Performance Analysis of Copy-Move Forgery Detection Techniques

Volume 6 Issue 1 January - March 2019

Survey Paper

Performance Analysis of Copy-Move Forgery Detection Techniques

Gulivindala Suresh *, Chanamallu Srinivasa Rao **

*_**Department of Electronics & Communication Engineering, JNTUK University College of Engineering, Kakinada, Andhra pradesh, India.

Suresh, G., & Rao, C. S. (2019). Performance Analysis of Copy-Move Forgery Detection Techniques. i-manager's Journal on Image Processing, 6(1), 38-43. https://doi.org/10.26634/jip.6.1.15925

Abstract

Copy Move Forgery (CMF) is a manipulation process, where a part of the image is copied and moved to another region in the same image. The advanced growth in technology and photo-editing software lead to the malicious manipulation of images. Distribution of such tampered images through high speed digital networks and social media is also increased, which leads to the incredibility of the images and the underlying information. Hence, it is much demanded to develop, evaluate, and propose CMF detection techniques. CMF detection can be achieved either by keypoint approach or block-based approach. In this paper, performance of block-based and keypoint based CMF detection and localization techniques are analyzed.

Iris Recognition based on Optimized Orthogonal Wavelet and Local Tetra Pattern (OOWLTrP) using Neural Network

Volume 6 Issue 1 January - March 2019

Research Paper

Iris Recognition based on Optimized Orthogonal Wavelet and Local Tetra Pattern (OOWLTrP) using Neural Network

Nuzhat F. Shaikh*

Professor and Head, Department of Computer Engineering, M. E. S. College of Engineering, Pune, Maharastra, India.

Shaikh, N. F. (2019). Iris Recognition based on Optimized Orthogonal Wavelet and Local Tetra Pattern (OOWLTrP) using Neural Network. i-manager's Journal on Image Processing, 6(1), 26-37. https://doi.org/10.26634/jip.6.1.14735

Abstract

This paper proposes a novel feature descriptor using Optimized Orthogonal Wavelets and Local Tetra Patterns OOWLTrP. Texture features are extracted by using Local Tetra Pattern (LTrP) and wavelet features are extracted through optimized orthogonal wavelet. Eye image from various databases, such as CASIA, MMUI, and UBRIS are first preprocessed to remove salt and pepper noise. Later, the iris is segmented from the rest of the eye image. Features are then extracted by using the proposed method, which combines the goodness of local patterns and orthogonal wavelets. Feed Forward Back Propagation Neural Network (FFBNN) is used for classification of images. During training, the weights of FFBNN are optimized using the Adaptive Central Force Optimization (ACFO). The well trained FFBNN-ACFO is further used for classification of iris images. It has been observed that, there is considerable improvement in accuracy and validation time of the system. The increase in accuracy is due to the fact that, LTrP extracts information from four directions as compared to LBP (Local Binary Pattern), LDP (Local Derivative Pattern), and LTP (Local Ternary Pattern). Coefficients of the orthogonal wavelet are optimization by genetic operators, this adds to the improvement in accuracy and reduction in validation time.

Skin Texture Recognition through Image Processing

Volume 6 Issue 1 January - March 2019

Research Paper

Skin Texture Recognition through Image Processing

Sukhdeep Sharma *, Aayushi Dubey**

*-**Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Haryana, India.

Sharma, S., & Dubey, A. (2019). Skin Texture Recognition through Image Processing. i-manager's Journal on Image Processing,6(1), 17-25. https://doi.org/10.26634/jip.6.1.15560

Abstract

Image processing is done to get a better version of an image or an enhanced or filtered image. It is used to extract important information from the image. A raw image is converted into a digital image by undergoing various processes for which proper algorithms and mechanisms are defined. To further investigate on an image, we can perform texture analysis on it. Texture recognition is also an important characteristic of image processing. Both texture recognition and image processing have a variety of uses in the medicine field. In this paper, the authors have discussed about how we can detect the texture of our skin and use it further to test the suitability of skin products that we use daily.

Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features

Volume 6 Issue 1 January - March 2019

Research Paper

Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features

Shaik Basheera*, M. Satya Sai Ram**

* Department of Electronics and Communication Engineering, Acharya Nagarjuna University College of Engineering, Acharya Nagarjuna University, Guntur, India.

**Department of Electronics and Communication Engineering, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.

Basheera, S., & Ram, M. S. S. (2019). Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features. i-manager's Journal on Image Processing, 6(1), 9-16. https://doi.org/10.26634/jip.6.1.15742

Abstract

This paper deals with the Brain disorder caused due to dementia, where the brain size gets effected and reduces its volume. Estimating the grade of Alzheimer's is a challenging task. Spatial texture information collected from T2 Weighted MRI images are used to perform classification and validation. The authors use 54 Brain MRI Slices, Gray Level Cooccurrence matrix is used to extract the attributes, and those are used to train the classifiers. On comparing Support Vector Machine (SVM) with Naïve Bayes classifier and KNN, SVM gives good classification accuracy of 98.1%. The classifiers classify the MRI into AD, MCI, and CN. Five independent images are collected from the Internet sources, by testing those images using SVM and correlating with clinical data of those images, it achieves 100% accuracy.

Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm

Volume 6 Issue 1 January - March 2019

Research Paper

Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm

H. A. Abdulkareem *, A. M. S. Tekanyi**, I. Yau***, K. A. Abu- Bilal****, H. Adamu*****

*-*****Ahmadu Bello University, Zaria, Nigeria.

Abdulkareem, H. A., Tekanyi, A. M. S., Yau, I., Abu-Bilal, K. A.,& Adamu, H.(2019). Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm. i-manager's Journal on Image Processing, 6(1), 1-8.https://doi.org/10.26634/jip.6.1.16363

Abstract

Transformation in mobile networks and multimedia communications make image and video compression important aspects of digital image processing. The main aim of image or video compression is to reduce the size of the image or video (redundancy) with little or no degradation of quality for an effective transmission and storage. This paper presents an optimized video compression using modified intelligent behavior of firefly algorithm. A total of six (four acquired and two benchmark) sample video data were used to implement the achieved technique. Frames were extracted from the video data and stored in the form of images in a buffer. Compression of the video frames was achieved by reducing the effect of pixel intensity with larger distance part. This was identified as one of the shortcomings with the Firefly Optimization Algorithm (FOA) method of image compression. In this paper, the impact of the modification was clearly shown using the Peak Signal to Noise Ratio (PSNR). The modification was achieved by including the root mean square in the standard equations of the FOA. In order to reduce the effect of pixel intensity with larger distance part, this was identified as one of the shortcomings. When the image samples were subjected to the (mFOA) compression technique, a same amount of improvement was achieved. Simulation results indicated that the mFOA technique outperformed the FOA method. The PSNR evaluation showed an improved reduction of frame size by 7.34%, 3.30%, 4.90%, and 5.75% for respective NAERLS1.avi, NAERLS2.avi, NTA1.avi, and NTA2.avi captured benchmark video frames and also 3.56% and 3.86% for respective video frames of Akiyo.avi and Forman.avi

Estimation of Hybrid Image Compression Algorithms for Underwater Images

Volume 5 Issue 4 October - December 2018

Research Paper

Estimation of Hybrid Image Compression Algorithms for Underwater Images

R. Pandian *, S. Lalitha Kumari **, R. Raja Kumar***, D. N. S. Ravikumar****

*-** Associate Professor, Department of Electronics and Instrumentation Engineering, Sathyabama Institute of Science and Technology,Chennai, Tamil Nadu, India.

*** Professor, Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai, TamilNadu, India.

**** Assistant Professor, Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai,Tamil Nadu,India.

Pandian, R., Kumari, S. L., Kumar, R. R., & Ravikumar, D. N. S.(2018). Estimation of Hybrid Image Compression Algorithms for Underwater Images. i-manager's Journal on Image Processing, 5(4), 34-38. https://doi.org/10.26634/jip.5.4.15209

Abstract

Image compression techniques find a wide role in the field of underwater image processing. The wavelet based image compression algorithm performance mainly depends on the encoding methods adopted. In this work, symlet wavelet and hybrid encoding techniques such as Set Partitioning Hierarchy Tree and Huffman encoding are applied on an underwater image and the performance is estimated by compression ratio and PSNR. The results clearly indicate that hybrid encoding performed well. In order to assess its value, PSNR and CR are calculated. The best compression algorithm is chosen based on a compromise between PSNR and CR. The underwater images are compressed in this work.

An Evaluation and Comparison of SVM and Neural Classifier for Breast Cancer Detection Utilizing Contourlet and Discrete Wavelet Transforms

Volume 5 Issue 4 October - December 2018

Research Paper

An Evaluation and Comparison of SVM and Neural Classifier for Breast Cancer Detection Utilizing Contourlet and Discrete Wavelet Transforms

Soumya Hundekar *, Saritha Chakrasali**

* PG Scholar, Department of Information Science and Engineering, BNM Institute of Technology, Bangalore, Karnataka, India.

** Professor, Department of Information Science and Engineering, BNM Institute of Technology, Bangalore, Karnataka, India.

Hundekar, .S., & Chakrasali, S. (2018). An Evaluation and Comparison of SVM and Neural Classifier for Breast Cancer Detection Utilizing Contourlet and Discrete Wavelet Transforms. i-manager's Journal on Image Processing, 5(4), 26-33.https://doi.org/10.26634/jip.5.4.15394

Abstract

The aim of this work is to efficiently detect breast cancer at an early stage and reduce the death rates of women. The purpose of this work is to identify the tumor present in the breast region of mammogram image as benign or malignant as these images are generally of low quality and sometimes radiologists need to seek second opinion to come to the conclusion that cancer is present. The image processing procedure is applied to detect breast cancer from mammographic ROI image. Earlier doctors used MRI, CT-scan, Ultrasound techniques to detect breast cancer, using which it was difficult to identify cancerous tumour at an early stage. The proposed methodology uses mammography technique to identify the tumor present in the breast region. The discrete wavelet and Contourlet transforms are used to decompose the given gray-scale image. The statistical and textual features are being extracted from the coefficients of spatial domain along with frequency domain values. The classification of mammographic ROI image is performed using support vector and artificial neural classifiers. The tool used in this work is Matlab. This work is recommended to study for all those working on breast cancer area of an image processing domain.