Background elimination review

Mar 14, 2019Download as PPTX, PDF0 likes297 views

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
/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…
/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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Object recognitionakkichester

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Bb32351355IJERA Editor

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Pc Seminar JordiUniversitat de Barcelona

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This document provides an overview of visual object recognition. It begins with an introduction explaining why object recognition is a challenging problem and discusses the importance of recognizing objects from different viewpoints, scales, textures, etc. It then describes how recognition can be achieved using local image features rather than analyzing the whole object. The document focuses on the Scale Invariant Feature Transform (SIFT) approach, outlining the key stages of detecting local features, generating invariant representations of those features, and verifying matches between images based on geometric configuration. Overall, the summary provides a high-level view of object recognition techniques with a focus on the seminal SIFT method.

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In this paper a new approach for invariant recognition of broken rectangular biscuits is proposed using fuzzy membership-distance products, called fuzzy moment descriptors. The existing methods for recognition of flawed rectangular biscuits are mostly based on Hough transform. However these methods are prone to error due to noise and/or variation in illumination. Fuzzy moment descriptors are less sensitive to noise thus making it an effective approach invariant to the above stray external disturbances. Further, the normalization and sorting of the moment vectors make it a size and rotation invariant recognition process .In earlier studies fuzzy moment descriptors has successfully been applied in image matching problem. In this paper the algorithm is applied in recognition of flawed and non-flawed rectangular biscuits. In general the proposed algorithm has potential applications in industrial quality control.

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Analysis of Classification Approachespaperpublications3

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Sketch Based Image Retrieval using Deep Learning based Machine LearningIRJET Journal

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Cs231n convolutional neural networks for visual recognitionvidhya DS

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AISF19 - Unleash Computer Vision at the EdgeBill Liu

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Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals

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Background elimination review

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 /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… /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

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