporting all intersection points, convex polygons
- Reporting all intersection points, general polygons
- Reporting all intersection points, generalized segments
- Reporting all intersection points, line segments
- Specular Reflection
- Texture Synthesis
- d-dimensional Convex Hull
- k-dimensional space, $l_m$ (or $l_\infty$) norm