Aug 20, 2006 - Feature Extraction from Noisy Image. Using PCNN*. Yide Mat, Zhaobin Wang and Chenghu Wu. School ofInformation Science andEngineering ...
Proceedings of the 2006 IEEE International Conference on Information Acquisition August 20 - 23, 2006, Weihai, Shandong, China
Feature Extraction from Noisy Image Using PCNN* Yide Mat, Zhaobin Wang and Chenghu Wu School ofInformation Science and Engineering, Lanzhou University, Lanzhou City, Gansu Province, China t ydmaglzu.edu.cn
noises, so the method of recognition will not work very well. Furthermore, most of images are polluted with various kinds of noises, but we are not easily aware which type of the noise is in the image. Artificial participation will be introduced in order to realize perfect result. however, it is not permitted in ATR and also needs more time in the processing. Applications of pulse-coupled neural network (PCNN), derived from the Echorn's neuron model [1, 2], were not realized until 1990's. The Echorn's neuron model is developed based on the observation of the visual cortex nerve cell of cats and simulating the activities of the visual nerve cell. Because the algorithm of PCNN model [1-6] derives directly from the studies of visual properties of the mammal, it is very suitable for image processing [7A16], such as image segmentation, noise reduction and image smoothness. PCNN is different from the traditional multi-layers neural networks
Abstract - The entropy sequence of the output image, gotten from the original gray image by Pulse-Coupled Neural Network (PCNN), as feature vector of the gray image, can be used as a unique feature expression of gray image, which has been proved by our experiment. therefore, in this paper, it is used in the image classification, and the mean square error (MSE) between the feature vector of the input image and standard feature vector is used to judge the input image belong to which kind of image groups. At the same time, the results of our experiment show that this method is strongly flexible to resist noises and greatly robust to recognize image, if the tested images in our experiment are disturbed with Gaussian noise, impulse noise or both of this. Index Terms - Feature Extraction, pattern recognition, entropy, Pulse-Coupled Neural Network (PCNN)
I.
INTRODUCTION
Many kinds of methods are introduced to solve image recognition; however, there are too many troubles to recognize directly because the original image is usually interfered with noises, which is one of hard problems in automatic target recognition (ATR). It is easy to recognize directly for simple images, such as digit and simple geometry, but it is very difficult for complex or nature images disturbed by noises. In most cases, such kind of situation can be solved in the following steps: first, the noise type is analyzed and many different kinds of method are used to remove a variety of noise; second, the image is segmented into different regions; then, the enough features will be extracted from different region; at the last, the image is recognized according to its feature vectors. But this kind of method has many
in that it's a single layer model, which is suited for real-time image processing. Until now, it is very difficult to determine the exact relationship between the parameters of PCNN model. The research on this theory and its applications has been extensively developed in recent years, but we find that the number of the paper about Feature Extraction with PCNN [17, 18] is not large. A method of feature extraction based on PCNN is provided in this paper for solving noisy image recognition, which has very strong capability to resist noise in both simply images and complex images. In addition, we use parallel arithmetic to process the whole image to extract feature, so the recognition speed is faster, and it is also very suitable to real time image processing.
disadvantages, for instance, if the original image is seriously noised, the result of image recognition will make mistakes.
II.
The PCNN is a very simplified network of a single layer with local lateral connections between neurons. The PCNN neuron (Eckhorn's neuron) is composed of multiple nodes
This is to say, the different contaminated images may be considered as the same one by mistakes because of mass * This work is supported by National Natural Science Fund
and 985 Special Study Project (LZ985-23 1-582627).
(NO.60572011)
formed a 2-D net. A full mathematical description of PCNN
iS given by Ranganath H S, Kuntimad G and Johnson J L [7].
tCorrespondence author.
1 -4244-0529-7/06/$20.OO
©C2006 IEEE
DESCRIPTION OF PCNN MODEL
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Then the binary image constructed by Y[n], the output of PCNN, is the segmented image.
The algorithm is performed by continuous iteration of the input and output, and most important equations are: F1, [n] = exp(-aF)Fi [n -1] + VFZMJklYklLn -1] + I¾ (1) YklLn -1] Li [n] = exp(-aL)Lj [n-1I -VLZWijkl +
(2)
Uij[n] = FiJ[n](1 +I3LiJ[n])
(3
Fij [n]>I T1#Lij[n]) rlU..n] =
Y[]
=
,U. [n]< T. [n]
ThLnJ =exp(-aT)T~[n -1] +
V7~YJ,[n-lI
III.
(3) (4)
DESCRIPTION OF THE ALGORITHM pulsing
images with PCNN. The aforementioned binary 2-D images may be used to create a 1 -D time signal including some
(5)
In these equations, Ii is the input stimulus such as the interesting features with respect to invariance and uniqueness. The time signal vector can be used to realize classification or leelfiagepixlsini psiton, F,1[n].. normalized graynormalized level of image pixels in (i, j) position, is the feedback input of the i th and j th neuron, and Lij[n] is recognition. Lindblad [17] used the sum of pulsed neurons of the linking item. ,6 is the linking coefficient. Uij[n] is the output binary image as 1 -D time signal in order to convert 2-D input to 1-D output. Through lots of experiments we find internal activity of neuron, and Tij[n] is the dynamic threshold. that most of images have exclusive time signal, which iS Yij[n] stands for the pulse output of neuron and it gets either invariant to larger changes in rotation, scale, shift, or skew of the binary value 0 or 1. M and W are the const synaptic the input image. The signal can be used as input to the feed wespectightymatrics fependingonan thethe distance inkingebetween inputn forward pattern recognition nets compatibly. In addition, respectively, which rhe dependant although it is better for the result with the method of Carlson neurons, normally W = M. aF, aL, and aT are the attenuation discrete contour transform (CDCT) to convert 2-D input to time constants of Fi1[n], Lij[n] and T,j[n], respectively. VF, VL I-D output [10], the method does not resist noise. That is to and VT are the inherent voltage potential of Fi1[n], Lij[n] and say, it is sensitive to noise. Thus, it is difficult to use their method for noisy image in automatic target recognition (ATR). A -I] The entropy of an image, reflected the quantity of the 2 l I Delaw', f; I t,> 0, I eae image information, is a main factor of the statistical character. { T __ f Del Through a series of experiments, this article proposes a new Fj[,,] algorithm that converts 2-D input to 1-D output. We use the ti[1/ ['1 entropy of the set of output binary 2-D images as the time I11j)11t8 111km (elieritr ide Geiierator ymputs' iiAilU* signal: G(n) = Hn(p), G(n) represents the time signal, and Hn(p) represents the entropy of the output image. The entropy Fig. 1. A model of PCNN contained in the binary image Y[n] (composed of mxn As is shown in Fig. 1, PCNN neuron accepts the Hn(p) feedback input Fij[n] and the linking input Lij[n], and then pixels), which is the segmentation output of PCNN, can be c calculated during the cyclic iteration operation: Ping Pi cyclic Po generates the internal activity Uij[n]. When Uij[n] is greater (6) Hn (P) = -PI 1092 PI - PO 1092 PO than the dynamic threshold Tij[n], PCNN produces pulse .. Pi andPo represent the probability when Y,j[n] = 1 and Y,j[n] sequence Y,1[n]. sequence Yin]. . .= 0 in the output Y[n] separately. When different images are A two-dimensional image (mxn) can be thought as a PCNAneuromimenwithal imxen ons, cand te thougra ll of inputted in PCNN to iterate some times, PCNN will generate PCNN neuromime with mxn neurons, and the gray level of tedfeetplesqec h orsodn the different pulse sequence Y [n] and the corresponding pixels can be thought as I,j, the input of the neuron. Obviously, enrpisaodfeet,sthoigalmgs'hpsad n
W toethr wth istheintriorliningmatix.Onepixl's
n
distributing of gray scales are not similar. So the new time
pulsating output can activate other corresponding pixels having the approximate gray level in the neighborhood and let them generate pulsating output sequence Y[n], while there are pixels with approximate gray levels in the neighborhood of W and ObvouslY[n conainsimpotantinfomatin ofthis image such as regional information, edge, and texture features.
sgaso ifrn mgsaedfeet hog oso experiments we find: any time signals of each different image are unique; and for the same kind of image with more or less noise, the time signals are greatly similar. In other words, this method iS very robust to resist noise.
809
image for example, we get the same effect from different disturbed images (as showed in Fig 2 and Fig 3). It means that our method is strongly flexible to resist noises. Second, the seven different complex images are taken as pattern warehouse; we can obtain the conclusion that the method is greatly robust to recognize the complex images (as showed in Fig 4). And then, how to classify the image is explained.
With the new time signal, we propose a simple method to recognize noisy image in this paper. Firstly, the original images are sent into PCNN separately, and then the sets of entropies are computed. Secondly, MSE between the two time signals is computed and noisy images are recognized directly according to the MSE. In addition, the MSE can be calculated during operation: MSE =
N
n=O
[GI' (n) - G (n)
Finally, our experiment environment is introduced.
(7)
1) The Lena image without noise is compared with different noisy images. the result shows the entropy sequence of the image is invariant to Gaussian and impulse noise with a wide range. We compute MSE, shown in table 2, of the entropy sequences of noisy image and the original images which are shown in Fig.2, 3. During our experiments, we find: when Gaussian noise is shorter than 20%, MSE less than 0.004, it can recognize directly; when Gaussian noise is more
Where G(n) represent the standard time signal. If the image is contaminated extremely by noises, it is necessary to need median filter for avoiding affection of noise in PCNN pulsing mechanism before the image is inputted into PCNN. It is important to set the proper threshold according to experience data, before the image is recognized by MSE,
IV RESULTS OF SIMULATION
than
20%, MSE is more, it may recognize wrongly; when
In this section, our scheme of the algorithm is stated in impulse noise is shorter than 400, MSE less than 0.004, it can recognize directly; when impulse noise is more than 40%, detail. First of all parameters of PCNN are introduced, which MSE is more, it may recognize wrongly. are extremely important for our experiment. And then, the 2) Lena images with different noise are compared with results and analysis are given in the subsequence. In addition, other images, and then we also compute MSE, shown in table seven images (256x256) are taken as pattern warehouse to reconizeso ato how ur etho robst vry wll.3, of the entropy sequences of noisy image and the original image shown in Fig.4. Obviously, MSE of different modes are very larger; while MSE of the same mode are smaller. A. The Parameters ofPCNN With this trait the image can be recognized very well.
3) Though mass experiments, we get that the threshold should be set to 0.04. When MSE is less than 0.04, the input image belongs to the same mode. If the noisy images (Gaussian noise variance less than 0.03, or impulse noise less than 40%) belong in image pattern warehouse, we will get l00% classification. If images do not belong in the image pattern warehouse, we will get 90% or better classification. Even adding two noise simultaneity, our method can get better than others, too. When noise is extreme, we can add post-processing (denoising or other pattern recognition nets)
By way of pledging uniqueness of the new time signals and repeatability of the experiment, the processing to all images must be in the same parameters of PCNN. In the paper we use the parameters of PCNN in table 1, LA U and Y matrices are initially set to zero, the gray level of pixels can be thought as Ij. M and Ware local Gaussians (dependant on the distance between the neurons) and normally W = M= [0.5, 1, 0.5; 1, 0, 1; 0.5, 1, 0.5]. TABLE I.BAsIc PARAMETERS OF PCNN g Parameters aL aT aF VF VL VT Value
B.
1.0
1.0
0.1
0.5
0.2
20
to recognize if we want to get better effect. 4) Experiments are tested in PC (766MHz, 64M RAM) and it needs 40 minutes to recognize an image. Our arithmetic is parallel algorithm, if parallel computer or FPGA is used the
0.1
The Results of Experimenuts
In this part in order to explain our results, we will provide lots of data in the experiments. First, taking Lena
time of processing is less than in PC. So it is suited to real time image processing.
810
(a)
(b)
(c)
(f) (d) (e) Fig.2. Comparison of entropy sequence of Lena original image and Gaussian noisy images. (a) Lena original image. (b) Gaussian noisy image (G2=0. 1). (c) Gaussian noisy image
(a)
(b)
(b) Child2
(c) Child3
(d) Child4
(e) lenal
(f) cameraman
(c)
(e)
(1)
0
01
(d)
(a) Childl
~
~
~ ~~~~~
(f)
(2)
~
(3)
~~1 ~~~~ ~104~10
(4)
(5)
(6)
Fig.4. Comparison of entropy sequence of noisy images and other mode images,(a)> (b) (c)> (d) (e), (f)represent the (4) (5) (6) entropy sequence of(1)> (2) (3)
Fig.3. Comparison of entropy sequence of Lena images with impulse noisy. (a) 5% impulse noisy image. (b) 10% impulse noisy image. (c) 30% impulse noisy image. (d)> (e)> (f) represent the entropy sequence of (a), (b), (c)
,
,
TABLE II. MSE OF ENTROPY SEQUENCE OF LENA NOISY IMAGES AND OTHER MODE IMAGES
Gaussian noise(&2)
Noise class Noise degree
0.0025
0.0064
0.0077
0.0324
0.04
0.09
MSE
0.0057
0.0089
0.0077
0.0278
0.0361
0.0428
Noise class
Impulse noise
Noise degree
5%o
10%
15%0
30%
40%o
MSE
0.0049
0.0072
0.0041
0.0051
0.0049
Gaussian noise -+ Impulse noise
Noise class Noise degree
MSE
1
(32001 &500o
(32001 &10|o
|0.0159
10.0123
811
(32001 &20%
|0.02821
1
TABLE III. MSE OF ENTROPY SEQUENCE OF LENA NOISY IMAGES AND OTHER MODE IMAGES
Gaussian noise (a2)
MSE
Lena original image
Noise degree
0
0.01
0.04
Lena
0
0.0077
0.0361
Childl
0.2518
0.2518
Child2
0.2852
Child3
Impulse noise 20%
30%
0.0072
0.0051
0.0099
0.2852
0.3078
0.3472
0.1474
0.2831
0.4496
0.3181
0.3206
0.3181
0.3078
0.3241
0.5313
0.3418
0.3478
0.3475
Cameraman
0.4955
0.5801
0.7643
0.4371
0.4449
0.4686
Lenal
0.0759
0.1006
0.1949
0.0717
0.0715
0.0906
Child4
0.1474
0.1403
0.2660
0.1849
0.1852
0.1775
10%
[3] Frien A, Eckhorn R, Bauer R, Woelbern T et al. Stimulus-specific fast oscillations at zero phase between visual areas V1 and V2 of awake monkey [J]. Neuro Rep.,
V CONCLUSION PCNN can extract some information of the image such as regional information, edge and texture features. In the paper, investigating the output entropy sequence with the same parameters of PCNN, we find that noises in the image make little effect to the entropy sequence. The mean square error (MSE) between the feature vector of the input image and standard feature vector is used to decide that the input image belongs to which kind of image groups, and we use the parameters that can make fast constringency. Hence, the experiment results have shown that the method is strongly flexible to resist noises and greatly robust to recognize image.
1994, 5(17), pp.2273-2277. [4] Stoecker.M, Eckhorn.R, Reitboeck.H.J. Size and position inaatvsulrpenaio spotseiooicm s vianselectiv backwardtaths: ppdnai second orde nua networkwmod a p functiona roeo reurretwconetion i theviu alcore [] Neurocomputing, 1997, 17(2), pp.1 11-132. [5] John L. Johnson, Mary Lou Padgett. PCNN Models and AppLictonso[] EEra on NuaNet works 199 10(3), pp.480-498.
[6] Lzhikevich, Eugene.M. Class 1 neural excitability,
ACKNOWLEDGMENT We should like to acknowledge that this work is supported by National Natural Science Fund (NO.60572011)
conventional synapses, weakly connected networks, and of mathematical foundations pulse-coupled models[J].IEEE Trans. on Neural Networks, 1999, 10(3),
and 985 Special Study Project (LZ985-231-582627). We would also like to thank Ying Zhu, a graduate student in the School of Life Science at Lanzhou University, for giving us many useful proposals for this paper.
pp. 499-507.
[7] Ranganath H S, Kuntimad G, and Johnson J L. Pulse coupled neural networks for image processing[A]. In: Proc. 1995 IEEE Southeast Con. [C],Raleigh NC,1995, pp.37-43. [8] G. Kuntimad, H. S. Ranganath. Perfect Image
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