Deformation and Improvement of Video Segmentation Based on

Deformation and Improvement of Video Segmentation Based on morphology Using SSD Technique

Ram Kumar Yadav Dr. Sanjeev Sharma Jitendra Singh Verma

SoIT,RGPV Bhopal SoIT,RGPV Bhopal SoIT,RGPV Bhopal [email protected] [email protected] [email protected]

Abstract The video segmentation is an important technique for improvement of quality of video on the basis of segmentation. Significance of this segmentation is analysis of video as deformation Video quality improvement and blurring of video. Various authors improve the quality of video using sliding window technique and using different noise filter but all these method are not accurate and suffering for object prediction in noise video. we have propose a new technique for the video quality improvement using contrast, median filter, color and shape based segmentation with using SSD technique all these technique simulate in simulation model.

1. Introduction The video segmentation is a technique of detecting changing frames in video and one of the important techniques required for efficient management of video data [1] several video segmentation algorithms have been proposed. They can be classified into types shape based video segmentation, edge information based video segmentation, image based video segmentation, texture based video segmentation and color based segmentation [2]. In this paper we use shape based segmentation and color based segmentation for any video. Shape based segmentation is based on morphology technique which is define shape of biological structure and other color base segmentation based on R,G and B channel which is defined Red, Green and Blue color of particular video data. Color segmentation on each frame to separate a frame into many homogeneous region. This paper describe the work for improvement of quality of video on the basis of designing simulation model and evaluating efficient algorithm related to segmentation of video files. Generally, video segmentation is a process to perform on multimedia data. Video refers to a set of technique which accepts

video as input. The result of the processing can be new video or data extracted from the input video. In which all the analysis and the designing of simulink model for particular type of segmentation of video work has been done already but we have to design combine (color segmentation and shape base segmentation) simulink model for improvement of quality of video on the basis of segmentation So, in this paper, we use a term ‘SSD’. Video is a continuous series of picture displayed sequentially at a fixed rate. Video is just a time sequence of images or all the pictures in a video files have equal size the pictures are called frames. Digital video information consists of a series of 25 frames per second. All video processing technique can be applied to frame. In this paper, we propose real-time and adaptive SSD technique. Proposed method determines result by changing the feature value of current frame and a mean feature value on variable sliding window. Proposed method can be used independently from the feature value of frame

The remainder of this paper is organized as follow. In Section 2, we describe about related work. The Proposed method is presented in Section 3. In Section 4, we discuss our Experimental result and analysis is described about Simulation on MATLAB 7.8.0. Finally, we give our concluding remarks in Section 5. 2. RELATED WORK In the process of video processing we study various papers. Here discuss some related work as by ZHAO Xin-bo proposed such a method as An Efficient Video Segmentation Algorithm [3].In this study, an efficient video segmentation algorithm is proposed. Digital video segmentation is an active area of research. Generally the importance of this segmentation mask can be defined as follows: Firstly the mask can be used for image examining, editing,

Ram Kumar Yadhav et al, Int. J. Comp. Tech. Appl., Vol 2 (5), 1322-1327

IJCTA | SEPT-OCT 2011 Available [email protected]

1322

ISSN:2229-6093

compression and analysis etc. Secondly the virtual reality system can use the panorama and mask to appear the virtual 3-D environment. So the research of such an efficient video segmentation algorithm can be divided into four steps, first motion estimator, panorama composing is second, third to remove the moving foreground object from the panorama and last determine the segmentation mask. It is final output of the segmentation algorithm. MENG Fanwen has proposed a scheme which introduces A Fast Face detection for video sequences [4]. In this study the paper proposed a fast face detection algorithm which integrated differential algorithm and mosaic rules for video. The comprehensive algorithm with temporal difference and background difference was used for acquiring and tracking the moving objects, which can accurately locate the target region. In the region, the gray rules based on mosaic image were used for detecting face. The paper used mosaic gray rules to implement fast face detection. The test results show that the detection method used in the paper can accurately detect and trace human face in video frequency. Zheng Cao and Ming Zhu have proposed a scheme which introduces an efficient video similarity search approach is proposed [5]. A new video feature representation ICC is computed based on the statistics of video spatial-temporal distribution. The video similarity is measured by computing the number of similar video components. Gang Zhang, Luming Yu and Wenlong Wang have presented a new video stabilization algorithm based on video object segmentation [6]. We design and implement a video stabilization system according to the fire scene video features. Shiping Zhu Xi Xia Qingrong Zhang has proposed a novel video object segmentation algorithm based on spatio-temporal integration is proposed [7]. The video sequence is segmented with change detection algorithm to get segmentation result in temporal domain. The method has [8] low complexity and high detection rate and it is also suitable for the real time face detection in video sequences. They proposed a block based scheme using the likely hood ratio comparison and features of local area of images sensitiveness to movement of the camera and object. This method determines shot change frame with variation of corresponding block of frames [9].

3. THE PROPOSED VIDEO SEGMENTATION ALGORITHMS In this paper, we proposed a new technique for video segmentation. All the process of segmentation describe as- 3.1 Proposed Algorithm color and shape based segmentation Algorithm for video. 1. Input a standard video and set the frame rate of video. 2. To use removable of low frequency data or distorted frequency data using meridian filter. 3. Apply region segmentation – (a) Shape based segmentation using morphology technique for determination of pixel. (b) Color based segmentation use three different color channel and convert each channel in binary format. 4. An estimate frequency of given video low and high on bases of selection procedure. 5. High frequency data passes through morphology and find shape structure of given video. 6. Low frequency data passes through color segmentation and video get segmented. 7. Apply SSD technique for cutting a video for morphology for detected shape and use increasing method of pixel for separate window. 8. Increase the value of low frequency and convert the video into binary format and display segmented color video. 9. In SSD techniques increases the size of frame and detect distortion of video. 10. Finally display all video in display window. 11. If shape are not detected step gone through the first.



1323

ISSN:2229-6093

3.2 Flow chart All the process of segmentation defines from given flow chart. Low High

Figure1. The flow-process diagram of video segmentation algorithm

Video input

Meridian filter

Segmentation Shape segmentation Color segmentation

Color channel Morphology

Increase size of SSD

Check frequency

Change distance of pixel

Binarization Cut video

Display

Detected segment

Display



1324

ISSN:2229-6093

4. Experimental result and analysis The performance of proposed video segmentation algorithm is tested with many parameters as- The proposed method was tested in the following conditions. PC specifications are CPU Dual core 2.6GHz and RAM 1GB. All the algorithms in the experiment were implemented with MATLAB 7.8.0 version. The video sequences were used which have 320*240 image and size.avi format, respectively. Table 1 lists the properties of original test video, changing the parameters of test video and find performance comparison with original test video properties. Table 2 lists the value of precision rate and recall rate in percentage.

Table 1.Default and changed parameters

Figure 2. The video for default parameters in table 1

Figure 3. The video for different parameters in table 1

Template parameter

Threshold low high

Frame rate

Pixel size

Constant Channel ratio

Upper side

99,108 Size

18,22 Bottom 18,25

Upper side

99,108 Size

18,22 Bottom 18,25

Upper side

99,108 Size

18,22 Bottom 18,25

0.25 ,0.60

0.20 ,0.60

0.23,0.60

5

10

20

3,3

4,4

8,8

2.5

3.0

5.5

12:23:43

14:33:53

25:53:63



1325

ISSN:2229-6093

Figure 4. The video for different parameters in table 1 Result of video processing in SSD technique as- 1. When we increase frame rate then deformation increase. 2. When we increase pixel size and frame rate constant then video quality improve. 3. When we constant pixel size and frame rate increase then video blurring is occur. 4. When we more increase the value of frame rate and pixel value then SSD can not apply. The performance is based on the accuracy of the results obtained, and precision and recall rates of each video. The Precision rate is defined as the ratio of correctly detected video to the sum of correctly detected video plus false positives. False positives are those regions in the video which are actually not parts of a object, but have been detected by the model as video regions. Correctly detected video Precision rate = ------------------------------------x100 Correctly detected video +false positives The Recall rate is defined as the ratio of correctly detected video to the sum of correctly detected video plus false negatives. False Negatives are those regions in the video which are actually objects, but have not been detected by the model.

Correctly detected video Recall rate = -------------------------------------- x 100 Correctly detected video +false negatives

Table 1.Default and changed parameter

5. Find precision rate and recall rate result from different methods. In this paper, find good result from (threshold + frame rate + window size + distance) method. 5. Conclusion In this paper basically focus on video segmentation using different techniques as here we have used color segmentation and shape segmentation. All these method simulate in simulink model (Matlab7.8.0).In experimental process getting very good result on the putting some standard parameters and also find very good precision rate and recall rate result from different changing parameters method as “threshold +frame rate +window size +distance” method. In the process of color segmentation, the conversions of color channel into binary data are very complex. In this process loss of some data of color channel. Now in future we have proposed a method for data conversion to binary data. In the process of shape segmentation using morphology technique these technique basically work of static image data but we use in moving video on that time minimize the frame rate of video and detect the structure of video. This task to take a lot of time. Now in future, minimize the time complexity of shape segmentation and improve precision rate, recall rate. 6. References [1] Won-Hee kim,Tae-II Jeong and Jong-Nam Kim “Video Segmentation Algorithm Using Threshold and Weighting Based on Moving Sliding Window” ICACT,2009,pp. 1781-1784. [2] Yasira Beevi CP and Dr.S.Natrajan “An efficient Video Segmentation Algorithm with Real time Adaptive Threshold Technique” International Journal of Signal

Methods Precision rate %

Recall rate %

Threshold +frame rate +window size

95 95.4

Threshold +frame rate +window size +distance

97 96.0

Threshold +frame rate +window size +distance +histogram

95 97.6



1326

ISSN:2229-6093

Processing,Image Processing and Pattern Recognition.Vol.2,No.4,December 2009,pp. 13-26. [3]ZHAO Xin-bo “An efficient Video Segmentation Algorithm” IEEE International Conference on Industrial and Information Systems, 2010,pp. 317-319. [4] MENG Fanwen and WV Peimin ”A Fast Face Detection for Video Sequences” IEEE International Conference on Intelligent Human-machine System and Cybernetics,2010,pp. 117-120. [5] Zheng Cao and Ming Zhu “An Efficient Video Similarity Search Algorithm “IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, May 2010. [6] Gang Zhang, Luming Yu and Wenlong Wang “Video Stabilization Algorithm Based on Video Object Segmentation” IEEE International Conference on Future Computer and Communication,vol.2,2010 [7] Shiping Zhu Xi Xia Qingrong Zhang ” A Novel Spatio-Temporal Video Object Segmentation Algorithm” IEEE.,2008. [8] CH.Y.Lu,CH.CH.Zhang and F.wen.”regional feature based Fast human face detection” J Tsinghua Univ. (Sci.and Tech.) China,vol.39, ,January 1999, pp.101- 105. [9] J.Yu and M.D.Srinath,”An efficient method for scene cutdetection,”pattern Recognition Letters,vol.22,2001,pp. 1379- 1391.



1327

ISSN:2229-6093

Documents

Deformation and Improvement of Video Segmentation Based on