accelerated connected component labeling using cuda … · connected component labeling (ccl) •...

Accelerated Connected Component Labeling Using CUDA Framework Fanny Nina-Paravecino, David Kaeli

ICCVG 2014

Outline

• Introduction• Connected Component Labeling• NVIDIA’s Compute Unified Device Architecture• Accelerated Connected Component Labeling• Performance Results• Conclusions

Fanny Nina-Paravecino and David Kaeli, ICCVG, 15-17 Sep. 2014, Warsaw, Poland

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Introduction• Image analysis plays an important role in many applications • In the field of physical security, there are challenging tasks

such as luggage scanning at airports that require:• Near real-time response• Very high rate accuracy

• Connected component algorithm identifies neighboring segments possessing similar intensities• Potential for efficient segmentation• Provides high quality results


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Introduction Matrix of Image512 x512

~700 images…

~700 matrices

…

One Frame

Multiple Frames


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Introduction• Flow chart of Object Detection

DICOM Image

DICOM Image

Input Object Detection

Preprocessing Preprocessing

Image Segmentation

Image Segmentation

Features ExtractionFeatures

Extraction

Object DetectionObject Detection

Our current focus

Our current focus


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Connected Component Labeling (CCL)• There have been a number of attempts to improve

performance of CCL:• Bailey and Johnston, “Single Pass Connected Components

Analysis. Image and Vision Computing” (2007)• Zhao et al., “Stripe-based Connected Components

Labeling” (2010)• Klaiber et al., “A memory-efficient parallel single pass

architecture for connected component labeling of streamed images” (2012)

• GPU implementations• Stava and Benes, “Connected component labeling in CUDA”,

GPU Computing Gems, (2010)


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NVIDIA’s Compute Unified Device Architecture (CUDA)• Compute capability architecture:• Tesla: Compute capability 1.0, 1.1, 1.2, 1.3.• Fermi: Compute capability 2.0, 2.1.• Kepler: Compute capability 3.0, 3.5.


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NVIDIA’s Compute Unified Device Architecture (CUDA)• Dynamic Parallelism


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NVIDIA’s Compute Unified Device Architecture (CUDA)• Concurrent Kernel Execution: Hyper-Q

Issue Order

Stream 0

Stream 1

Fermi

Stream 0 Stream 1 Stream 0 Stream 1

Kepler GK110Kernel Execution

Time


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Accelerated Connected Component Labeling• Two phases:• Phase 0: Find Spans• Phase 1: Merge Spans

Phase 0 Phase 1

1 1

1 1

1

0 0 2 2

1 2 - -

0 0 - -

Spans matrixN x K

Image matrixN x M

Each pair = span

1 2

3 -

5 -

Label Index MatrixN x K/2

Input

Binary imageN x M

threads

0 0 2 2

1 2 - -

0 0 - -

Spans matrix

1 2

2 -

5 -

Label Index

UpdateLabel

Kernel

UpdateLabel

Kernel

Child

threads


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Accelerated Connected Component Labeling• Phase 0: Find Spans• Each span has two elements: (ystart, yend)

• A unique label is assigned immediately1 1

1 1

1

0 0 2 2

1 2 - -

0 0 - -

Spans matrix

1 2

3 -

5 -

Label Matrix

Binary imageN x M


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spanx {(ystart,yend) | I (x,ystart ) I (x,ystart1 ) ... I (x,yend )}

Accelerated Connected Component Labeling• Phase 1: Merge Spans

Merge Span parent kernel

0 0 2 2

1 2 - -

0 0 - -

Spans matrix

1 2

3 -

5 -

Label Matrix

Merge Span parent kernel

0 0 2 2

0 0 2 2

0 1 - -

Spans matrix

1 2

1 2

1 -

Label Matrix

Concurrent Kernels

Multiples images at a time

Update LabelChild KernelUpdate LabelChild Kernel

Merge?

Merge?

Yes

NoNext span

One single update

Multiples updates at the same time

Update LabelChild KernelUpdate LabelChild Kernel

Merge?

Merge?

Yes

NoNext span

1 1

1 1

1 -

1 2

2 -

5 -

Label Matrix

Label Matrix


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Performance Results• Input Image:• DICOM format• Integer values [0 – 255]• More than 700 images (512 x 512 pixels)


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Performance Results• Pre-processing steps:• Background noise removal• Binary Conversion

Original Image Binary Image


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Performance Results• Experimental Environment:• CPU• Intel Core i7-3779K processor• RAM: 8GB

• GPU• GK 110 (NVIDIA GTX Titan)• Compute Capability 3.5• CUDA 5.5

• gcc compiler 3.7• OpenMP 3.0


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Performance Results• One Image

Method Running Time (s) Speedup

CCL Serial 0.25 1.00x

CCL OpenMP 0.18 1.39x

ACCL 0.05 5.00x


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Performance Results• Multiple Images: Hyper-Q

# Streams CCL Serial (s) ACCL (s) Speedup

1 0.25 0.05 5.00x

2 1.08 0.10 10.80x

3 2.16 0.14 15.36x

4 4.18 0.19 21.44x

5 6.09 0.23 25.91x


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Performance Results• Stava, O., Benes, B., CCL in CUDA comparison analysis

Mpixels/s Speedup

O. Stava, B. BenesCCL in CUDA

1542 1.0x

ACCL 5242 3.3x


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Conclusions• Described Accelerated Connected Component Labeling

(ACCL) using the CUDA framework• Presented evaluation of new features of the NVIDIA

Kepler GPU such as: dynamic parallelism and Hyper-Q• Compared serial CCL, OpenMP CCL with ACCL• Our algorithm scales well as long as we increase the number

of streams

• Dynamic parallelism turns out to be a disadvantage when trying to use a larger number of child thread kernels


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