Sudipta Roy ¹, Siddhartha Sadhukhan ¹, Shayak Sadhu ¹, S. K. Bandyopadhyay ²

Affiliations
1 Department of MCA, Academy of Technology, Adisaptagram, Aedconagar, Hooghly - 712121, West Bengal, IN
2 Department of Computer Science and Engineering, University of Calcutta, 92 A.P.C. Road, Kolkata - 700009, West Bengal, IN

Abstract

Objective: For the purpose of Data Communication Steganography is a technique which is combination of both the science and art by which we may hide information inside other covered media. Method: Here we use 3-layers steganographic data encryption, in the first stage data are encoded based upon DNA-Sequence and then the stego-key is added with individual byte then the byte streams are encoded within the image. First encrypt the data is more secure than encoding the raw data and authentication values which provide access only to authorized persons. Finding: Only Steganography is not enough secure for the present scenario. Simple Steganography is very much vulnerable in front of attack, but if we apply some encryption on the data itself and then use Steganography then that will be more secure than the use raw data in Steganography. Application: It will cover all the fields where we need Data Security and it covers a zone of data security where we need to hide the existence of data.

Keywords

3-Layer Encryption, DNA-based Encryption, Image Steganography, Steganography

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Thiyam Ibungomacha Singh ¹, Romesh Laishram ², Sudipta Roy ³

Affiliations
1 Department of Computer Science and Engineering, Manipur Institute of Technology, Takyelpat - 795001, Manipur, IN
2 Department of Electronics and Communication Engineering, Manipur Institute of Technology, Takyelpat - 795001, Manipur, IN
3 Department of Computer Science and Engineering, Assam University, Silchar - 788011, Assam, IN

Abstract

Objectives: The development of image processing tools for medical image processing has recently generated lot of interest. Medical image segmentation is one such area of focus for many researchers over the years. Methods/Statistical Analysis: In this work we have proposed an algorithm which is a combination of Fuzzy C-Means Clustering (FCM) with spatial constraints which is called spatial FCM (SFCM) and swarm intelligence optimization methods for medical image segmentation. The swarm intelligence algorithm that we have considered in this work is the Artificial Bee Colony (ABC) optimization. Findings: The algorithm is applied to brain MRI image segmentation and compared with other existing algorithm and the validation of the algorithm is evaluated by cluster a validity function which is an indication of how good a clustering result is. The results show that the combined algorithm i.e. ABCSFCM has better performance and improve the cluster validity functions as compared to SFCM. Applications/Improvements: The result is quite promising and although the proposed algorithm is tested on brain MRI image it can be extended to other problems of interest. The other variants of FCM and other natured inspired optimization are worth investigating for further improvements.

Keywords

ABC, ABCSFCM, MRI, SFCM, Segmentation.

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Dalton Meitei Thounaojam ¹, Sudipta Roy ², Kh. Manglem Singh ³

Affiliations
1 Department of Computer Science & Engineering, National Institute of Technology, Silchar - 788010, Assam, IN
2 Department of Computer Science & Engineering, Assam University, Silchar - 788011, Assam, IN
3 Department of Computer Science & Engineering, National Institute of Technology, Imphal – 795001, Manipur, IN

Abstract

Objectives :The objective of this paper is to find out the abrupt transitions between consecutive shots in a video with less false detection and high F1 score. Method/Analysis: This paper presents a video shot boundary detection approach using Gray Level Cooccurrence Matrix (GLCM). The proposed system can roughly be divided into feature extraction using GLCM and the application of the abrupt shot boundary detection. In the first step, the frames are converted into gray level and GLCM is calculated from each frame in the video. Secondly, correlation coefficient is calculated from the GLCM of two consecutive frames of the video. A threshold is set to identify the shot boundaries of the video. The proposed system can detect abrupt transitions effectively with less false detection in the uncompressed domain. Findings: The proposed system can able to achieve an average F1 score of 93.51%, which is achieve due to the reduced false detection. Novelty/Improvement: The proposed system uses the GLCM matrix directly instead of calculating the contrast, entropy,etc, i.e., the proposed system is purely based on the correlation of the pixel's co-occurrence .The proposed system also reduced the false detection there by increasing the precision and F1 score.

Keywords

Abrupt, Gradual, Gray Level Cooccurrence Matrix, Shot Boundary Detection,Video Segmentation

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Archita Tah ¹, Sudipta Roy ², Prasenjit Das ¹, Anirban Mitra ¹

Affiliations
1 Department of Computer Science and Engineering, Academy of Technology, Adisaptagram, Hooghly – 712121, West Bengal, IN
2 Department of Computer Science and Engineering, U. V. Patel College of Engineering, Ganpat University, Kherva – 384012, Gujarat, IN

Abstract

Objectives: Object tracking and detection are significant and demanding tasks in the area of computer vision such as video surveillance, vehicle navigation, and autonomous robot navigation. Methods/Statistical Analysis: This paper presents the moving object tracking using Kalman filter and reference of background generation. Kalman filter is based on two types of filters: cell Kalman filter and relation Kalman filters. The process entails separating an object into different sub-regions and discovering the relational information between sub-regions of the moving objects. Findings: In this paper, the precise and real-time method for moving object detection and tracking is based on reference background subtraction and use threshold value dynamically to achieve a more inclusive moving target. This method can effectively eliminate the impact of luminescence changes. Due to deployment of Kalman filter this fast algorithm is very straightforward to use to detect moving object in improved way and it has also a broad applicability. This technique is very authentic and typically used in video surveillance applications. Application/Improvements: This technique is very legitimate and typically used in video surveillance applications. The Kalman filtering algorithm upgrades the model and enlarges the dimensionality of the moving system state.

Keywords

Background Subtraction, Detection and Segmentation, Moving Object, Kalman Filter, Object Tracking.

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Rajeev Rajkumar ¹, Sudipta Roy ¹, Kh. Manglem Singh ²

Affiliations
1 Department of Computer Science and Engineering, Assam University, Silchar – 788011, Assam, IN
2 Department of Computer Science and Engineering, National Institute of Technology, Imphal – 795001, Manipur,, IN

Abstract

Objective: To detect copy-move forgery from a given digital image by reducing false detection thus increasing precision rate and F1 score. It is also invariant to scaling, rotation, noise attack. Methods/Analysis: The paper describes an effective and novel method to detect the copy-move forgery detection using Singular Value Decomposition (SVD) and Scale-Invariant Feature Transform (SIFT) features. It divides the given image into equal sized blocks and applies keypoint detection and feature descriptor for each block of the image. Then again SVD is calculated from128 SIFT descriptor to detect the forgery part of the given image. In this approach, a correlation is calculated for different images under different attacks like scaling, rotation, and noise. Finding: The proposed system is tested using standard image data against various types of image attacks like scaling, rotation, noise, blur etc. From the result, it is found that the proposed system is robust and invariant to most of the image attacks. The proposed system easily detects the copied part of the image more efficiently compared to other block based method since SIFT feature is invariant to scaling, rotation, noise, blur etc. Novelty/Improvement: The proposed methods uses block based technique and apply SIFT and SVD technique for detecting forged region in the given image. False detection is reduced in the proposed system which increases the precision and F1 score.

Keywords

Copy-Move Forgery, Difference of Gaussian (DoG), Digital Forgery, Scale-Invariant Feature Transform (SIFT), Singular Value Decomposition (SVD), Tampered Image

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Daizy Deb ¹, Sudipta Roy ¹

Affiliations
1 Department of Computer Science and Engineering, Assam University, Silchar, Assam − 788011, IN

Abstract

Objectives: In this paper, we focus our research on detecting brain tumours for various diagnostic purposes in medical field. Methods/Statistical Analysis: The method applied here is soft computing for image segmentationto detect the brain tumour from a particular MRI image which is important for various diagnostic purposes in medical field. For the purpose of identifying abnormal cells in the MRI images, which are collected from various real time situations, have been passed through a de-noising algorithm followed by a clustering approach. Findings: A new Fuzzy C Means clustering method followed by multilevel thresholding and level set algorithm have been adopted to recognise the tumour affected areas. This method has been compared against existing two techniques like multilevel thresholding and K-means algorithm. K-means algorithm is more efficient regarding time but this improved technique of image segmentation ensures more precise result. Application/improvements: This algorithm is fully tested with various medical images like MRI images and also working nicely to achieve orientation of accurate shape and size of brain tumor.

Keywords

Erosion, Fuzzy C-Mean (FCM), Image Segmentation, K-Means, Level Set, Multilevel Thresholding

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