Object Oriented Framework for CUDA based Image Processing

組員名單 :P76004588 徐華煊P76004423 曾郁凱P76014216 吳品頡

Keywords: Object oriented framework, CUDA,design patterns, image processing

Pritam Prakash Shete, Venkat P. P. K., Dinesh M. Sarode, Mohini Laghate, S. K. Bose & R. S. Mundada,

Bhabha Atomic Research Centre, Mumbai, India

International Conference on Communication, Information & Computing Technology (ICCICT) Oct. 19-20,2012, Mumbai, India

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1. Introduction• Compute Unified Device Architecture (CUDA)

– CUDA is a novel and promising GPU programming frame work from NVIDIA.– The CUDA has been speedup many computationally intensive graphics as well

as nongraphic• Essential for a seamless panoramic mosaic

– A pyramidal image blending algorithm• Our goal

– To show that use of design patterns facilitate extending existing functionality by adding new classes, rather than modifying an existing classes or functionality

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1. Introduction- Panoramic Mosaic

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1. Introduction- Finding Key Point

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2. Analysis and Design- Gaussian Blur Operation

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2

22

2)(

2exp

21),,(

yx

yxG

),(*)),,(),,(( )1( yxIkyxGkyxG nn

),,(),,( )1( nn kyxLkyxL

),(*),,(),,( yxIyxGyxL

doubles for the next octave

),,( )1( nkyxD

L(σ)

sn ,,2,1

D(σ)

High frequency

k = 2(1/s)

s: Image per octaves = 3 in this case σ=1.6

σ=k3*1.6

DoG filterGaussian filter

σ=k4*1.6

σ=k-1*1.6

σ=k*1.6

σ=k2*1.6

σ=1.6σ=2*1.6

2. Analysis and Design- Laplacian Pyramid

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2. Analysis and Design- Remove Edges Response

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3. Implementation - Modules

• Image Blending Library (IBL): They developed framework for CUDA based image processing.

• This frame work offers 3 modules for an image processing:1) CPU Module2) Simple-CUDA Module3) IO-CUDA Module

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3. Implementation - Modules

1) CPU Module– Using single thread

– Implementation function:1. Gaussian blur2. Laplacian pyramid3. REDUCE operation4. EXPAND operation

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3. Implementation - Modules

2) Simple-CUDA Module

CPU

GPU Global Memory

CPU Memory

Send image

ProcessImage

Return image

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3. Implementation - Modules

3) IO-CUDA Module

GPU Global Memory

Image already get

Shared Memory

ProcessImage

Send image

Return image

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3. Implementation - Automatic Image Conversion

• Visitor Design pattern– Image type: CPUBuffer image and CUDABuffer image

Visitor

Concrete Visitor

Concrete Visitor

Element

Concrete Element

Concrete Element

Output: CUDABuffer

Output: CPUBuffer

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3. Implementation - Image Source Integration

• General Hierarchy Pattern

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3. Implementation - Extensible Architecture

• Construction of Gaussian & Laplacian Pyramid

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3. Implementation - Extensible Architecture

• Building the Gaussian pyramid– Non modifiable code along with placeholders for extending it

• Using Factory Method pattern placeholders– Creating the image buffer– Gaussian blur– REDUCE operation

• Realized by the respective subclasses– Ex. CPUBlendingFactory– Ex. IOCUDABlendingFactory

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3. Implementation - Extensible Architecture

• Combine Pyramid Operation

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3. Implementation - Extensible Architecture

• Combine Pyramid Operation– Validating an input pyramids– Allocating memory for an output pyramid– Combining high pass and low pass images

• The Template Method pattern is used to define the skeleton• Combining high pass images and low pass image are

placeholder or extension points• Subclasses redefine combining high pass and low pass

images without changing basic algorithm structure– Ex. CPUCombinePyramidOperation– Ex. IOCUDACombinePyramidOperation

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4. Result• Specification

– Intel Core 2 Duo with E8400 3.00 GHz processor– 2GB RAM– NVIDIA’s Quadro FX 4600– Input images covers low resolution (128x128) as well as a

high resolution (2048x2048)

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4. Result

• Panoramic image stitching

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4. Result• CUDA based modules perform much better than the CPU

module • Simple-CUDA

– Device global memory• IO-CUDA module

– Fast on-chip shared memory

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5. Conclusion

• Implemented an object oriented framework for a GPU based image processing

• Using software engineering principles and design patterns

• Extending the framework for computation using the GPU memory

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THANK YOU