Shivaprakash Koliwad - Academia.edu (original) (raw)

Papers by Shivaprakash Koliwad

Nowadays, a chief problem encountered by content providers and owners is the protection of their ... more Nowadays, a chief problem encountered by content providers and owners is the protection of their material. They are apprehensive about copyright protection and further forms of exploitation of their digital content. The ease by which digital information can be duplicated and distributed has led to the need for effective copyright protection tools. Diverse techniques including watermarking have been introduced in effort to tackle these increasing concerns. Recently digital watermarking technology has emerged as an effective solution for protecting the digital content from unauthorized copying. A wide variety of watermarking techniques have been proposed by researchers for the copyright protection of digital images. An extensive review of the prevailing literature in watermarking of digital images for copyright protection is presented along with the classification. In addition, a concise introduction about digital watermarking is presented along with its properties, applications and t...

Remote Sensing and Digital Image Processing, 2019

Crop-type classification has been relied upon on only spectral/spatial features. It does not prov... more Crop-type classification has been relied upon on only spectral/spatial features. It does not provide the in-season information for researchers and decision makers for both practical and scientific purposes. While satellite images have desirable spectral and spatial information for classification, the ability to extract temporal information in satellite data remains a challenge due to revisiting frequency and gaps in the time period of capturing the data. To circumvent this challenge and generate more accurate results for an in-season crop-type classification, we have used Rectified Linear Unit (RLU) approach based on the concept of deep neural networks for intelligent and scalable computation of the classification process. The work was carried out on Nanjangud Taluk located in Mysuru District, Karnataka state on a Landsat data (multi-temporal scene) from 2010 to 2015. The results indicate that RLU shows an improvement of 5% to 15% for overall classification accuracy at 3 classes over the traditional against support vector machine. In comparison with KSRSC data set, this study reveals an accuracy of 85% for classifying rice and banana with an improvement of 10% over KSRCS crop-filed data.

5.1 Introduction An anonymous quote reads, "An image speaks more than ten thousand words.&qu... more 5.1 Introduction An anonymous quote reads, "An image speaks more than ten thousand words." For mammographic images, this quote could be rephrased to read, "Images save more than ten thousand lives," as using mammography screening to detect cancer is effective in reducing breast cancer mortality rates by 30 to 70%. Breast cancer is the most common form of cancer in the human female, affecting an average of 1 in 11 women at some phase of their lives in the Western world. Currently, x-ray mammography is the clinical gold standard for the detection of breast cancer. Mammographic images are large in size, high in resolution, and require voluminous storage space and transmission bandwidth. Sophisticated compression methods are required to reduce representative data while preserving clinical information. This chapter is an attempt to review various lossless compression algorithmic techniques, some of which have been exclusively developed for mammographic images. The cha...

Soft computing techniques are becoming popular in designing real world applications. Researchers ... more Soft computing techniques are becoming popular in designing real world applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms, decision trees etc. to develop hybrid intelligent autonomous classification systems that provide more flexibility by exploiting tolerance and uncertainty of real life situations. The paper reviews soft classification techniques for Remotely Sensed Data. The emphasis is placed on the summarization of major soft classification approaches and the techniques used for RS Data Classification. Keywords— Remote Sensing, Soft Computing, Artificial Neural Networks, Genetic Algorithms, Decision Tree and Fuzzy Logic.

Biologically Rationalized Computing Techniques For Image Processing Applications, 2017

Attempts to classify high-resolution satellite data with conventional classifier show limited suc... more Attempts to classify high-resolution satellite data with conventional classifier show limited success since the traditional-per-pixel classifiers examine only the spectral variance ignoring the spatial distribution of the pixels corresponding to the land use/land cover classes. The work is carried out in two stages on panchromatic sharpened IRS P-6 LISS-IV (2.5 m) multispectral (MS) imagery of the year 2014 of Mangalore coastal zone along the west coast of Karnataka state of India. In the first stage, in order to overcome the limitations experienced in the parametric and nonparametric classifications, the swarm intelligence optimisation technique based on Artificial Bee Colony (ABC) algorithm has been studied for twelve land cover classes that are mapped. In the second stage, to bring out a greater separability between the spectrally overlapping classes, a texture-based image classification approach has been introduced and a methodology is developed to determine the optimal window size, interpixel distance and the best combinations of texture bands in multispectral data. The five texture measures, viz. entropy (ENT), angular second moment (ASM), contrast (CON), MEAN and homogeneity (Hmg) derived from the grey-level co-occurrence matrix (GLCM), are investigated in the study. The major observations and contributions of this work are as follows: in the first stage, the image classifier employing the ABC algorithm exhibits higher classification accuracy when compared with maximum likelihood classifier. In the second stage, the results show that combining textural features and spectral bands in classification approach has proven very useful in delineating the spectrally overlapping classes, particularly at higher class hierarchy level.

Fusion of Remote Sensing (RS) Images is an important process of integrating the spectral informat... more Fusion of Remote Sensing (RS) Images is an important process of integrating the spectral information of a single sensor or the information from different kinds of sensors. The image fusion results in a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. The fused image should preserve, as closely as possible, all relevant information contained in the input images. The fusion process should not introduce any artifacts or inconsistencies, which can be discarded or mislead the human observer. In the fused image, irrelevant features and noise should be suppressed to a maximum extent. This paper explains how Discrete Wavelet Transform (DWT) can be used for merging the lower frequency component of a multi-spectral image and its higher spatial resolution images by means of rules. Then, using DWT fused image can be used to the spectral post processing such as classification which...

Artificial Bee Colony; Classification onlooker bees; MLC; Remote sensing data Abstract The presen... more Artificial Bee Colony; Classification onlooker bees; MLC; Remote sensing data Abstract The present study employs the traditional swarm intelligence technique in the classification of satellite data since the traditional statistical classification technique shows limited success in classifying remote sensing data. The traditional statistical classifiers examine only the spectral variance ignoring the spatial distribution of the pixels corresponding to the land cover classes and correlation between various bands. The Artificial Bee Colony (ABC) algorithm based upon swarm intelligence which is used to characterise spatial variations within imagery as a means of extracting information forms the basis of object recognition and classification in several domains avoiding the issues related to band correlation. The results indicate that ABC algorithm shows an improvement of 5% overall classification accuracy at 6 classes over the traditional Maximum Likelihood Classifier (MLC) and Artificia...

Image fusion is a formal framework for combining and utilizing data originating from different so... more Image fusion is a formal framework for combining and utilizing data originating from different sources. It aims at producing high resolution multispectral images from a high-resolution panchromatic (PAN) image and low-resolution multispectral (MS) image. This fused image must contain more interpretable information than can be gained by using the original image. Ideally the fused image should not distort the spectral characteristics of multispectral data as well as, it should retain the basic colour content of the original data. There are many data fusion techniques that can be used which include Principal Component Analysis (PCA), Brovey Transform (BT), Multiplicative Transform (MT) and Discrete Wavelet Transform (DWT). One of the major problems associated with a data fusion technique is how to assess the quality of the fused (spatially enhanced) MS image. This paper presents a comprehensive analysis and evaluation of the most commonly used data fusion techniques. The performance of...

Fuzzy logic, a knowledge-based method, widely used in Pattern Recognition today and is proposed t... more Fuzzy logic, a knowledge-based method, widely used in Pattern Recognition today and is proposed to be applied in remote sensing image classification. Fuzzy logic makes no assumption about statistical distribution of the data and it provides more complete information for a thorough image analysis, such as fuzzy classification results. It is interpretable and can use expert knowledge and training data at the same time. Major advantage of this theory is that it allows the natural description in linguistic terms of problems that should be solved rather than in terms of relationships between precise numerical values. This advantage, dealing with the complicated systems in simple way, is the main reason why fuzzy logic theory is widely applied in decision making. Also, it is possible to classify the remotely sensed image as well as any other digital imagery; in such a way that certain land cover classes are clearly represented in the resulting image. The urban land cover types show spectr...

This study is to classify satellite data based on supervised fuzzy classification technique. Atte... more This study is to classify satellite data based on supervised fuzzy classification technique. Attempts to classify remote sensed data with traditional statistical classification technique faced number of challenges as the traditional per-pixel classifier examine only the spectral variance ignoring the spatial distribution of the pixels, corresponding to the land cover classes and correlation between bands causes problems in classifying the data and its result. Hence in this work, we use fuzzy classification.this makes no assumption about stastical distribution of the data & it provides more complete information for a thorough image analysis.The results show that fuzzy supervised technique algorithm showed an improvement of more than 5% of accuracy at 12 classes on comparison with MLC.

Current Science, Jul 25, 2017

Korean Journal of Acupuncture, 2015

International Journal of Computer Applications, 2011

The detection and extraction of text regions in an image is a well known problem in the computer ... more The detection and extraction of text regions in an image is a well known problem in the computer vision research area. Text extraction is a critical and essential step as it sets up the quality of the final recognition result. It aims at segmenting text from background, i.e isolating text pixels from those of background. Since readymade mixed mode image data is not available, it is necessary to create our own database. The database plays an important role as segmentation is to be done in an image. In educational videos and in presentation of lectures, graphic play an important role. In television industry text and images are simultaneously transmitted. In such similar application compression of data and bandwidth play an important role. To achieve better compression and bandwidth utilization properly, an efficient segmentation technique is necessary. In this paper, we analyze mixed mode images by two methods.

The Acupuncture, 2014

The Effect of Alismatis Rhizoma Herbal-acupuncture at KI 10 on LPS-induced Nephritis in Rats

Journal of Biomedical Science and Engineering, 2009

Mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. Thi... more Mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. This is an efficient means of early detection of breast cancer. Archiving and retaining these data for at least three years is expensive, difficult and requires sophisticated data compression techniques. We propose a lossless compression method that makes use of the smoothness property of the images. In the first step, de-correlation of the given image is done using two efficient predictors. The two residue images are partitioned into non overlapping sub-images of size 4x4. At every instant one of the sub-images is selected and sent for coding. The sub-images with all zero pixels are identified using one bit code. The remaining subimages are coded by using base switching method. Special techniques are used to save the overhead information. Experimental results indicate an average compression ratio of 6.44 for the selected database.

2010 Second International conference on Computing, Communication and Networking Technologies, 2010

The recent advancement in the digital multimedia technologies has presented many facilities in th... more The recent advancement in the digital multimedia technologies has presented many facilities in the transmission, reproduction and manipulation of digital images. Nevertheless, the advancement has also brought about problems such as copyright protection for content providers. Digital watermarking is one of the proposed solutions for copyright protection of digital images. A wide variety of watermarking techniques have been proposed by researchers for the copyright protection of digital images. This paper presents a novel robust invisible watermarking scheme for embedding and extracting a digital watermark in an image to protect its copyrights. The invisible insertion of the watermark image into the original image is performed in wavelet domain using Haar wavelet transform. A mask matrix is generated using the original image with the aid of MD5 algorithm and random matrix generation. The generated mask matrix is utilized in both embedding and extraction processes. The watermark is extracted by computing the relationship degrees between the mask matrix and the watermark embedded wavelet coefficients. The results of the experimentation using attacks demonstrate the robustness and efficacy of the proposed watermarking scheme. Ownership proof could be established under various hostile attacks.

Abstract—Soft computing techniques are becoming popular in designing real world applications. Res... more Abstract—Soft computing techniques are becoming popular in designing real world applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms, decision trees etc. to develop hybrid intelligent autonomous classification systems that provide more flexibility by exploiting tolerance and uncertainty of real life situations. The paper reviews soft classification techniques for Remotely Sensed Data. The emphasis is placed on the summarization of major soft classification approaches and the techniques used for RS Data Classification.

Current Science

This study proposes a new multispectral (MS) and panchromatic (PAN) image fusion algorithm based ... more This study proposes a new multispectral (MS) and panchromatic (PAN) image fusion algorithm based on regionally weighted principal component analysis (RW-PCA) and wavelet. First, the MS images are segmented into spectrally similar regions based on the fuzzy c-means (FCM) clustering method. Secondly, based on the spectral vector's degree of membership in each region, a new RW-PCA method is proposed to fuse the MS and PAN images region by region, and fused MS images are obtained. In the traditional PCA-based fusion method, the MS and PAN images are fused globally with the same transform method. In the proposed RW-PCA-based fusion method, the local spectrum information of the MS images is employed, and the spectral information is better preserved in the fused MS images. Finally, in order to improve the quality of spectral and spatial details, the above fused MS images and the original PAN images are further fused using the wavelet-based fusion method, and the final fused MS images are obtained. Experimental results demonstrated that the proposed image fusion algorithm performs better in spectral preservation and spatial quality improvement than some other methods do.