Showing papers by "Tanuja Sarode published in 2013"

ImageCompression Using Real Fourier Transform, Its Wavelet Transform And Hybrid Wavelet With DCT

Abstract: This paper proposes new image compression technique that uses Real Fourier Transform. Discrete Fourier Transform (DFT) contains complex exponentials. It contains both cosine and sine functions. It gives complex values in the output of Fourier Transform. To avoid these complex values in the output, complex terms in Fourier Transform are eliminated. This can be done by using coefficients of Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST). DCT as well as DST are orthogonal even after sampling and both are equivalent to FFT of data sequence of twice the length. DCT uses real and even functions and DST uses real and odd functions which are equivalent to imaginary part in Fourier Transform. Since coefficients of both DCT and DST contain only real values, Fourier Transform obtained using DCT and DST coefficients also contain only real values. This transform called Real Fourier Transform is applied on colour images. RMSE values are computed for column, Row and Full Real Fourier Transform. Wavelet transform of size N2xN2 is generated using NxN Real Fourier Transform. Also Hybrid Wavelet Transform is generated by combining Real Fourier transform with Discrete Cosine Transform. Performance of these three transforms is compared using RMSE as a performance measure. It has been observed that full hybrid wavelet transform obtained by combining Real Fourier Transform and DCT gives best performance of all. It is compared with DCT Full Wavelet Transform. It beats the performance of Full DCT Wavelet transform. Reconstructed image quality obtained in Real Fourier-DCT Full Hybrid Wavelet Transform is superior to one obtained in DCT, DCT Wavelet and DCT Hybrid Wavelet Transform.

Image Compression Using Column, Row and Full Wavelet Transforms Of Walsh, Cosine, Haar, Kekre, Slant and Sine and Their Comparison with Corresponding Orthogonal Transforms

Abstract: In this paper, image compression using orthogonal wavelet transforms of Walsh, Cosine, Haar, Kekre, Slant and Sine is studied Wavelet transform of size N 2 xN 2 is generated using its corresponding orthogonal transform of size NxN These wavelet transforms are applied on R, G, and B planes of 256x256x3 size colour images separately In each transformed plane rows/columns are sorted in their descending order of energy, and lowest energy coefficients are eliminated to compress the image This procedure is repeated for different compression ratios and in each case image is reconstructed Root Mean Square Error (RMSE) between original image and reconstructed image is calculated to measure the performance of transform Wavelet transforms are applied in three different ways: Column wavelet, Row wavelet and Full wavelet and their performance is compared using RMSE From the results it has been observed that, RMSE obtained using full wavelet transform is nearly half than column and row wavelet transform Also, results of wavelet transforms are compared with results of their orthogonal transforms Full DCT wavelet transform outperforms all other transforms

Hybrid watermarking of color images using dct- wavelet, dct and svd

Abstract: This paper presents a technique of digital image watermarking using DCT wavelet transform. Use of Haar wavelet is very common in watermarking. However, here DCT wavelet transform of size 256*256 is generated using existing well known orthogonal transform DCT of dimension 128*128 and 2*2. This DCT Wavelet transform is used in combination with the orthogonal transform DCT and SVD to increase the robustness of watermarking. HL2 sub-band is selected for watermark embedding. Performance of the proposed watermarking scheme is evaluated against various image processing attacks like contrast stretching, image cropping, resizing, histogram equalization and Gaussian noise. DCT wavelet transform performs better than our previously proposed DWT-DCT-SVD based watermarking scheme wherein Haar functions are used as basis functions for wavelet transform.

Hybrid Approach for Detection of Hard Exudates

Abstract: Diabetic Retinopathy is a severe and widely spread eye disease which can lead to blindness. Hence, early detection of Diabetic Retinopathy is a must. Hard Exudates are the primary sign of Diabetic Retinopathy. Early treatment of Diabetic Retinopathy is possible if we detect Hard Exudates at the earliest stage. The main concentration of this paper is to discuss techniques for efficient detection of Hard Exudates. The first technique, discusses Hard Exudates detection using mathematical morphology. The second technique, proposes a Hybrid Approach for Detection of Hard Exudates. This approach consists of three stages: preprocessing, clustering and post processing. In preprocessing stage, we resize the image and apply morphological dilation. The clustering stage applies Linde-Buzo-Gray and k-means algorithm to detect Hard Exudates. In post processing stage, we remove all unwanted feature components from the image to get accurate results. We evaluate the performance of the above mentioned techniques using the DIARETDB1 database which provides ground truth. The optimal results will be obtained when the number of clusters chosen is 8 in both of the clustering algorithms.

Image Scrambling using R-Prime Shuffle

Abstract: The recent growth of networked multimedia systems has increased the need for the protection of digital media. Digital media includes text, digital audio, images, video and software. Image Scrambling techniques are designed to make the image content unintelligible. In this paper, we have introduced a Novel approach for securing image data. The method proposed is a simple but powerful technique. The method uses R-Prime Shuffle to encrypt the image. It makes use of two different R-Prime numbers for rows and columns which make it more robust to decryption.

Multi-resolution Analysis of Multi-spectral Palmprints using Hybrid Wavelets for Identification

Abstract: Palmprint is a relatively new physiological biometric used in identification systems due to its stable and unique characteristics. The vivid texture information of palmprint present at different resolutions offers abundant prospects in personal recognition. This paper describes a new method to authenticate individuals based on palmprint identification. In order to analyze the texture information at various resolutions, we introduce a new hybrid wavelet, which is generated using two or more component transforms incorporating both their properties. A unique property of this wavelet is its flexibility to vary the number of components at each level of resolution and hence can be made suitable for various applications. Multi-spectral palmprints have been identified using energy compaction of the hybrid wavelet transform coefficients. The scores generated for each set of palmprint images under red, green and blue illuminations are combined using score-level fusion using AND and OR operators. Comparatively low values of equal error rate and high security index have been obtained for all fusion techniques. The experimental results demonstrate the effectiveness and accuracy of the proposed method.

Image Compression using Wavelet Transforms of DCT, DST, Hartley and Real-DFT with Variation in Size of Component Transforms

Abstract: This paper presents simple wavelet based image compression method where wavelet transform is generated from orthogonal component transforms. Wavelets of DCT, DST and Real-DFT and Discrete Hartley Transform (DHT) are generated. Each generated wavelet transform is applied separately on R, G, and B plane of 256x256x3 colour image. Performance of these wavelet transforms is evaluated using two parameters. One is compression ratio and other is rate- distortion graph. Size of component transforms in their respective wavelet transform is varied and results are compared. From results it is observed that DCT and Real-DFT wavelet gives better compression using component transform size of m=8 and n=32. DST wavelets show considerable blocking effect. Blocking effect is more intense if local component transform size (i.e. 'n') is larger. These wavelet transforms are compared in terms of bit rate as a function of distortion. It shows that DCT wavelet gives best performance among all four. Real-DFT ranks second followed by Hartley wavelet and then Discrete Sine wavelet transform.

Digital image encryption using improved chaotic map lattice

Abstract: Chaotic map lattice (CML) has been used recently for digital image encryption; however, it has several flaws in terms of irreversibility and violates some basic principles of cryptography. In this work, we propose an improved CML (ICML) using piecewise linear chaotic maps instead of logistic map to achieve better chaotic properties, and zigzag coupling direction for chained iteration in ICML. The results of several experimental, statistical analysis and key sensitivity tests show that the proposed image encryption scheme provides an efficient and secure way for image encryption, and has properties desirable in a good security cryptosystem.

Robust Watermarking using Walsh Wavelets and SVD

Abstract: In this paper, a wavelet domain based watermarking technique is proposed. However instead of using traditional Haar wavelet functions, Walsh wavelet transform is used that is derived from orthogonal Walsh transform matrices of different sizes. 256*256 Walsh wavelet is generated using 128*128 and 2*2 Walsh transform matrix and then using 64*64 and 4*4Walsh matrix which depicts the resolution of host image taken into consideration. It is supported by DCT and SVD to increase the robustness. Walsh wavelet based technique is then compared with DCT wavelet based method. Performance of three techniques is compared against various attacks and they are found to be almost equivalent. However, computationally Walsh wavelet is preferable over DCT wavelet. Also Walsh wavelet obtained by 64*64 and 4*4 is preferable over DCT wavelet and Walsh wavelet obtained from corresponding orthogonal transform matrix of size 128*128 and 2*2.

Performance Comparison of Wavelets Generated from Four Different Orthogonal Transforms for Watermarking With Various Attacks

Abstract: This paper proposes a watermarking technique using different orthogonal wavelet transforms like Hartley wavelet, Kekrewavelet, Slant wavelet and Real Fourier wavelet transform generated from corresponding orthogonal transform. Theseorthogonal wavelet transforms have been generated using different sizes of component orthogonal transform matrices.For example 256*256 size orthogonal wavelet transform can be generated using 128*128 and 2*2 size componentorthogonal transform. It can also be generated using 64*64 and 4*4, 32*32 and 8*8, 16*16 and 16*16 size componentorthogonal transform matrices. In this paper the focus is to compare the performance of above mentioned transformsgenerated using 128*128 and 2*2 size component orthogonal transform and 64*64 and 4*4 size component orthogonaltransform in digital image watermarking. The other two combinations are not considered as their performance iscomparatively not as good. Comparison shows that wavelet transforms generated using (128,2) combination of orthogonal transform give better performances than wavelet transforms generated using (64,4) combination of orthogonaltransformfor contrast stretching, cropping, Gaussian noise, histogram equalization and resizing attacks. Real Fourierwavelet and Slant wavelet prove to be better for histogram equalization and resizing attack respectively than DCT waveletand Walsh wavelet based watermarking presented in previous work.

An Efficient Method for Similarity Measure in Independent PCA based Classification

Abstract: The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the feature vectors of training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest training feature vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of feature vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test feature vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared with distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of training set.

Classification of Image Database Using Independent Principal Component Analysis

Abstract: The paper presents a modified approach of Principal Component Analysis (PCA) for an automatic classification of image database. Principal components are the distinctive or peculiar features of an image. PCA also holds information regarding the structure of data. PCA can be applied to all training images of different classes together forming universal subspace or to an individual image forming an object subspace . But if PCA is applied independently on the different classes of objects, the main direction will be different for them. Thus, they can be used to construct a classifier which uses them to make decisions regarding the class. Also the dimension reduction of feature vector is possible. Initially training image set is chosen for each class. PCA, using eigen vector decomposition, is applied to an individual class forming an individual and independent eigenspace for that class. If there are n classes of training images, we get n eigenspaces. The dimension of eigenspace depends upon the number of selected eigen vectors. Each training image is projected on the corresponding eigenspace giving its feature vector. Thus n sets of training feature vectors are produced. In testing phase, new image is projected on all eigenspaces forming n feature vectors. These feature vectors are compared with training feature vectors in corresponding eigenspace. Feature vector nearest to new image in each eigenspace is found out. Classification of new image is accomplished by comparing the distances between the nearest feature vector and training image feature vector in each eigenspace. Two distance criteria such as Euclidean and Manhattan distance are used. The system is tested on COIL-100 database. Performance is tested and tabulated for different sizes of training image database.

K-Means Codebook Optimization using KFCG Clustering Technique

Abstract: Codebook Optimization is a concept of vector quantization which is applied to achieve lossy compression. Optimization of the codebook helps in maintaining the quality of the image. The codebook is generated using Kekre’s Fast Codebook Generation (KFCG) algorithm and Random Selection Method. The K-means algorithm is used to optimize the codebook. The Mean Square Error (MSE) is used as the measurement parameter. The point where the MSE converges is the optimal point and the codebook at that point is said to be the optimized codebook. The results obtained show that when K-means algorithm is applied to the codebook generated by KFCG Algorithm, less MSE is obtained as compared to Random Selection Method.

Performance Comparison of Transforms using Row Mean and Column Mean for Hand Gesture Recognition

Abstract: computer interaction is a major issue in research industry. In order to offer a way to enable untrained users to interact with computer more easily and efficiently gesture based interface has been paid more attention. This paper presents a new approach for hand gesture recognition. An approach consists of three modules: a) Preprocessing of the image b) Feature extraction c) Pattern matching for gesture recognition. Feature extraction is based on feature vector of transformed image using Discrete Cosine Transform, Walsh Transform, Haar transform and Kekre's transform. This transforms are applied on column mean and row mean of the images and various percentage of feature vectors are generated such as 100%, 50%, 25%, 12.5% and 6.25%. Results found to be better than existing system.

Performance Comparison of K-means Codebook Optimization using different Clustering Techniques

Abstract: Vector quantization is a compression technique which is used to compress the image data in the spatial domain. Since it is a lossy technique, so maintaining the image quality and the compression ratio is a difficult task. For this, the codebook which stores the image data should be optimally designed. In this paper, the K-means algorithm is used to optimize the codebook. We have generated the codebooks from LBG, KPE, KFCG and Random Selection Methods. It is found that the Mean Square Error reduces for every iteration and after a certain number of iterations it stops reducing because the optimal value is reached. We can say that the codebook is optimized at that point. The results show that the codebook obtained from KFCG Algorithm has a least Mean Square Error. This shows that KFCG codebook is more nearer to the optimal point and when applied with K-means algorithm gives the best optimization in comparison with other algorithms.