slic

README

SLIC

An implementation of the SLIC (simple linear iterative clustering) superpixel generation algorithm

Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. They capture image redundancy, provide a convenient primitive from which to compute image features, and greatly reduce the complexity of subsequent image processing tasks. They have become key building blocks of many computer vision algorithms, such as multi-class object segmentation, depth estimation, segmentation, body model estimation, and object localization. SLIC Superpixels Compared to State-of-the-art Superpixel Methods

Documentation

Index

• func SuperPixelSizeForCount(width, height, count int) int
• type SLIC
  • func MakeSlic(image image.Image, compactness float64, supsz int) *SLIC
  • func (slic *SLIC) AverageColors() (lvec, avec, bvec []float64)
  • func (slic *SLIC) DrawEdgesToImage(img image.Image) image.Image
  • func (slic *SLIC) Run(iterations int)
• type SuperPixel

Functions

func SuperPixelSizeForCount

func SuperPixelSizeForCount(width, height, count int) int

Types

type SLIC

type SLIC struct {
	Superpixels []*SuperPixel
	XStrips     int
	YStrips     int

	Labels []int
	// contains filtered or unexported fields
}

func MakeSlic

func MakeSlic(image image.Image, compactness float64, supsz int) *SLIC

func (*SLIC) AverageColors

func (slic *SLIC) AverageColors() (lvec, avec, bvec []float64)

func (*SLIC) DrawEdgesToImage

func (slic *SLIC) DrawEdgesToImage(img image.Image) image.Image

func (*SLIC) Run

func (slic *SLIC) Run(iterations int)

type SuperPixel

type SuperPixel struct {
	L, A, B float64
	X, Y    float64
	// contains filtered or unexported fields
}