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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-

6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME

149

WAVELET BASED HISTOGRAM METHOD FOR CLASSIFICATION

OF TEXTURES

Jangala. Sasi Kiran 1, U Ravi Babu

2, Dr. V. Vijaya Kumar

3

1

(Research Scholar, University of Mysore, Mysore, Associate Professor & HOD-CSE,

VVIT, Hyderabad, A.P, India) 2

(Research Scholar, Aacharya Nagarjuna University Asst. Professor, GIET Rajahmundry,

A.P, India) 3

(Professor & Dean Computer Sciences, Anurag Group of Institutions, JNTUH, Hyderabad,

A.P, India)

ABSTRACT

To achieve high accuracy in classification the present paper proposes a new method

on texton pattern detection based on wavelets. Each texture analysis method depends upon

how the selected texture features characterizes image. Whenever a new texture feature is

derived it is tested whether it precisely classifies the textures. Here not only the texture

features are important but also the way in which they are applied is also important and

significant for a crucial, precise and accurate texture classification and analysis. That is the

reason the present paper applied the derived a new method called Wavelet based Histogram

on Texton Patterns (WHTP). So far no exhaustive work was carried out in the wavelet

domain for classification of textures, based on histogram of texton pattern extraction. This is

the principal motivation for the work done in this paper. The proposed WHTP method is

tested on stone textures for precise classification.The proposed texton pattern detection

evaluates the relationship between the values of neighboring pixels in the wavelet domain.

The experimental results on various stone textures indicate the efficacy of the proposed

method when compared to other methods.

Key words: Texton, Pattern detection, neighboring pixels, feature extraction, stone textures,

multi resolution

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING

& TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 3, May-June (2013), pp. 149-164 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com

IJCET

© I A E M E



150

I. INTRODUCTION

Texture analysis plays an important role in many image processing tasks, ranging

from remote sensing to medical image processing, computer vision applications, and natural

scenes. A number of texture analysis methods have been proposed in the past decades [1, 2,

3, 4, 5, 6, 7] but most of them use gray scale images, which represent the amount of visible

light at the pixel’s position, while ignoring the color information. The performance of such

methods can be improved by adding the color information because, besides texture, color is

the most important property, especially when dealing with real world images [8]. In contrast

to intensity, coded as scalar gray values, color is a vectorial feature assigned to each pixel in a

color image. Although the use of color for texture image analysis is shown to be

advantageous, the integration of color and image is still exceptional.

The wavelet methods [3, 4, 8, 9] offer computational advantages over other methods

for texture classification and segmentation. Study of patterns on textures is recognized as an

important step in characterization and classification of texture. Various approaches are

existing to investigate the textural and spatial structural characteristics of image data,

including measures of texture [10], Fourier analysis [11, 12], fractal dimension [13],

variograms [14, 15, 16, 17] and local variance measures [18]. Fourier analysis is found as the

most useful when dealing with regular patterns within image data. It has been used to filter

out speckle in radar data [19] and to remove the effects of regular agricultural patterns in

image data [19]. Study of regular patterns based on fundamentals of local variance was

carried out recently [20, 21]. Hence, the study of patterns still plays a significant area of

research in classification, recognition and characterization of textures [22].

A wavelet transform-based texture classification algorithm has several important

characteristics: (1) The wavelet transform is able to decorrelate the data and achieve the same

goal as the linear transformation [23]. (2) The wavelet transform provides orientation

sensitive information which is essential in texture analysis. (3) The computational complexity

is significantly reduced by considering the wavelet decomposition. This is the reason the

proposed WHTP employed wavelet transforms.

In [24] proposed a complex texton, complex response 8 (CR8) are used and an 8-

dimensional feature is extracted. After that, similar to MR8 [25], a complex texton library is

built from a training set by k-means clustering algorithm and then an texton distribution is

computed for a given texture image. The main drawback of this is, it lacks spatial

information. Texture patterns can provide significant and abundance of texture and shape

information. One of the features proposed by Julesz [26, 27] called texton, represents the

various patterns of image which is useful in texture analysis. In the present paper, Textons are

detected on wavelet decomposed texture image for texture classification. The different

textons may form various image features.

The proposed WHTP method is an extension of our earlier method [28], with multi

resolution and robust features. The proposed WHTP method attempted to classify various

HSV-based color stone textures classification based on frequency occurrence of textons in

wavelet decomposed image, which is different from the earlier studies. In this work,

classification accuracy can refer to the percentage of correctly classified texture samples.

The rest of the paper is organized as follows. Section 2 describes wavelet based texton

feature evaluation method. Experimental results and comparison the results with other

methods are discussed in section 3 and conclusions are given in section 4.



151

2-level

DWT

Texton Frequency

Extraction

Feature

Library

II. COMPUTATION OF WAVELET BASED HISTOGRAMS ON TEXTON

PATTERNS (WHTP)

The proposed wavelet based texton feature evaluation method is represented in the

following Figure 1.

Figure 1: Block diagram of (WHTP) Wavelet based Histograms on

Texton Patterns

In this paper, the DWT is applied on a set of texture images and texton frequencies

are extracted from the approximation and detail subbands of DWT decomposed images, at

different scales. The various combinations of the texton frequencies are applied for texture

classification and a set of best feature vector are chosen. In order to improve the success rate

of classification, the texton frequencies are calculated for original image, approximation and

detail sub-bands of 1-level DWT decomposed images. It is found that the success rate is

improved much by combining the texton frequencies of original and decomposed images.

2.1 Discrete wavelet transform The word wavelet is due to Morlet and Grossmann in the early 1980s. They used the

French word ondelette, meaning “small wave.” Soon it was transferred to English by

translating “onde” into “wave,” giving “wavelet.”

Today wavelets play a significant role in Astronomy, Acoustics, Nuclear Engineering,

Subband Coding, Signal and Image Processing, Neurophysiology, Music, Magnetic

Resonance Imaging, Speech Discrimination, Optics, Turbulence, Earthquake Prediction,

Radar, Computer and Human Vision, Data Mining and Pure Mathematics Applications such

as Solving Partial Differential Equations etc.

The most commonly used transforms are the Discrete Cosine Transform (DCT), Discrete

Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Discrete Laguerre Transform

(DLT) and the Discrete Hadamard Transform (DHT). The DCT is favoured in the early

image and video processing. There are large numbers of image processing algorithms that use

DCT routines. DCT based image processing techniques are robust compared to spatial

domain techniques. The DCT algorithms are robust against simple image processing

operations like low pass filtering, brightness and contrast adjustment, blurring etc. However,

they are difficult to implement and are computationally more expensive. DCT is one of the

most popular and widely used compression methods. The quality of the reconstructed images

in DCT is degraded by the “false contouring” effect for specific images having gradually

Original Texture

Images



152

shaded areas. The false contouring occurs in DCT when smoothly graded area of an image is

distorted by an aberration due to heavy quantization of the transform coefficients. The effect

looks like a contour map. Due to this reason, the DCT based image processing

methods are weak against geometric attacks like rotation, scaling, cropping etc.

To overcome the above drawbacks, the present paper adopted DWT techniques to

achieve better performance. The Discrete wavelet transform (DWT) is a powerful tool of

signal and image processing that have been successfully used in many scientific fields such as

signal processing, image compression, image segmentation, computer graphics, and pattern

recognition .

The DWT based algorithms, has been emerged as another efficient tool for image

processing, mainly due to its ability to display image at different resolutions and to achieve

higher compression ratio. In DWT, signal energy concentrates to specific wavelet

coefficients. This characteristic feature is useful for multi-resolution analysis. DWT provides

sufficient information both for analysis and synthesis of the original signal, with a significant

reduction in the computation time.

Haar wavelet is one of the oldest and simplest wavelet. Therefore, any discussion of wavelets

starts with the Haar wavelet. The Haar, Daubechies, Symlets and Coiflets are compactly

supported orthogonal wavelets. These wavelets along with Meyer wavelets are capable of

perfect reconstruction. The Meyer, Morlet and Mexican Hat wavelets are symmetric in shape.

The wavelets are chosen based on their shape and their ability to analyze the signal in a

particular application.

2.1.1 Salient features of Haar wavelet transform

The Haar wavelet is the first known wavelet. The Haar wavelet transform has a

number of advantages:

1. It is conceptually simple.

2. It is fast.

3. It is memory efficient, since it can be calculated in place without a temporary array.

4. It is exactly reversible without the edge effects that are a problem with other wavelet

transforms.

The image is actually decomposed i.e., divided into four sub-bands and sub-

sampled by applying DWT as shown in Figure 2(a). These subbands are labeled LH1, HL1

and HH1 represent the finest scale wavelet coefficients i.e., detail images while the sub-band

LL1corresponds to coarse level coefficients i.e., approximation image. To obtain the next

coarse level of wavelet coefficients, the sub-band LL1 alone is further decomposed and

critically sampled. This results in two-level wavelet decomposition as shown in Figure 2(b).

Similarly, to obtain further decomposition, LL2 will be used. This process continues until

some final scale is reached. The values in approximation and detail images (sub-band images)

are the essential features, which are shown here as useful for texture analysis and

discrimination. In this paper/thesis Haar wavelet, Daubechies wavelets, and Symlet wavelet

are used for decomposition.



153

(2a) (2b)

Figure 2: DWT Decomposition:

2(a) First level of DWT 2(b) second level of DWT

2.2 Texton detection

Textons [26, 27] are considered as texture primitives, which are located with certain

placement rules. A close relationship can be obtained with image features such as shape,

pattern, local distribution orientation, spatial distribution, etc.., using textons. The textons are

defined as a set of blobs or emergent patterns sharing a common property all over the image

[26, 27]. The different textons may form various image features. To have a precise and

accurate texture classification, the present study strongly believes that one need to consider

all different textons. That is the reason the present study considered all. There are several

issues related with i) texton size ii) tonal difference between the size of neighbouring pixels

iii) texton categories iv) expansion of textons in one orientation v) elongated elements of

textons with jittered in orientation . By this some times a fine or coarse or an obvious shape

may results or a pre-attentive discrimination is reduced or texton gradients at the texture

boundaries may be increased. To address this, the present paper utilized six texton types on a

2×2 grid as shown in Figure 3(a). In Figure 3(a), the four pixels of a 2×2 grid are denoted as

V1, V2, V3 and V4. If two pixels are highlighted in gray color of same value in subband image

then the grid will form a texton. The six texton types denoted as TP1, TP2, TP3, TP4, TP5 and

TP6 are shown in Figure 3(b) to 3(g).

V1 V2 V3 V4

(a)

(b)

(c)

(d)

(e) (f) (g)

Figure 3: Six special types of Textons:

a) 2×2 grid b) TP1 c) TP2 d) TP3 e) TP4 f) TP5 and g) TP6

b)

The working mechanism of texton detection for the proposed method is illustrated in Figure

4. The present paper conducted experiments using Harr wavelet transform due to its

advantage as specified in the section 2.1.1. First, the original image is decomposed using

LL1 HL1

LH1 HH1

LL2 HL2

HL1

LH2 HH2

LH1 HH1



154

Haar, transform. On the approximation subband image, textons are identified. Then

the frequency occurrences of all six different textons as shown in Figure 4, with different

orientations are evaluated. To have a precise and accurate texture classification, the present

study considered sum of the frequencies of occurrences of all six different textons as shown

in Figure 3 on a 2×2 block.

TP2 TP1

0 3 0 0 2 2

3 1 0 0 0 3

TP3

0 0 3 4 0 0

0 0 3 2 0 0

TP4 TP4 TP3 5 0 4 3 1 0

1 5 2 4 1 5

(d)

(e)

Figure 4: Illustration of the texton pattern detection process:

(a) 2×2 grid (b) wavelet transformed image (c) & (d) Texton location and texton types (e)

Texton image

III. RESULTS AND DISCUSSIONS

Experiments are carried out on the proposed WHTP method to demonstrate the

effectiveness of the proposed method for stone texture classification. The proposed method

WHTP paper carried out the experiments on two Datasets. The Dataset-1 consists of various

brick, granite, and marble and mosaic stone textures with resolution of 256×256 collected

from Brodatz textures, Vistex, Mayang database and also from natural resources from digital

camera. Some of them in Dataset-1 are shown in the Figure. 5. The Dataset-2 consists of

various brick, granite, and marble and mosaic stone textures with resolution of 256×256

collected from Outtex, Paulbourke color textures database, and also from natural resources

from digital camera. Some of them in Dataset-2 are shown in the Figure. 6. Dataset-1 and

Dataset-2 contains 80 and 96 original color texture images respectively.

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976

6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May

Figure.5: Input texture group of 9 samples of Granite, Brick, Mosaic, and Marble in

Journal of Computer Engineering and Technology (IJCET), ISSN 0976

6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME

155

Input texture group of 9 samples of Granite, Brick, Mosaic, and Marble in

Dataset-1

Journal of Computer Engineering and Technology (IJCET), ISSN 0976-

June (2013), © IAEME

Input texture group of 9 samples of Granite, Brick, Mosaic, and Marble in



156

Figure 6: Input texture group of 12 samples of Mosaic, Granite, Brick, and Marble

with size of 256×256 in Dataset-2



157

The present paper used Harr wavelet transform due to its advantages as specified in

section 2.1.1.The frequency of occurrence (histogram) of Harr wavelet based texton patterns

of Granite Marble, Mosaic, and Brick texture images in Dataset1 are listed out in Table 1.

The sum of frequency of occurrence of the proposed WHTP method of each input texture

images in Dataset1 are listed out in Table 2.

Table 1: Frequency occurrence of proposed WHTP method for granite. mosaic, marble and

Brick texture in daraset1

S.

No

Granite

Texture Name

Six

textons

frequency mosaic Texture Name

Six

textons

frequency

marble

Texture Name

Six

textons

frequency

Brick

Texture

Name

Six

textons

frequency

1 blue_granite 698 concrete_bricks_170756 116 apollo 1790 Brick.0001 3070

2 blue_pearl 556 concrete_bricks_170757 43 canyon_blue 2230 Brick.0002 3599

3 blue_topaz 611 concrete_bricks_170776 121 cotto 1326 Brick.0003 3547

4 brick_erosion 641 crazy_paving_5091370 72 curry_stratos 1694 Brick.0004 4171

5 canyon_black 719 crazy_paving_5091376 72 flinders_blue 1716 Brick.0005 4046

6 dapple_green 741 crazy_tiles_130356 55 flinders_green 2629 Brick.0006 3351

7 ebony_oxide 586 crazy_tiles_5091369 68 forest_boa 1889 Brick.0007 3256

8 giallo_granite 459 dirty_floor_tiles_footprints_2564 52 forest_stone 1524 Brick.0008 3565

9 gosford_stone 492 dirty_tiles_200137 125 goldmarble1 2380 Brick.0009 3717

10 greenstone 830 floor_tiles_030849 66 green_granite 2589 Brick.0010 3326

11 interlude_haze 719 grubby_tiles_2565 293 grey_stone 1238 Brick.0011 3487

12 kalahari 889 kitchen_tiles_4270064 264 greymarble1 2564 Brick.0012 3894

13 mesa_twilight 554 moroccan_tiles_030826 118 greymarble3 2511 Brick.0013 3683

14 mesa_verte 690 moroccan_tiles_030857 80 marble001 1055 Brick.0014 4084

15 monza 636 mosaic_tiles_8071010 54 marble018 1373 Brick.0015 3285

16 pietro_nero 605 mosaic_tiles_leaf_pattern_201005060 82 marble034 2078 Brick.0016 4141

17 russet_granite 485 mosaic_tiles_roman_pattern_201005034 266 marble033 2419 Brick.0017 3870

18 granite10 690 motif_tiles_6110065 176 marble012 2512 Brick.0018 3464

19 granite13 779 ornate_tiles_030845 139 marble014 1726 Brick.0019 3381

20 granite20 817 repeating_tiles_130359 296 marble020 1452 Brick.0020 4083



158

Table 2: The sum of frequency occurrence of proposed WHTP method for 4 categories of

stone textures in dataset1

Granite mosiac marble brick

698 116 1790 3070

556 43 2230 3599

611 121 1326 3547

641 72 1694 4171

719 72 1716 4046

741 55 2629 3351

586 68 1889 3256

459 52 1524 3565

492 125 2380 3717

830 66 2589 3326

719 283 1238 3487

889 264 2564 3894

554 118 2511 3683

690 80 1055 4084

636 54 1373 3285

605 82 2078 4141

485 266 2419 3870

690 176 2512 3464

779 139 1726 3381

817 296 1452 4083

Figure.7: Classification graph of stone textures based on sum of the occurrences of proposed

WHTP method

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1 2 3 4 5 6 7 8 9 1011121314151617181920

Granite

mosiac

marble

brick



159

The Table 1, 2 and the classification graph of Fig.7, indicates that sum of frequency

occurrences wavelet based textons for granite, marble, mosaic and brick in dataset1 textures

are lying in-between 43 to 296, 459 to 889, 1055 to 2629, and 3070 to 4171 respectively. The

Table 1, Table 2 and the classification graph of Figure.7 indicates a precise and accurate

classification of the considered stone textures.

The frequency of occurrence of proposed WHTP method of granite and mosaic, brick

and marble texture images in dataset1 are listed out in Table 3. The sum of frequency of

occurrence of the proposed WHTP method of each input texture images in dataset2 are listed

out in Table 4.

Table 3: Frequency occurrence of proposed WHTP method for granite, marble, mosaic and

brick textures in daraset2

S

no

Granite

Texture

Name

Frequency

of WT Marble Texture Name

Frequency

of WT

Mosaic

Texture

Name

Frequency

of WT Brick Texture Name

Frequency

of WT

1 images_002 2705 blotched_marble_2052007 2159 images_024 627 alternating_brick_3121141 4335

2 images_006 2808 bricklike_marble_2052068 1919 images_027 750 alternating_brick_3121142 4817

3 images_009 2648 coarse_marble_9261512 1593 images_028 953 brick_1241070 3437

4 images_011 2327 dotted_marble_2052053 1416 images_044 865 brick_3141206 6443

5 images_020 2311 dotty_marble_92398723 1434 images_057 815 brick_3141207 3345

6 images_065 2727 faded_marble_9160023 1132 images_065 732 brick_4161585 8243

7 images_024 2303 fine_textured_marble_9181141 1278 images_080 848 brick_and_wood_wall_3141270 4767

8 images_030 2329 fossils_A220534 2220 images_101 811 brick_blotchy_litchen_2562 7463

9 images_032 2803 marble_cracks_circles_4168 1840 images_132 724 brick_closeup_5013216 6281

10 images_033 2690 marble_fossils_4167 2220 images_133 691 brick_detail_6080096 5593

11 images_038 2836 marble_texture_9181134 1934 images_144 210 brick_flooring_1010262 6299

12 images_040 2971 marble_texture_B231063 1541 images_153 201 brick_lichen_closeup_2561 3127

13 images_041 2757 marble_with_fossils_4165 2012 images_158 105 brick_P3012913 4245

14 images_047 2428 marble_with_fossils_4166 1215 images_178 590 brick_removed_plant_2560 6259

15 images_050 2373 marblelike_stone_9261514 1528 images_197 586 brick_square_pattern_9261479 4988

16 images_051 2303 patterned_stone_C050573 1434 images_239 943 brick_texture_221691 6443

17 images_052 2329 rose_coloured_marble_9181131 1132 images_240 433 brick_texture_4161572 3345

18 images_053 2574 rounded_markings_marble_2397234 1567 images_271 984 brick_texture_9181117 8243

19 images_058 2803 rounded_pattern_marble_2052013 1257 images_285 575 brick_wall_3141250 4767

20 images_062 2690 roundy_marble_297234 1130 images_287 691 brick_wall_3141267 3898

21 images_065 2836 shiny_reflective_marblelike_stone_9261513 1278 images_289 210 brick_wall_7070215 7463

22 images_067 2862 specked_marble_9261515 1643 images_290 201 brick_wall_7070225 5593

23 images_068 2950 specked_marble_C050546 2220 images_296 960 brick_wall_7070226 6299

24 images_071 2971 spotty_marble_4142267 1694 images_326 590 brick_wall_7070227 3946



160

Table 4: The sum of frequency occurrence of proposed WHTP method for 4 categories of

stone textures in dataset2

Granite mosiac marble brick

2705 627 2159 4335

2808 750 1919 4817

2648 953 1593 3437

2327 865 1416 6443

2311 815 1434 3345

2727 732 1132 8243

2303 848 1278 4767

2329 811 2220 7463

2803 724 1840 6281

2690 691 2220 5593

2836 210 1934 6299

2971 201 1541 3127

2757 105 2012 4245

2428 590 1215 6259

2373 586 1528 4988

2303 943 1434 6443

2329 433 1132 3345

2574 984 1567 8243

2803 575 1257 4767

2690 691 1130 3898

2836 210 1278 7463

2862 201 1643 5593

2950 960 2220 6299

2971 590 1694 3946

Figure.8: Classification graph of stone textures based on sum of the occurrences of proposed

WHTP method

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Granite

mosiac

marble

brick



161

828486889092949698

spectral, variance

and wavelet-based

features

Wavelet Transforms

Based on Gaussian

Markov Random

Field approach

Proposed WHTP

Method

The Table 3, 4 and the classification graph of Fig.9, indicates that sum of frequency

occurrences proposed WHTP method for granite, marble, mosaic and brick in dataset2

textures are laying in-between 2303 to 2971, 1130 to 2220, 105 to 984, and 3127 to 8243

respectively. The Table 3, Table 4 and the classification graph of Figure.8, indicates a precise

and accurate classification of the considered stone textures.

IV. COMPARISON WITH OTHER METHODS

The proposed WHTP method detections is compared with spectral, variance and

wavelet-based features [29] and GMRF model on linear wavelets [30] methods. The above

methods classified stone textures into three groups only. This indicates that the existing

methods [29, 30] failed in classifying all stone textures. Further the present paper evaluated

mean classification rate using k-nn classifier. The percentage of classification rates of the

proposed WHTP method and crashes methods [29, 30] are listed in table 5. The table 5

clearly indicates that the proposed WHTP method detection outperforms the other existing

methods and did not need any classification technique. Fig.9 shows the comparison chart of

the proposed wavelet based texton detection with the other existing methods of Table 5.

Table 5: mean % classification rate of the proposed and existing methods

Image Dataset

spectral,

variance and

wavelet-based

features

Wavelet Transforms

Based on Gaussian

Markov Random Field

approach

proposed WHTP

method

Brodatz 88.05 92.19 94.56

VisTex 89.23 92.56 93.15

Outtex 87.76 93.29 96.57

Mayang 90.07 92.86 95.06

Paulbourke 89.66 91.76 95.97

Figure. 9: comparison graph of proposed and existing systems



162

V. CONCLUSIONS

The present paper proposed WHTP method to classify the textures among the class of

textures. The present paper used Harr wavelet due to its advantages; However other wavelet

transforms are also yielding the same results. The graphs plotted based on occurrences of texton

patterns clearly classifies and recognizes Brick, Marble, Granite and Mosaic textures precisely. The

recent stone texture Classification methods failed in classifying all the stone textures precisely.

ACKNOWLEDGMENT

I would like to express my cordial thanks to CA. Basha Mohiuddin, Chairman Vidya Group

of Institutions, Chevella, R.R.Dt for providing moral support and encouragement towards research,

Anurag Group of Institutions, Hyderabad and MGNIRSA, Hyderabad for providing necessary

Infrastructure. Authors would like to thank the anonymous reviewers for their valuable comments.

And they would like to thank Dr.G.V.S.Ananta Lakshmi, Professor in Dept. of ECS, Anurag Group of

Institutions for her invaluable suggestions and constant encouragement that led to improvise the

presentation quality of this paper

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164

AUTHORS PROFILE

J. Sasi Kiran Graduated in B.Tech. (EIE) from JNTU University in

2002. He received Masters Degree in M.Tech. (C&C), from Bharath

University, Chennai, in 2005 and pursuing Ph.D from University of

Mysore, Mysore in Computer Science under the guidance of Dr V.

Vijaya Kumar. He served as Assistant Professor from 2005 to 2007 and

working as Associate Professor & HOD in CSE Dept., since 2008 at

Vidya Vikas Institute of Technology, Hyderabad. His research interests

include Network Security, Digital Watermarking, and Pattern

Recognition & Image Analysis. He has published research papers in

various National, International conferences, proceedings and Journals. He is a life member of

ISTE, ISC and management committee member of CSI. He has received significant

contribution award from CSI India.

U Ravi Babu obtained his MSc Information Systems (IS) from

AKRG PG College, Andhra University in the year 2003 and M.Tech

Degree from RVD University in the year 2005. He is a member of

SRRF-GIET, Rajahmundry. He is pursuing his Ph.D from AN

University-Guntur in Computer Science & Engineering under the

guidance of Dr V. Vijaya Kumar. He has published research papers in

various National, Inter National conferences, proceedings. He is

working as an Assistant Professor in GIET, Rajahmundry from July

2003 to till date. He is a life member of ISCA

Vakulabharanam Vijaya Kumar received integrated M.S.

Engg, degree from Tashkent Polytechnic Institute (USSR) in 1989. He

received his Ph.D. degree in Computer Science from Jawaharlal Nehru

Technological University (JNTU) in 1998. He has served the JNT

University for 13 years as Assistant Professor and Associate Professor

and taught courses for M.Tech students. He has been Dean for Dept of

CSE and IT at Godavari Institute of Engineering and Technology since

April, 2007. His research interests include Image Processing, Pattern

Recognition, Network Security, Steganography, Digital Watermarking, and Image retrieval.

He is a life member for CSI, ISTE, IE, IRS, ACS, ISC, NRSA and CS. He has published

more than 150 research publications in various National, Inter National conferences,

proceedings and Journals. He has received best researcher, best teacher award s from JNTUK

Kakinada and Gold plated silver award from Indian Red Cross Society.

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