Karin Iccsa2012 v1
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Transcript of Karin Iccsa2012 v1
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1
A Bio-Inspired System for
Boundary Detection in Color
Natural Scenes
Karin S. Komati (IFES/ Serra),
Evandro Salles (UFES),
Mario Sarcinelli-Filho (UFES)
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Input
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The only input of our method is the raw image.
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Output
3The output is a marked image, like the one presented in this slide.
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Edge detection result
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Examples
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In a natural
illumination
condition, a scene
includes bothdirect and indirect
illumination
distributed in a
complex way.
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Examples
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Different pattern
structures, in
different sizes androtations. A
repetitive pattern
but non-regular.
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Examples
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The border is not
very well-defined,
that is a ill-defined.
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Examples
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There is a huge
variety of textures.
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The Proposed Method
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Fully-Unsupervised
There isn't training phase;
We don't require input of number of regions; We don't require input of any characteristics
of each image;
Don't require any parameter-tuning forindividual images.
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Human Visual System
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P & M
Parvocelular
Retinas parvocellular ganglion
neurons show a low sensitivity to
contrast, high spatial resolution, and
low temporal resolution or sustained
responses to visual stimuli. These
cellular characteristics make the
parvocellular visual path-ways
especially suitable for the analysis ofdetails in the visual world, the
perception of color and maintenance
of color perception regardless of
lighting (color constancy).
Magnocelular
Retinas magnocellular ganglion
neurons show a high sensitivity to
contrast, low spatial resolution, andhigh temporal resolution or fast
transient responses to visual
stimuli. These characteristics make
the magnocellular branch of the
visual system especially suitable to
quickly detect novel or moving
stimuli.
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Human Visual System
ventral
dorsal
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Grossberg
Theory
The Two Streams Hypothesis
Complementary Computing
FACADE)
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Complementarypaths
FACADE
WHAT stream
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RegionGrowing
MM-Frac
Image
Enhance Superposed Pixels +Eliminate or Reduce False Boundaries
= KSS
Result
MultifractalDescriptor
Edge Detection
J-image
Controled by the shapeof power spectrum of
the image
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1) J-Image from original work
Deng and Manjunath (2001)
2) Multifractal Measurement We use the differential box-counting
method, proposed by Chaudhuri and
Sarkar (1995), to estimate the multifractalmeasurement (MM) of the original image.
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1/fSpectra of Natural Images
Torralba and Oliva (2003) observed the
energy spectra of real-world images falls, in
average, into a form 1/f
. They also show that the shape of the power
spectrum can be used to categorize the
different semantic of scenes.
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Alpha Estimation
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Here represents the slope of the
decreasing energy spectrum values,
from low to high spatial frequencies.
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MM-Frac
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map
ij
=J-valuenorm
+ (1-norm
)MM-value
where norm= /max(i), iindexing the 200 images used as
training set (provided by BSDS)
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RegionGrowing
MM-Frac
Image
Enhance Superposed Pixels +Eliminate or Reduce False Boundaries
= KSS
Result
MultifractalDescriptor
Edge Detection
J-image
Controled by the shapeof power spectrum of
the image
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Traditional Approaches
Region-Growing Methods
Result tend to be over-segmented
Inaccurate boundaries
Edge-Detection Methods
Noisy edges
Gaps
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KSS3
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Experimental Results
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Human Perception
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Human percpetion is subjective. Here we present segmentations from 6 different
human beings.
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The Berkeley SegmentationDataset and Benchmark (BSDS)
We tested all methods with natural coloredimages provided by BSDS test dataset.
100 images of the test dataset200 images of the training dataset
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BSDS metrics
Each image has at least 5 hand-labeledsegmentations made by human beings, which
constitute the ground truth.
Precision, Recall and F-measure metrics!
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Results
R l
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Results
R lt
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Results
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Metrics
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Conclusion
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One conclusion is that the Multfractal descriptor improvesthe sensitivity to boundary regions, thus providing
segmentation results that match the human perception
better than the segmentation results associated with the
original JSEG algorithm. The KSS algorithm works well and solves the problem of
false boundaries of region-growing approach and keeping
the details of edge detection approach.
The final results match the human perception better than
the individual methods.
Unfortunately, the KSS results present broken edges, not
keeping the contour closed.
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Thank you for your
attention
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