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Presentation Transcript

Presentation Process

Presentation Process

Presentation Process

Presentation Process

Presentation Process

Presentation Process

Presentation Process

Presentation Process

Presentation Process

Presentation Process

Isfahan University of Technology

Electrical and Computer Department

Master Thesis of Computer Engineering- Artificial Intelligence and Robotic

Image alignment and stitching using Object Recognition Methods

By: Navid Einackchi

Supervisors: Dr Rasoul Amirfattahi

Dr javad Askari

Advisor: Dr M. Saraee

Spring 2007

Presentation Process

- Introduction
- Definition
- Aligning concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Some slides are from other sources

Presentation Process

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Presentation Process

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Image Alignment and Stitching

- Definition
- Applications
- Mosaic Image
- Panorama
- Virtual Environment
- Robotic

Image Alignment and Stitching

- Definition
- Applications
- Mosaic Image
- Panorama
- Virtual Environment
- Robotic

Image Alignment and Stitching

- Definition
- Applications
- Mosaic Image
- Panorama
- Virtual Environment
- Robotic

Presentation Process

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Aligning Concepts

- Image Transformations
- Applicable Transformations

- Find transformations
- How many parameters?
- Calculating Parameters

Image Transformations

- Transition
- Number of Parameters: 2
- Number of Points: 1

Image Transformations

- Euclidean
- Number of Parameters: 3
- Number of Points: 2

Image Transformations

- Similarity
- Number of Parameters: 4
- Number of Points: 2

Image Transformations

- Affine
- Number of Parameters: 6
- Number of Points: 3

Image Transformations

- Perspective
- Number of Parameters: 8
- Number of Points: 4

Image Transformation Computation

- Direct
- Mapping one image into another using different parameters
- Define an Error Function based on pixel intensity difference

- Using Corresponding points
- Obtaining Corresponding points
- Computing Transformation parameters using corresponding points

Direct Method

Error Function

- How many points?
- How to select?
- Using human
- Automatically

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Stitching

- Stitching
- Choices
- Final Plane
- Pixel weighting

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Object Recognition Methods

- Local Feature Detection
- Edge
- Corner
- Hole

- Interest Regions Matching
- Appearance Matching
- Geometric Matching

- Finding Object Relations in Image

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Salient Features

- Features of Feature!
- Invariants against transformations
- Ability to explain the image
- Ability to being Matched (Repeatability)

- Selected Features
- Corners
- Holes

Harris Detector

Plain

No changes in every direction

Edge

High changes in Edge Direction

Corner

High changes in every direction

Harris Detector(Continued)

- Harris measurement

Harris Detector(Continued)

- Harris measurement response

2

“Edge”

R < 0

“Corner”

R > 0

Local Maximum bigger than Threshold

“Plain”

“Edge”

|R| small

R < 0

1

R

R

x

x

Harris Detector(Continued)- Features
- Invariant against rotation
- Invariant against intensity

Harris Detector(Continued)

- Example

- Scale Problem

Scale Invariant

Finding Appropriate Window Size

=

Scale Selection- Using Differential of Gaussian Filters
- Laplacian of Gaussian (LoG)
- Difference of Gaussian (DoG)

Window thst maximize filter

DoG

y

x

DoG

SIFT Detector- Bulb Detector
- Invariant against scale
- Using DoG
- Bulb detection
- Scale detection

Scale position

SIFT Detector- Detecting Bulb and Scale Simultaneously
- Bulb position = maximum in plain
- Scale = maximum in third dimension

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Descriptor

- Vector which describes region of interest point
- Pixels
- Histogram
- Differential

- Feature of Descriptor
- Invariant against image distortion (view, angle, intensity)
- Able to express similarities and differences

2p

0

SIFT Descriptor- Based on value and direction of gradients
- 128 dimensional Vector
- Construction
- Calculation of value and direction of gradient of each pixel
- Making direction histogram of gradients
- Rotating interest region based on dominant direction
- Dividing the region into 16 region
- Constructing direction histogram for each region

DescriptorFeatures

- Invariant against
- Rotation
- Position
- Affine changes in intensity(I Ia+ b)

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Matching

- Appearance Matching
- Comparing distance of each two descriptors
- Euclidean Distance
- K nearest neighbor

- Geometrical Matching
- Based on geometrical relations between points
- Estimation of image transformation
- Rejecting false corresponding points

Automatically image alignment

- Direct Method
- Based on interest points

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Brown and Lowe Algorithm

O(n4 m4)

Similarity Transform rather than perspective

- Why
- Detector are unable to handle perspective
- Small portion of each images is overlapped
- Increasing the tolerance of true corresponding

- So We use Similarity Transformation

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

First Proposed Algorithm

- Detecting Interest points using Harris detector
- Defining descriptors for each point
- Finding corresponding points using appearance
- Comparing interest points with each other
- K Nearest Neighbor

- Finding relation between 2 images
- Using Similarity
- Based on voting

- Rejecting wrong corresponding points based on geometrical relations

First Proposed Algorithm

- Detecting Interest points using Harris detector
- Defining descriptors for each point
- Finding corresponding points using appearance
- Comparing interest points with each other
- K Nearest Neighbor

- Finding relation between 2 images
- Using Similarity
- Based on voting

- Rejecting wrong corresponding points based on geometrical relations

Concluding

- Very good for rotation
- Good for little scaling
- Weak for big scaling
- Due to Harris detector weakness

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Improvement

- Using less time order algorithm
- Using Scale Invariant Feature Transform

Second proposed Algorithm

- Detecting Interest points
- SIFT Detector

- Finding Corresponding points
- Based on appearance
- Using k-d tree
- Distance ratio threshold

- Finding relation between images
- Voting on step 2 based on Appearance corresponding

- Rejecting wrong corresponding points
- Based on geometric relations between points

Distance Ration Threshold

- Find 1NN and its distance = D1
- Find 2NN and its distance = D2
- Find ratio of these distances (D1/D2 = ratio)
- If Lower than threshold -> reject
- Else accept

Distance Ration ThresholdMy Improvement

- Find 1NN and its distance = D1
- Multiply D1 by ratio = range
- Look for range in k-d tree

Results of threshold on ratio

- Threshold Ratio = 0.5

Results of threshold on ratio

- Threshold Ratio = 0.7

Second proposed Algorithm

- Detecting Interest points
- SIFT Detector

- Finding Corresponding points
- Based on appearance
- Using k-d tree
- Distance ratio threshold

- Finding relation between images
- Voting on step 2 based on Appearance corresponding

- Rejecting wrong corresponding points
- Based on geometric relations between points

Estimating Geometrical Relation

- Each interest point has
- Dominant Direction
- Scale

- Use this information for voting

Estimation Histogram

Direction

Direction

Scale

Scale

O(nm)

O(n)

Maximum

Maximum

Using 3NN method

Using threshold method

Comparing Knn and Ratio Threshold settingBased on Number of true corresponding points

Comparing Knn and Ratio Threshold settingBased on Number of true corresponding points

Comparing Knn and Ratio Threshold setting(In Tolerating False Corresponding)

Comparing Knn and Ratio Threshold setting(In Tolerating False Corresponding)

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Stitching

- Pixel Weights based on their distance

Other examples

- Adobe Photoshop could not stitch these images

- Introduction
- Definition
- Aligning Concepts
- Stitching Concepts

- Object Recognition Problem
- Salient Feature
- What is Descriptor
- Matching

- Proposed Method for Aligning
- Using Harris Detector
- Using SIFT Detector
- Stitching

- Concluding and Suggestions

Concluding

- Ideas
- Using Harris and SIFT as a detector
- Using Similarity transform as a general image transform
- Using voting based on direction and scale of each interest points
- Introducing Evaluation Method

- Results
- Harris is more reliable but SIFT is much better in presence of scale transform
- Similarity can handle prospective transform for human eyes
- Voting can obtain similarity transform with much lower time order
- Threshold ratio has a better behavior than Knn

Questions?

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