Background elimination review

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This document discusses background elimination techniques which involve three main steps: object detection to select the target, segmentation to isolate the target from the background, and refinement to improve the quality of the segmented mask. It provides an overview of approaches that have been used for each step, including early methods based on SVM and more recent deep learning-based techniques like Mask R-CNN that integrate detection and segmentation. The document also notes that segmentation is challenging without object detection cues and discusses types of segmentation as well as refinement methods that use transformations, dimension reduction, and graph-based modeling.

Background Elimination
Tough Introduction to Background Elimination
Licking Review

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

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

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!

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/

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

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

Segmentation
Before market capture, segmenting market is important

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

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

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

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

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

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

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

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

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

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

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

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

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