Researching - Image Segmentation with Watershed Algorithm

SUCH AS MULTI-FOCUS OR MULTI-CUE FOR CAPTURE IMAGE

MULTI-CAMERASAPPLICATION

Professor: Pei-Jun LeeResearcher: Bui Trong An

Weekly report 105.11.23

Outline

- Progressing- Has implemented- Implement in this week- Implement in next week- Issue

Progressing

Reference: Multi-focus image fusion with dense SIFT – Published: May 2015

Has implementedSetup Environment development

Setup environment development mobile app and OpenCV Library for Coding.

Convert Source Image to SIFT ImageInput source image from mobile device to application. Then use OpenCV library converted source image to SIFT image.

Implement in this weekP1: Initial segmentation - Convert Image to Initial segmentation in Progressing Fig.2

- Research algorithm- Library algorithm in OpenCV- Apply to Mobile Project- Run and test with mobile device – Android)

Implement in this weekResearch algorithm

- Initial segmentation- Image Segmentation with Watershed Algorithm

http://docs.opencv.org/3.1.0/d3/db4/tutorial_py_watershed.html

Implement in this weekImage segmentation

•Image segmentation is an aspect of image processing and is a com-puter vision process.

•The goal of segmentation: is to simplify and/or change the repre-sentation of an image into something that is more meaningful and easier to analyze.

https://en.wikipedia.org/wiki/Image_segmentation

Implement in this weekMethod Image segmentation

•Thresholding•Clustering methods•Compression-based methods•Histogram-based methods•Edge detection•Dual clustering method•Region-growing methods•Partial differential equation-based methods•Variational methods•Graph partitioning methods

https://en.wikipedia.org/wiki/Image_segmentation

Implement in this weekWatershed Algorithm

http://cmm.ensmp.fr/~beucher/wtshed.html

The segmentation paradigm

Segmenting an image by the watershed transformation is therefore a two-step process: 

• Finding the markers and the segmentation criterion (the crite-rion or function which will be used to split the regions - it is most often the contrast or gradi-ent, but not necessarily). 

• Performing a marker-controlled watershed with these two ele-ments. 

Implement in this weekWatershed Algorithm in OpenCV and apply to Mo-bile.

Imgproc.watershed(image,markers);

http://stackoverflow.com/questions/11435974/watershed-segmentation-opencv-xcode/11441676#11441676

Implement in this weekRun and test with Mobile device - Android

Implement in next weekFocus algorithm and progressing

• Watershed Algorithm - detailed• Multi input ( 2 source image) and compile to P1

Outline

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Implement in this weekFundamentals

Let R represent the entire region occupied by an image.

Image segmentation partitions R into n subregions R1, R2, ….., Rn,

such that

a. U Ri = R

b. Ri is a connected set, i = 1, 2, …., n

c. Ri n Rj = ø for all i & j, i ≠ j

d. Q(Ri) = TRUE for i = 1, 2, ….., n

e. Q(Ri U Rj) = FALSE for any adjacent regions Ri & Rj.

Implement in this weekFundamentals

Q(Rk) is a logical predicate over the points in set Rk

Explanation:

Every pixel must be in a region.

Points in a region be 4- or 8- connected.

Regions must be disjoint

Q(Ri) = TRUE if all pixels have same intensity level.

Two adjacent regions Ri & Rj must be different in the sense of predicate

Q.