A Blind approach of QR Code based color Image watermarking using DWT

Volume 6 Issue 2 April - June 2019

Research Paper

A Blind approach of QR Code based color Image watermarking using DWT

Lokeswara Rao K.*, B. Jagadeesh**, A. Lekhamrutha***

*Communication Engineering and signal Processing, GVP College of Engineering (Autonomous), Madhurwada, Visakhapatnam, Andhra Pradesh, India.

**_***Department of Electronics and communication Engineering, GVP College of Engineering (Autonomous), Madhurwada, Visakhapatnam, Andhra Pradesh, India.

Rao, K. L., Jagadeesh, B., & Lekhamrutha, A. (2019). A Blind approach of QR Code based color Image watermarking using DWT. i-manager's Journal on Image Processing, 6(2), 40-45.

Abstract

The fast growth in internet and communication technology has facilitated an increasing in the exchange of digital multimedia content like audio, video, and images etc. It is necessary to accomplish secure communication for the digital information in open networks. The art of hiding information has become amajor issue in the late years, the security of information has become a biginterest in this internet era. Encodinginformation using QR codes, as they have greater potential to carry different types of information in a smaller space. Due to these properties they have gained popularity in various fields of application and used for both human interaction and automated systemsThis paper includes two phases, i.e.; generating a QR code and hiding the QR code inside a color image. In the embedding process, binary image is converted into a corresponding digitally invisible watermark that is inserted in a QR code. TheQuick response(QR) code(watermark) is embedded by using the Discrete Wavelet Transform in YCbCr color space, it should be extracted blindly without the host image or original watermark after applying different image processing attacks.

Change Detection Analysis using Landsat Multi-Temporal Imagery and GIS Techniques: A Case Study for Tirupati, South India

Volume 6 Issue 2 April - June 2019

Research Paper

Change Detection Analysis using Landsat Multi-Temporal Imagery and GIS Techniques: A Case Study for Tirupati, South India

V. Raja Rajeswari*, S Narayana Reddy**

*_**Department of Electronics and Communication Engineering, SVUCE, Tirupati, India.

Rajeswari, V. R., & Reddy, S. N. (2019). Change Detection Analysis using Landsat Multi-Temporal Imagery and GIS Techniques: A Case Study for Tirupati, South India. i-manager's Journal on Image Processing, 6(2), 32-38.

Abstract

Land use/land cover (LULC) information in the form of maps is essential for the planning, supervising natural resources, utilisation of land to meet the increasing human demands and monitoring changes in the ecosystem. In this study, remote sensing data and geographic information system applications were used to analyse the LULC and its changes in Tirupati, which is located in the Seshachalam hill range in the Chittoor district of Andhra Pradesh (AP) state, South India. The study area is a world-famous pilgrimage centre and fast-developing towns. Therefore, updated LULC maps must be created for various departments. The aim of this study was to classify and determine changes in the LULC over the 40-year period 1978-2018 by using multi-temporal Landsat satellite images and Survey of India toposheet map. The 1978 and 2018 Landsat images and field survey data were selected to classify the data. The ERDAS Imagine v16 and ArcGIS v10.1 were used to process images and assess the changes in land use of this study area. Classification was performed using the maximum likelihood classifier algorithm of supervised classification. Images were classified into five major classes: forest, water bodies, agricultural land, barren land, and built-up land. A post-classification change detection technique was used to find out the changes in LULC. Changes were mainly observed in the built-up areas. The results demonstrate that during the forty years period built-up area and barren land/other land increased 454.33%, and 104.7%, and area under water bodies, agriculture and forest decreased 73.07%, 61.84% and 31%, respectively. In the future, these changes may have a significant influence on the ecosystem.

Defogging of Image Using Spatial MRF with Boundary Constraints

Volume 6 Issue 2 April - June 2019

Research Paper

Defogging of Image Using Spatial MRF with Boundary Constraints

Sivachanaukya Dora H.*, Sunil Prakash M**

*_**Department of ECE, MVGR College of Engineering, Vizianagaram, India.

Dora, H. S., & Prakash, S. M. (2019). Defogging of Image Using Spatial MRF with Boundary Constraints. i-manager's Journal on Image Processing, 6(2), 26-31.

Abstract

Capturing the image in fog scene suffer from the distortions of the information and the time taken to predict the object in the way become complicated. To overcome this difficulty the image are taken into consideration as the grown up technology opens the door for converting the analog scene to digital scene in the form of an image, image captured in less visibility of the scene predominantly in foggy weather conditions the structure of image and also several human activities like drones, aircrafts, flights and travellers which in turn will affect several computer vision applications like tracking, artificial intelligence, remote sensing. Thus restoring back the outdoor scene from such foggy image is significantly important. The main focus is to defog the image in the patch; atmospheric light in foggy days looks as that of the fog, so this to be reduced; edges and corners must be visible, inner depths to be reconstructed form the fog scene. To fulfill these characteristics of image several defog techniques was investigated. The Spatial Random Markov Fields with Boundary Constraints was proposed which performs on image within the space and patch using boundary constraints. Experimental results demonstrate that the proposed work is efficient to remove fog, restore space of an image and preserve’s the natural atmospheric light even in foggy days without changing the color.

Primary Screening Technique for Detecting Breast Cancer

Volume 6 Issue 2 April - June 2019

Research Paper

Primary Screening Technique for Detecting Breast Cancer

C. Naga Raju*, A Himabindhu**

* Associate Professor and Head, Department of Computer Science and Engineering, YSR Engineering College of Yogivemana University,Proddatur, Andhra Pradesh, India.

**Research Scholar, Department of Computer Science and Engineering, Ryalaseema University, Kurnool, Andhra Pradesh, India.

Raju, C. N., & Bindu, A. H.(2019). Primary Screening Technique for Detecting Breast Cancer. i-manager's Journal on Image Processing, 6(2),19-25.

Abstract

The breast cancer is absolutely life intimidating and dreadful disease. The primary screening of breast tumor is still under research because of some risk features such as gene, taking birth control fills, smoking, obesity and Age are playing vital role spreading the cancers. The malignant tumors that induct into the breast cells and eventually this tumor extends to the surrounding tissues. The proposed technique consists of four steps. Step1 is for digitized noises removal, step2 is for suppression of radio opaque artifacts, step3 is for Pectoral Muscle removal and step4 is for detecting location of cancer on breast for emphasizing the region of breast profile. To reveal the capability of this technique two separate digital mammograms are tested using GT( Ground Truth) mammograms for assessment of performance characteristics. The Experimental results indicate that the breast cancer regions are extracted truthfully in compliance to respective Ground Truth Images.

Gabor Based Band Selection for Multispectral Palmprint Recognition System using feature Fusion

Volume 6 Issue 2 April - June 2019

Research Paper

Gabor Based Band Selection for Multispectral Palmprint Recognition System using feature Fusion

Abubakar Sadiq Muhammad*, Auwal Sani iliyasu**, Abubakar A. Umar***, Bello A. Imam****, Shehu H.Ayagi*****, Muhammad A. Baballe******

*_******School of Technology, Department of Computer Science and Engineering, Kano State Polytechnic Kano, Nigeria.

Muhammad, A. S., Iliyasu, A. S., Umar, A. A., Imam, B. A., Ayagi, s. H., & Baballe, M. A. (2019). Gabor Based Band Selection for Multispectral Palmprint Recognition System using feature Fusion. i-manager's Journal on Image Processing, 6(2), 14-18.

Abstract

The efficiency of a palmprint recognition system lies in its robustness and antispoofing capabilities. Enhancing their capabilities will require the use of new techniques for extracting more discriminative features from collected images particularly those with different illuminations. Feature level fusion is presented using the popular using Gabor Wavelet Transform (GWT)for palmprint images collected from different light illumination Red, Green, Blue and Near Infra-red (NIR). Individual spectra were fused as triple (R, B, NIR and G, B, NIR) at feature level in the experiments, followed by verification ofsystem performance by a number of classifiers. The method demonstrates an increase in recognitionperformance of almost 100% could be obtained in the system by fusion of the spectra as compared to previous works.

Effects of Unequal Bit Costs on Data Compression

Volume 6 Issue 2 April - June 2019

Research Paper

Effects of Unequal Bit Costs on Data Compression

Mohamed Yacine Gheraibia*

University of Teluq, Montreal, Canada.

Gheraibia, M. Y. (2019). Effects of Unequal Bit Costs on Data Compression. i-manager's Journal on Image Processing, 6(2), 1-13.

Abstract

Contour representation of binary object is widely used in pattern recognition. Chain codes are a compression methods where the original data are reconstructed from the compressed data for representing binary objects including contours. Though a notably huge image size reduction is obtained by fixed-length chain code, so far, more efficient and reliable methods for data encoding is possible by using technique that treats the binary bits differently considering its requirement of storage space, energy consumption, speed of execution and so on. This paper proposes a new variant of Huffman Coding (HC) by taking into consideration the fact that the costs of bits are different, the new representation of the Freeman Chain Code (FCC) is based on an eight-direction scheme. An experimentation of the cost efficiency of the new representation over the classical FCC is described and compared to other techniques. Our experiments yield that the proposed FCC representation reduces overall both the storage and the transmission cost of encoded data considerably with compared to the classical FCC.

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.

Removal of Pectoral Muscles and Locating Cancer in Breast using Fuzzy Technique

Volume 5 Issue 4 October - December 2018

Research Paper

Removal of Pectoral Muscles and Locating Cancer in Breast using Fuzzy Technique

C. Naga Raju*, A. Hima Bindu**

* Associate Professor and Head, Department of Computer Science and Engineering, YSR Engineering College of Yogivemana University,Proddatur, Andhra Pradesh, India.

**Research Scholar, Department of Computer Science and Engineering, Ryalaseema University, Kurnool, Andhra Pradesh, India.

Raju, C. N., & Bindu, A. H.(2018). Removal Of Pectoral Muscles And Locating Cancer In Breast using Fuzzy Technique. i-manager's Journal on Image Processing, 5(4),17-25. https://doi.org/10.26634/jip.5.4.15422

Abstract

The Micro-calcification which is an early sign of breast cancer is hard to find due to its small size, poor contrast, and blurry image boundary. Pectoral Muscles on mammograms are soft tissues of the body other than breast muscles, which looks like a cancer. The fuzzy algorithms used in this scenario are fuzzy opertors that analyze the image at pixel level to detect abnormalities and identify the location of abnormalities on the breast and pectoral muscles. This paper describes a technique which consist of five steps to find location of cancers in breast by removing pectoral muscles 1) To enhance the quality of poor breast images 2) preconisation of the breast shape 3) extract the cancer part from the breast images 4) removing the Pectoral muscles depends on the orientation of the breasts 5) location of the cancer part on breast images. The result shows the possibility and adequacy of the proposed approach.

Comparative Evaluation of Nature-Based Optimization Algorithms for Feature Selection on Some Medical Datasets

Volume 5 Issue 4 October - December 2018

Research Paper

Comparative Evaluation of Nature-Based Optimization Algorithms for Feature Selection on Some Medical Datasets

Ali Muhammad Usman *, Ali Usman Abdullah**, Alhassan Adamu***, Musa M Ahmed****

*-** Department of Computer Science, Federal College of Education (Tech.), Gombe, Nigeria.

*** Department Computer Science, Kano State University of Technology, Wudil, Nigeria.

**** Lecturer, Department of Physical Science Education, MAUTECH, Yola, Nigeria.

Usman, A.M., Abdullah, A.U., Adamu, A., & Ahmed, M.M.(2018). Comparative Evaluation of Nature-Based Optimization Algorithms for Feature Selection on Some Medical Datasets.i-manager's Journal on Image Processing, 5(4), 9-16.https://doi.org/10.26634/jip.5.4.15938

Abstract

Nowadays, the veracity, velocity, values, and size of data are growing exponentially. In fact, the data is growing beyond the capacity of current hardware facilities. This resulted in high cost of storing the data. Perhaps, some of the data stored are not very useful and create problems when mining the data, to make some sense out of it. Feature selection is a step forward towards reducing the unnecessary huge amount of the stored data. In this study, Flower Pollination Algorithm (FPA) along with its binary version (BFPA) are used for feature selection on some medical datasets. The results obtained is in favor of the BFPA with better classification accuracy of over 90% on some of the datasets and fewer number of features compared to FPA, improved harmony search with rough set together with particle swarm optimization with rough set. Hence, the experimental results demonstrate the efficiency and effectiveness of BFPA as the best technique among the evaluated methods for feature selection particularly on medical datasets.

Brightness Enhancement Technique for Video Frame Improvement Based on Pixel Intensity Analysis

Volume 5 Issue 4 October - December 2018

Research Paper

Brightness Enhancement Technique for Video Frame Improvement Based on Pixel Intensity Analysis

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.(2018). Brightness Enhancement Technique For Video Frame Improvement Based On Pixel Intensity Analysis. i-manager's Journal on Image Processing, 5(4), 1-8.https://doi.org/10.26634/jip.5.4.15937

Abstract

This study developed a brightness enhancement technique for video frame pixel intensity improvement. Frames extracted from the six sample video data used in this work were stored in the buffer as images. Noise was added to the extracted image frames to vary the intensity of their pixels so that these pixel values of noisy images differ from their true values in order to determine the efficiency of the developed technique. Simulation results of this paper showed improvement in pixel intensity and histogram distribution. The Peak to Signal plus Noise Ratio evaluation showed that the efficiency of the developed technique for both grayscale and coloured video frames were improved by PSNR of 12.45%, 16.32%, 27.57%, and 19.83% over those of the grey level colour (black and white) images for the NAELS1.avi, NAELS2.avi, NTA1.avi, and NTA2.avi, respectively. Also, a percentage improvement of 28.93% and 31.68% were obtained for the coloured images over the grey level images for Akiyo.avi and Forman.avi benchmark video frames, respectively.

A Comparative Analysis of Leaf Disease Detection using Image Processing Technique

Volume 5 Issue 3 July - September 2018

Review Paper

A Comparative Analysis of Leaf Disease Detection using Image Processing Technique

C. M. Samiha*, S. P. Pavan Kumar **

* PG Scholar, Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.

** Assistant Professor, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

Samiha, C.M., and Kumar, P.S.P., (2018). A Comparative Analysis of Leaf Disease Detection Using Image Processing Technique. i-manager’s Journal on Image Processing, 5(3), 40-46. https://doi.org/10.26634/jip.5.3.15044

Abstract

The essential part of any ecosystem is plant. All the organisms get energy from plants directly or indirectly. It is important to identify the disease in plant parts like leaf, stem, and fruit. Leaf diseases are caused by virus, bacteria, etc. Normally, a farmer identifies the leaf disease by observing spots, color, and shape of the leaf, but sometimes they take help from the experts to detect diseased leaf or crops. The manual detection of disease is less accurate and complex. Image processing techniques help farmers for timely detection of the diseases. K-Nearest Neighbor (KNN), K-Means clustering, Support Vector Machine (SVM), Artificial Neural Network (ANN), and various segmentation algorithm and classifiers are used for detection and classification of leaf diseases. In this paper, various diseases that occur in parts of the plant and identification of leaf diseases were discussed.

Blood Leukemia Detection using Neural Networks and Fuzzy Logic: A Survey and Taxonomy

Volume 5 Issue 3 July - September 2018

Review Paper

Blood Leukemia Detection using Neural Networks and Fuzzy Logic: A Survey and Taxonomy

Fameshwari Deshmukh*, Amar Kumar Dey**

*PG Scholar, Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

**Assistant Professor, Department of Electronics and Telecommunication Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh,India.

Fameshwari, and Dey, A.K., (2018). Blood Leukemia Detection Using Neural Networks And Fuzzy Logic: A Survey And Taxonomy. i-manager’s Journal on Image Processing, 5(3), 34-39. https://doi.org/10.26634/jip.5.3.14984

Abstract

Blood cancer or leukemia detection using microscopic images is a challenging task considering the fact that variations in blood cell patterns are miniscule in nature and human detection may be prone to errors due to inherent deficiencies or anomalies in the dataset or due to human errors. Hence using automated classification has been considered using data pre-processing techniques such as Artificial Neural Networks and Fuzzy Logic. Recently, a new domain of research called neuro-fuzzy systems has garnered a lot of attention due to its efficacy. This paper introduces the challenges faced in the detection and classification of blood leukemia. Along with it, the paper focuses on the various significant contributions in the field by different researchers. This may pave the path for further improvement in accuracy of classification of leukemia.

LM, RP, and GD Based ANN architecture models for Biomedical Image Compression

Volume 5 Issue 3 July - September 2018

Research Paper

LM, RP, and GD Based ANN architecture models for Biomedical Image Compression

G. Vimala Kumari *, G. Sasibhushana Rao**, B. Prabhakara Rao***

* Assistant Professor, Department of Electronics and Communication Engineering, MVGR College of Engineering, Vizianagaram,Andhra Pradesh, India.

**Professor, Department of Electronics & Communication Engineering, Andhra University College of Engineering, Visakhapatnam,Andhra Pradesh, India.

*** Programme Director, School of Nanotechnology, JNTU, Kakinada, Andhra Pradesh, India.

Kumari, V.G., Rao, S.G., and Rao, p.B., (2018). LM, RP AND GD Based Ann Architecture Models For Bio Medical Image Compression. i-manager’s Journal on Image Processing, 5(3), 21-33. https://doi.org/10.26634/jip.5.3.15195

Abstract

The aim of this paper is to present an image compression method using feedforward backpropagation neural networks. Medical imaging is an efficient source for better diagnosis of the disease and also helps in assessing the severity of the disease. But due to the increasing size of the medical images, transferring and storage of images require huge bandwidth and storage space. Therefore, it is essential to derive an effective compression algorithm, which have minimal loss, time complexity, and increased reduction in size. With the concept of Neural Network, data compression can be achieved by producing an internal data representation. The training algorithm and development architecture gives less distortion and considerable compression ratio and also keeps up the capability of hypothesizing and is becoming important. The performance metrics of three algorithms, Levenberg Marquardt algorithm, Resilient backpropagation algorithm, and Gradient Decent algorithm have been computed on Magnetic Resonance Imaging (MRI) images and it is observed that Levenberg Marquardt algorithm is more accurate when compared to the other two algorithms.

Upright FAST-Harris Filter

Volume 5 Issue 3 July - September 2018

Research Paper

Upright FAST-Harris Filter

Abdulmalik Danlami Mohammed*, Saliu Adam Muhammed**, Idris Mohammed Kolo***, Adama Victor Ndako****, Shafi’i Muhammed Abdulhamid*****, Abdulkadir Baba Hassan******, Abubakar Saddiq Mohammed*******

*_****Lecturer, Department of Computer Science, Federal University of Technology, Minna, Nigeria.

*****Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria

*** ***Associate Professor, Department of Mechanical Engineering, Federal University of Technology, Minna, Nigeria

*******Department of Electrical/Electronic Engineering, Federal University of Technology, Minna, Nigeria

Mohammed, A.D., Saliu, A.M., Kolo, I.M., Ndako, A.V., Abdulhamid, S.M., Hassan, A.B., and Mohammed, A.S (2018). Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos. i-manager’s Journal on Image Processing, 5(3),14-20.https://doi.org/10.26634/jip.5.3.15689

Abstract

The traditional approaches to the classification of image regions suffer drawbacks in the face of imaging conditions (occlusion, illumination changes, rotation, viewpoint changes, and image blurring) and thus contribute to the poor performance of several vision based applications, such as object recognition, object tracking, image retrieval, pose estimation, camera calibration, 3D reconstruction, Structure from motion, stereo images, and image stitching. However, in this work, feature points extraction method by decomposition of image structure is employed in order to overcome these challenges. The decomposition of an image structure into feature set enhances the performance of many vision-based applications and system. The feature point extraction method which we refer to as Upright Feature from Accelerated Segment Test with Harris filter (UFAH) in this text, works by combining Feature from Accelerated Segment Test detector with Harris filter. The result obtained in the evaluation process shows that UFAH is robust and also invariant to imaging conditions (i.e rotation, illumination changes, and image blurring).