review papers and applications for background removal segmentation, matting, and refinement

1. Background Elimination
Tough Introduction to Background Elimination
Licking Review

2. Justice Define ( or Assumption )
2. Segmentation
Background Extermination flow ==
1. Select Target
Auto select
- by Category
- by Saliency
- …?
3. Refinement
User Input
- Shape based
- Line
- Crop
- Hint Mask
- Or just Category?
DL Models
- Pixelwise ||
Kernelwise
segmentation
- etc …
Algorithm
- Graph based
- Thresholding
- etc …
One Segmentation Model can’t be
perfect!
-> See Next page

3. Why two step Segmentation is better?
CNN
- Good at detect semantic
Graph based
- Good at Segmentation
More Accurate boundaries
or can be more robust
Neural Net output Chan-vese output
Pixelwise

4. Human’s Background Elimination
https://www.youtube.com/watch?v=0ve6JhrZBC4 (Disney’s Nogada)
Background Elimination = Object Detection ( selecting target : if auto)
+ Segmentation ( segmenting target )
+ Refinement ( improve quality of segmented mask )
간단해 보이지만 자그마치 3가지 컴비네이션이 들어간 기술!

5. Object Detection
Targeting the market

6. Object Detection is in Image Classification
https://www.slideshare.net/ssuserafc864/deep-learning-atoc-with-image-perspective
2 step method : extract region -> classification
It was SVM…?
SVM->NN
https://blog.lunit.io/2017/06/01/r-cnns-tutorial/

7. Or may be not…
https://www.slideshare.net/ssuserafc864/deep-learning-atoc-with-image-perspective
1 step method : extract region + classification

8. And the King has come - MaskRCNN
Classification + Segmentation + Object Detection

9. Segmentation
Before market capture, segmenting market is important

10. Segmentation
Image segmentation is the process of partitioning a digital image into
multiple segments
Low-level vision
http://scikit-image.org/docs/stable/auto_examples/segmentation/plot_thresholding.html#sphx-glr-auto-examples-
segmentation-plot-thresholding-py

11. Why Segmentation is Hard?
From 고양이책
?
Before deep learning, segmentation conducts without object detection
== Pixel wise segmentation! Wow… tough huh?

12. Types of Segmentation
1. Pixel-based Segmentation
Low-level vision
http://scikit-image.org/docs/stable/api/skimage.segmentation.html
Image -> Segmentation

13. Types of Segmentation
2. Guided Segmentation
High-level vision
User-friendly Interactive Image Segmentation through Unified Combinatorial
User Inputs ( 2010 )
Image + Hints(matting, co-, shape, …) -> Segmentation

14. Types of Segmentation
3. Semantic Segmentation
High-level vision
Image -> Segmentation + Class

15. Deep Image Matting
Matting + Image -> Segmentation -> Refinement

16. DeepMask / SharpMask
Image -> Segmentation
Learning to Segment Object Candidates (2015) Learning to Refine Object Segments (2016)
Segmentation +
scoring ( 0 to 1 )
(i) the patch contains an object roughly centered in the input patch
(ii) the object is fully contained in the patch and in a given scale range

17. Or Not DL…

18. Refinement
Nano marketing

19. Sharpening the mask
Segmentation Mask Refinement Using Image Transformations (2017)

20. Semantic Soft Segmentation
DeepLab with
TripleNet Network ( for L2 )
128 dimension (b) PCA to 3 (b) guided filter
-> dimension
reduction
https://github.com/iyah4888/SIGGRAPH18SSS https://github.com/yaksoy/SemanticSoftSegmentation
RefinementSegmentation

21. Semantic Segmentation Refinement by Monte Carlo Region Growing of
High Confidence Detections (2018)

22. DEEP LOGISMOS: DEEP LEARNING GRAPH-BASED 3D
SEGMENTATION OF PANCREATIC TUMORS ON CT
SCANS ( 2018 )
Use GMM for refinement
( remove false positive )

23. B.E. Related Product
https://enumcut.com/
Of course Photoshop
Allibaba’s Luban function