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Eyes detection in compressed domain using classification. Technical University of Cluj-Napoca Faculty of Electronics, Telecommunications and Information Technology. Eng. Alexandru POPA alexandru_popa@autenticmedia.com. Contents:. Object detection in digital images - PowerPoint PPT Presentation
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Eyes detection in compressed domain using Eyes detection in compressed domain using classificationclassification
Eng. Alexandru POPA
alexandru_popa@autenticmedia.com
Technical University of Cluj-Napoca
Faculty of Electronics, Telecommunications and Information Technology
Object detection in digital imagesThe principle of image processing in the compressed domainThe Discrete Cosine Transform (DCT)The spatial relationship of DCT coefficients between a block and its sub-blocks Object recognition using classificationThe linear discriminant classifier (LDA, Fisher classifier)DemoResultsConclusions
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the approached method consists in feature extraction using image transformations, creation of a new space of features followed by objects classification in that space
feature extraction methods: DCT, Wavelet, Gabor
DCT gives in general good features for object description. Is the base of the JPEG standard, and the properties of the DCT coefficients blocks, makes them very good for generating features spaces
the idea is to make the classification of the objects direct in JPEG compressed domain
DCT = Discrete Cosine Transform
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almost all image processing algorithms are defined in pixel level; rewriting them in the compressed domain is not directstandard implementation schemes decompress the image, apply the algorithm and them recompress the image. The disadvantage is that these schemes are time consumingit is wished to rewrite these algorithms directly in the compressed domain for optimizing the processing chain
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E n tr o p yd ec o d e
D ec o m p r es s io n( R L E , Z ig - zag ,
Q u an tizer , D C T )
P ix e l lev e lp r o c es s in g
C o m p r es s ed d o m ainp r o c es s in g
C o m p r es s io n( R L E , Z ig - zag ,
Q u an tizer , D C T )R L E
v ec to r
E n tr o p yen c o d e
J P E Gb its tr eam
R L Ev ec to r
J P E Gb its tr eam
The formula for DCT applied on a image:
Properties:
Decorelation – the principal advantage of transformed images is the low redundancy between neighbours pixels. From this fact results uncorrelated coefficients which can be coded independently Energy compactness – the capacity of the transformation to pack the input datas in as few coefficients as possible Separability – the 2D DCT can be calculated in two steps by applying the 1D formula successively on the lines and the columns of an image
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(1)
(2)
a new problem could occur from the fact that various DCT block sizes have to be used in order to ensure optimized performances8x8 blocks used in JPEG, 4x4 blocks used in image indexing, and 16x16 macro-blocks in MPEGto deal with inter-transfer of DCT coefficients from different blocks with various sizes, the existing approach would have to decompress the pixel data in the spatial domain via the IDCT, redivide the pixels into new blocks with the required size and then apply the DCT again to produce the DCT coefficientsit is obvious that the approach is inefficient
Bibliography: The Spatial Relationship of DCT Coefficients Between a Block and Its Sub-blocks, Jianmin Jiang and Guocan Feng
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4x4 block
Transformation from 4 blocks of 2x2 pixels in one of 4x4 pixels:
106 97 83 85
106 95 84 85
105 84 74 69
77 60 57 89
The block with the pixelsluminance
DCT202 10 168 -1
1 -1 84 85
163 19 144 -13
26 2 -1 19
The DCT coefficients of4 block of 2x2 pixels
Matricea A*
339 23 22 -3
34 13 -13 0
-12 -16 8 -5
-1 13 -4 4
1 0 1 0
0.9239 0.3827 -0.9239 0.3827
0 1 0 -1
-0.3827 0.9239 0.3827 0.9239
Ecuation:
Original image
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(3)
Transformation form a 4x4 block to 4 block of 2x2 pixels:
106 97 83 85
106 95 84 85
105 84 74 69
77 60 57 89
DCT
202 10 168 -1
1 -1 84 85
163 19 144 -13
26 2 -1 19
The inverse matrix of A*
339 23 22 -3
34 13 -13 0
-12 -16 8 -5
-1 13 -4 4
0.5 0.4619 0.5 -0.1913
0 0.1913 0 0.4619
0.5 -0.4619 0.5 0.1913
0 0.1913 0 0.4619
Ecuation :
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(4)
The block with the pixelsluminance
The DCT coefficients ofThe 4x4 block
Original image 4x4 block
geometric classifiers are those classifiers which implies the deduction of some decision borders in the features spacea classifier demands a set of training datas (datas + labels)the number of datas must be big enough for a correct learning with generalization capacity for unknown datas
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Data classification:
means that an unknown sample is presented to the classifier, his position regarding the decision boundaries is calculated and depending on it a label is associated
LDA (Linear Discriminant Analysis) using Fisher’s classifier implies finding a line in the features space and projecting the datas from the training set on this line. Describes the datas by their projectionsConsidering a bi-dimensional space we have:
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Fisher’s criteria for selecting w and w0 parameters:
The optimal direction w is the line direction for which: 1) the distance between the projections of the classes centers on w is maximum2) the variance of the projections from each class is minimumThe optimum value w0 is the scalar value which minimize the classification error in the training data set
is the label assigned to the i data by the Fisher classifier
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The image form which the training set was taken
it was proved that the implementation of Fisher`s classifier in compressed domain was a wise choice because it has good results in eyes regions detectionit`s a novelty in the image processing field because this algorithm wasn`t written in compressed domainusing the spatial relationship of DCT coefficients between a block and its sub-blocks facilitates the computation of coefficients for big blocks starting from small blocks in the way of speed and computation complexity
Others applications that can derive:
gaze tracking/focusing
automatic system for detecting the vigilance of driversbiometrics applications: person identification using iris recognition
, contează foarte mult structura acesteia precum şi setul de antrenare
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Thank you for your Thank you for your attention!attention!Questions?Questions?
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