slic

package module
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 Go to latest Published: May 18, 2014 License: MIT Imports: 5 Imported by: 0

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

Constants

This section is empty.

Variables

This section is empty.

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
}

Source Files

View all Source files

Directories

Path Synopsis
examples
average_colors
slic

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