Transcript 下載/瀏覽Download

Image processing based recognition of
images with a limited number
of pixels using simulated prosthetic vision
Information Sciences, vol 180, no 16,
2010, pp 2915-2924.
南台科技大學
資訊工程系
指導教授：李育強
報告者 ：楊智雁
2010/11/16
Outline
2
1
Introduction
2
Materials and methods
3
Experimental Results
4
Conclusion
1. Introduction
 For many diseases that have no effective treatment
 Visual prostheses offer another feasible method to
restore partial functional vision for blind
individuals
 It is important to confirm the minimum
requirements for visual prostheses through
different experiments
3
1. Introduction (c.)
 Two image processing methods
 Threshold-binarization’ images
 Edge images
 Two kinds of pixel shapes
4
1. Introduction (c.)
 Square and circular pixel
 Different sized image arrays
 (8x8, 16x16, 24x24, 32x32, 48x48 and 64x64)
5
2. Materials and methods
 Object images, and all of them have 288 x 288
pixels.
6
2. Materials and methods (c.)
 Image processing strategy
 Pixel shapes and pixel numbers on recognition in
this study
 Direct binarization and edge extraction
7
2. Materials and methods (c.)
 Pixel number reduction
 N x N pixels of the original image were merged
into one single pixel
 Uniform luminance values corresponding to the
mean grayscale value of the original matrix
8
2. Materials and methods (c.)
 Adaptive threshold-binarization
 An appropriate threshold to binarize images can
make an object and background separate
completely in non-complicated images
 The resulting image is able to reflect the main
features of the objects
9
2. Materials and methods (c.)
t
1   ipi / P1
i 0
2 
L 1
 ip / P
i
i t 1
D  1  2
10
2
2. Materials and methods (c.)
 Cohesion of S1 and S2 is a significant sign for the
effectiveness of the classification
t
pi
d1   i  1 
P
i 0
L 1
pi
d 2   i  2 
P2
i t 1
 A smaller dispersion degree indicates better
cohesion and effectiveness of classification
11
2. Materials and methods (c.)
P1  P2  D
H (t ) 
P1  d1  P2  d 2
 Edge extraction
 The Sobel operator performs a 2-D spatial
gradient measurement on an image to emphasize
regions of high spatial frequency that correspond
to edges
12
2. Materials and methods (c.)
 Display of pixelated Image
13
3.Experimental Results
14
4. Conclusion
 This study focused on the effects of the image mode
and pixel shape on recognition
 To simulate actual daily life, the images chosen
should be commonly used or seen
 The array with 32 x 32 binary pixels were
sufficient for the recognition of most objects
without prior information
15
4. Conclusion (c.)
 From 16 x 16 to 24 x 24 the mean recognition
accuracy increased threefold
 For simple scenes it was between 32 x 32
and 48 x 48
 Some relatively ‘simple’ objects such as a comb or
banana could not be correctly recognized
16
4. Conclusion (c.)
17
南台科技大學
資訊工程系