for Rapid Video Data Analysis

chemistoddAI and Robotics

Nov 6, 2013 (4 years and 3 days ago)

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Computer Science Department, Duke University

PhD Defense Talk

May 4, 2005

Fast Extraction of Feature Salience Maps

for Rapid Video Data Analysis

Nikos P. Pitsianis

and Xiaobai Sun




Fast Extraction of Feature Salience Maps for Rapid Video Data Analysis


FSMs are used in


separation or integration


automatic or assisted visual search tasks


target indication, object recognition, tracking


Salient information in multiple feature dimensions


color, edge orientation, shape, texture, motion


selective tuning, feedback,
attentional

or intentional guidance


High volume and rate of video data, frame by frame


Involves many filtering steps at multiple spatial scales

Feature Salience Maps (FSMs)

Sept 15, 2010

HPEC 2010

2

MUNDHENK, T. N., ITTI, L. Computational modeling and exploration of contour integration for visual saliency. Biological
Cybernetics (2005).

Fast Extraction of Feature Salience Maps for Rapid Video Data Analysis


Direct domain


Filter
-
centric


Image
-
centric



Fourier domain


Based on the convolution
theorem

Processing of feature maps on GPU

Sept 15, 2010

HPEC 2010

3


Using CUDA SDK 3.1


NVIDIA Tesla C1060


240 processing cores @
1.3GHz


4GB or GDDR3


CUFFT CUDA FFT library


Asynchronous I/O and
Streaming

5x5
10x10
15x15
20x20
0.05
0.1
0.15
0.2
0.25
0.3
NVIDIA Tesla C1060 @1.3GHz, CUDA v3.1
2D Convolutions of 10 512x512 frames and 16 filters/frame
Template Size
Execution Wallclock Time (sec)


Spatial domain
Fourier domain
5x5
10x10
15x15
20x20
20
40
60
80
100
120
140
NVIDIA Tesla C1060 @1.3GHz, CUDA v3.1
2D Convolution of 512x512 frames and 16 filters/frame
Template Size
Frames Per Sec


Spatial domain
Fourier domain
Fast Extraction of Feature Salience Maps for Rapid Video Data Analysis


The extraction and use of salient information from static or
dynamic images are recent and active research topics


The computation based on an extraction model serves two
purposes


Test and validate the underlying neurobiological model for certain
visual function in the visual system of the primate brain


Exploit the new understanding and model(s) for developing and
improving artificial vision systems.


Challenges :


generation of motion features, which are much more computation
intensive


visual tasks at the higher levels


segmentation, object recognition, tracking of moving targets.


data representation at higher levels, sparse and irregular, but still
structured


Efficiency of high
-
level processing steps
on GPUs

Discussion

Sept 15, 2010

4

HPEC 2010