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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    17 17

    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

    3

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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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    l

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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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    3333

    Thank you for your

    attention

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