This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch's t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the shapes are segregated into various regions according to its nature; otherwise, it is treated as textured image and considered the entire image as it is for the experiment. The aforesaid tests are applied regions-wise. First, the F-ratio test is applied, if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. means of the two images. If the images pass both tests, then it is concluded that the two images are the same or similar. Otherwise, they differ. Since the proposed system is distribution-based, it is invariant for rotation and scaling. Also, the system facilitates the user to fix the number of images to be retrieved, because the user can fix the level of significance according to their requirements. These are the main advantages of the proposed system.

A Framework for track human motion in an enclosed atmosphere from sequences of monocular gray scale pictures that are obtained from mounted cameras. The detection of objects that are moving uses background subtraction algorithm which is working based on Gaussian mixture models. Variable Gaussian models square measure applied to seek out the most likely matches of human movements between successive frames taken by cameras mounted in varied locations. The modified Kalman filters is used for tracking objects in each frame, and determine the possibility of each detection is being assigned to each track. An important aspect of this project is Track maintenance.

This paper proposes a novel method, based on a statistical probability distributional approach, Hotelling’s T2 statistic and Orthogonality test. If the input query image is structured, it is segmented into various regions according to its nature and structure. Otherwise, the image is treated as textured; and it is considered for the experiment as it is. The test statistic T2 is applied on each region and compares it to the target image. If the test of hypothesis is accepted, it is inferred that the query and target images are same or similar. Otherwise, it is assumed that they belong to different groups. Moreover, the Eigen vectors are computed on each region, and the orthogonality test is employed to measure the angle between the two images. The obtained results outperform the existing methods.

This paper proposes a novel technique, based on Canonical correlation analysis, and the Chi-square test is employed to test the significance of the correlation coefficients. If it is significant, then it is concluded that the input query and target images are same or similar; otherwise, it is inferred that the two images are significantly different. In order to experiment the proposed canonical correlation method, a database is designed and constructed with the help of different types of images and their feature vectors. The Fβ-measure is applied to evaluate the performance of the proposed method. The obtained results expose that the proposed technique yields better results than the existing.

This paper presents a unified learning framework for heterogeneous medical image retrieval based on a Full Range Autoregressive Model (FRAR) with the Bayesian approach (BA). Using the unified framework, the color autocorrelogram, edge orientation autocorrelogram (EOAC) and micro-texture information of medical images are extracted. The EOAC is constructed in HSV color space, to circumvent the loss of edges due to spectral and chromatic variations. The proposed system employed adaptive binary tree based support vector machine (ABTSVM) for efficient and fast classification of medical images in feature vector space. The Manhattan distance measure of order one is used in the proposed system to perform a similarity measure in the classified and indexed feature vector space. The precision and recall (PR) method is used as a measure of performance in the proposed system. Short-term based relevance feedback (RF) mechanism is also adopted to reduce the semantic gap. The Experimental results reveal that the retrieval performance of the proposed system for heterogeneous medical image database is better than the existing systems at low computational and storage cost.

In this paper, a data hiding scheme with distortion tolerance for videos is proposed, which is generally used to embed secret information into the cover video for secure transmission and protecting copyright. The secret information feasibly is a text or an image. To protect the copyright of a video, a signature represented by a sequence of binary data is embedded into the video. In the proposed scheme, initially the embedding error between the frame of the cover video and the secret information is calculated. Based on this embedding error, the stego-video is computed then the embedded data are extracted by the extraction procedure. This scheme can tolerate some distortion such as salt and pepper noise, Gaussian noise, and uniform noise, when transmitting a stego-video through any network. Experimental results and discussions reveal that the proposed scheme tolerates those distortions with acceptable video quality.

This paper proposes a novel technique, based on Canonical correlation analysis, and the Chi-square test is employed to test the significance of the correlation coefficients. If it is significant, then it is concluded that the input query and target images are same or similar; otherwise, it is inferred that the two images are significantly different. In order to experiment the proposed canonical correlation method, a database is designed and constructed with the help of different types of images and their feature vectors. The Fβ-measure is applied to evaluate the performance of the proposed method. The obtained results expose that the proposed technique yields better results than the existing.

This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The Fmeasure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

The field of image processing is addressed significantly by the role of CBIR. Peculiar query is the main feature on which the image retrieval of content based problems is dependent. Relevant information is required for the submission of sketches or drawing and similar type of features. Many algorithms are used for the extraction of features which are related to similar nature. The process can be optimized by the use of feedback from the retrieval step. Analysis of color and shape can be done by the visual contents of image. Here neural network, Relevance feedback techniques based on image retrieval are discussed.

This paper proposes a novel method, based on Full Range Autoregressive (FRAR) model with Bayesian approach for color image retrieval. The color image is segmented into various regions according to its structure and nature. The segmented image is modeled to RGB color space. On each region, the model parameters are computed. The model parameters are formed as a feature vector of the image. The Hotlling T2 Statistic distance is applied to measure the distance between the query and target images. Moreover, the obtained results are compared to that of the existing methods, which reveals that the proposed method outperforms the existing methods. Keywords- FRAR model, query image, target image, feature vector, spatial features.

A novel method for color image retrieval based on statistical non-parametric tests such as two-sample Wald Test for equality of variance and Man-Whitney U test is proposed in this paper. The proposed method tests the deviation, i.e. distance in terms of variance between the query and target images; if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. distance between the mean values of the two images; otherwise, the test is dropped. If the query and target images pass the tests then it is inferred that the two images belong to the same class, i.e. both the images are same; otherwise, it is assumed that the images belong to different classes, i.e. both images are different. The obtained test statistic values are indexed in ascending order and the image corresponds to the least value is identified as same or similar images. Here, either the query image or the target image is treated as sample; the other is treated as population. Also, some other features such as Coefficient of Variation, Skewness, Kurtosis, Variance, and Spectrum of Energy are compared between the query and target images color-wise. The proposed method is robust for scaling and rotation, since it adjusts itself and treats either the query image or the target image is the sample of other. The results obtained are comparable with the existing methods. Keywords-Variance, mean, query image, target image, non-parametric tests.

This paper proposes wavelet based orthogonal polynomial coefficients model for content based image retrieval (CBIR). The coefficients are categorized into low-frequency and high-frequency based on a criteria. The criteria is adaptively determined and fixed according to the nature and structure of the image, because the wavelet based orthogonal polynomial model spatially localizes the frequency information. Wavelet packet and Daubechies-4 transforms are jointly used to construct both approximation (low-frequency) and detailed (high-frequency) multiresolution image subbands. Color feature are extracted from low-frequency subband based on color autocorrelogram, whereas texture features are extracted from high-frequency subband based on co-occurrence matrix. Based on these features, the feature vector is formed. The proposed CBIR method reduces the feature variation when different modalities of images are combined. The proposed system assessed two medical image databases and one general image database with Minkowski-form distance method. The experimental results show that the proposed method achieves comparable retrieval performance with medical dataset; moreover, it is very fast with low computational load. Further, the obtained results were compared with other recently developed methods such as highly adaptive wavelet method, Wavelet optimization method and effective CBIR techniques. The proposed method yields better results compared to that of existing methods.

A content based image retrieval (CBIR) system for diverse collection of color images is proposed. The proposed image retrieval framework consists of Full Range Gaussian Markov Random Field (FRGMRF) model with Bayesian Approach (BA) for image feature extraction and multi-class Support Vector Machine (SVM) learning mechanism for the categorization of image feature vectors in the feature vector database. The Minkowski distance based similarity measure is performed in the final level on the pre-filtered images. In order to incorporate a better perception subjectivity, relevance feedback (RF) technique is added to update the query image dynamically. Experiments are conducted on various image databases. The experimental results based on precision and recall method are reported. Experimental result demonstrates the progress, effectiveness, and efficiency achieved by the proposed framework. Keywords- Content based image retrieval, Full Range Gaussian Markov Random Field model, Support vector machine, Relevance feedback, Bayesian approach.

This paper proposes a new and effective framework for color image retrieval based on Full Range Autoregressive Model (FRAR). Bayesian approach (BA) is used to estimate the parameters of the FRAR model. The color autocorrelogram, a new version of edge histogram descriptor (EHD) and micro-texture (MT) features are extracted using a common framework based on the FRAR model with BA. The extracted features are combined to form a feature vector, which is normalized and stored in image feature vector database. The feature vector database is categorized according to the nature of the images using the radial basis function neural network (RBFNN) and k-means clustering algorithm. The proposed system adopted Manhattan distance measure of order one to measure the similarity between the query and target images in the categorized and indexed feature vector database. The query refinement approach of short-term learning based relevance feedback mechanism is adopted to reduce the semantic gap. The experimental results, based on precision and recall method are reported. It demonstrates the performance of the improved EHD, effectiveness and efficiency achieved by the proposed framework..

Regression testing concentrates on finding defects after a major code change has occurred. Specifically, it exposes software regressions or old bugs that have reappeared. It is an expensive testing process that has been estimated to account for almost half of the cost of software maintenance. To improve the regression testing process, test case prioritization techniques organizes the execution level of test cases. Further, it gives an improved rate of fault identification, when test suites cannot run to completion.

Regression Testing is the process of executing the set of test cases which have passed on the previous build or release of the application under test in order to validate that the original features and functions are still working as they were previously. It is impracticable and in-sufficient resources to re-execute every test case for a program if changes occur. This problem of regression testing can be solved by prioritizing test cases. A regression test case prioritization technique involves re-ordering the execution of test suite to increase the rate of fault detection in earlier stages of testing process. In this paper, test case prioritization algorithm is proposed to identify the severe faults and improve the rate of fault detection. This proposed test case prioritization algorithm prioritizes the test cases based on four groups of practical weight factor such as: customer allotted priority, developer observed code execution complexity, changes in requirements, fault impact, completeness and traceability. The proposed prioritization technique is validated with three different validation metrics and is experimented using two projects. The effectiveness of proposed technique is achieved by comparing it with un-prioritized ones and by validation metrics.

The techniques of test case prioritization schedule the execution order of test cases to attain respective target, such as enhanced level of forecasting the fault. The requirement of the prioritization can be viewed as the en-route for deriving an order of relation on a given set of test cases which results from regression testing. Alteration of programs between the versions can cause more test cases which may respond differently to following versions of software. In this, a fixed approach to prioritizing test cases avoids the preceding drawbacks. The JUnit test case prioritization techniques operating in the absence of coverage information, differs from existing dynamic coverage-based test case prioritization techniques. Further, the prioritization test cases relying on coverage information were projected from fixed structures relatively other than gathered instrumentation and execution. Keywords: Software Testing, Regression Testing, Test Case, Prioritization, JUnit, Call Graph

Abstract Regression Testing is the process of executing the set of test cases which have passed on the previous build or release of the application under test in order to validate that the original features and functions are still working as they were previously. It is impracticable and in-sufficient resources to re-execute every test case for a program if changes occur. This problem of regression testing can be solved by prioritizing test cases. A regression test case prioritization technique involves re-ordering the execution of test suite to increase the rate of fault detection in earlier stages of testing process. In this paper, test case prioritization algorithm is proposed to identify the severe faults and improve the rate of fault detection. This proposed test case prioritization algorithm prioritizes the test cases based on four groups of practical weight factor such as: customer allotted priority, developer observed code execution complexity, changes in requirements, fault impact, completeness and traceability. The proposed prioritization technique is validated with three different validation metrics and is experimented using two projects. The effectiveness of proposed technique is achieved by comparing it with unprioritized ones and by validation metrics. Keywords Regression Testing, Test case prioritization, Fault severity, Rate of fault detection.

Dynamic texture is the texture which is in motion. Dynamic texture segmentation is a challenging task, as the texture can change in shape and direction over time. In this paper segmentation of Dynamic textures into distinct regions has been done. For the segmentation of dynamic texture, three different techniques are combined together to obtain better segmentation. Two local texture descriptors called Local binary pattern and Weber local descriptor are used. These descriptors, when used in spatial domain helps to segment a frame of video into distinct regions based on the histogram of the region. Also using the same texture descriptors in temporal domain, it is possible to obtain the dynamic texture in a given video. In addition to these texture descriptors, optical flow of pixels is used for detecting dynamic texture in a video, as optical flow is the natural method for detection of motion. After computing the three different features from multiple split sections of a group of video frames, individual histograms are obtained for each of the split sections of the video. These histograms each are converted to a single value called the Cumulative. The cumulative so obtained is compared with the threshold and filtered to obtain the dynamic texture. Since the histograms are converted to a single value, the computation of threshold is very easy as the whole set of values of Cumulative falls in two different set of values. The magnitude of motion detected depends on the threshold selected. Keywords- Dynamic texture- Dynamic texture, segmentation, Cumulative, Texture descriptor, optical flow.

An embedded system is presented in which face recognition and facial recognition for Human-Robot Interaction are implemented. To detect face with a fast and reliable way, HMM combined with SVM algorithm is used. The full pose from face recognition data base is considered to detect the face recognition. Performance of the face recognition reaches to 99.617%. The two main advantages of our method are that it does not require manually selected facial landmarks or head pose estimation. In order to improve the performance of our pose normalization method in face recognition, an algorithm is presented for classifying whether a given face image is at a frontal or non frontal pose. In addition to the proposed method, a pre processing state of detecting the face is included here. The images which are non faces are detected and eliminated from the database is an another main advantage in of this work.

Image degradation usually occurs when certain image losses the stored information due to conversion or digitalization that decreases the quality of the image. Variation in brightness of color information in images is usually called as noise. Image usually subjects to noise of many types. Gaussian noises that occur in the image are concentrated. The principle of Fourier transformation is used so as to decompose the image into sine and cosine components for representing the image in particular frequency. Image histograms are an important technique for inspecting images that are used to spot the background and grey value range at a glance. The concepts of spatiogram with both first order and second order are to enhance the image with high definition clarity thereby removing the degradation in image. In addition, the images were preevaluated before image manipulation process. Keywords: Gaussian noise, Fourier transformation, Histogram, Spatiogram

This paper proposes a unified framework for color image retrieval, based on statistical multivariate parametric tests, namely test for equality of covariance matrices, test for equality of mean vectors, and the orthogonality test. The proposed method tests the variation between the query and target images; if it passes the test, then it proceeds to test the spectrum of energy of the two images; otherwise, the test is dropped. If the query and target images pass both the tests then it is concluded that the two images belong to the same class, i.e. both the images are same; otherwise, it is assumed that the images belong to different classes, i.e. both the images are different. The obtained test statistic values are indexed in ascending order and the image corresponds to the least value is identified as same or similar images. Here, either the query image or target image is treated as sample; the other is treated as population. Also, some other features such as Coefficient of Variation, Skewness, Kurtosis, Variance-Covariance, Spectrum of Energy, and number of shapes in the images are compared between the query and target images color-wise. Furthermore, to emphasize the efficiency of the proposed system, the geometrical structure, viz. test for orthogonality between the query and target images, is examined. In the case of structure images, the number of shapes in the query and target images are compared; if it matches, then the contents in the shapes are compared color wise. The proposed system is invariant for scaling, and rotation, since the proposed system adjusts itself and treats either the query image or the target image is the sample of other. The proposed framework provides hundred percent accuracy if the query and target images are same, whereas there is a slight variation for similar, scaled, and rotated images.

The advent of large scale digital image database leads to great challenges in contentbased image retrieval (CBIR) method. The CBIR is considered an active area of research; however, it comprises a strong backdrop for new methodologies and system implementations. Hence, many research contributions focus on these techniques to enable higher image retrieval accuracy while preserving the low level computational complexity. This paper proposes a CBIR method, which is based on an efficient combination of multiresolution based color and texture features. This paper considers color autocorrelogram of the hue(H) and saturation(s) components of HSV color space for color features, and value(V) component of HSV color space for texture features. These two image features are extracted by computing co-occurrence matrix at optimum level image, which is the basis for the formation of feature vector. Though the optimum level is constructed based on wavelet transform, which contains a few dominant wavelet coefficients. The efficiency of the proposed system is tested with standard image databases, and the experimental results show that the proposed method achieves better retrieval accuracy at optimum level; moreover, the proposed method is very fast with low computational load. The obtained results are compared with existing techniques such as orthogonal polynomial model, multiresolution with BDIP-BVLC method and GLCMbased system, and results reveal that the proposed method outperforms the existing methods.

Scheduling test cases by using test case prioritization technique enhances their efficiency of attaining some performance criteria. The rate at which the errors are detected within the testing process is one such criteria. An enhanced rate of fault detection during testing can provide quicker feedback on the system under test thereby allowing s/w engineers to rectify errors before usual time. An application of prioritization techniques includes regression testing of s/w which has undergone alterations. To optimize regression testing s/w testers may assign test case preferences so that to some extent the more significant ones are run earlier in the regression testing process. Software testing and retesting is an inherent part of software development as far as the errors are detected at the earliest that may not change the sequence hence gaining confidence. Regression testing has been proved to be crucial stage of software testing .Regression test prioritization techniques manipulates the execution of test case so that faults are detected at the earliest. To achieve performance requirements test cases with higher priority are executed than those with lower priority by test case prioritization techniques. This proposed test case prioritization algorithm prioritizes the test cases based on four groups of practical weight factors such as Time factor, Defect factors, Requirement factor and complexity factors. The proposed technique is validated with three different validation metrics and is experimented using two projects The algorithm illustrated detects serious errors at earlier phases of testing process and effectiveness between prioritized and un prioritized test cases is compared using ASFD. KEY WORDS: Regression Testing, Test case, Test case prioritization, Fault severity, Rate of fault detection

A statistical approach, based on full range Gaussian Markov random field model, is proposed for texture analysis such as texture characterization, unique representation, description, and classification. The parameters of the model are estimated based on the Bayesian approach. The estimated parameters are utilized to compute autocorrelation coefficients. The computed autocorrelation coefficients fall in between -1 and +1. The coefficients are converted into decimal numbers using a simple transformation. Based on the decimal numbers, two texture descriptors are proposed: (i) texnum, the local descriptor; (ii) texspectrum, the global descriptor. The decimal numbers are proposed to represent the textures present in a small image region. These numbers uniquely represent the texture primitives. The textured image under analysis is represented globally by observing the frequency of occurrences of the texnums called texspectrum. The textures are identified and are distinguished from untextured regions with edges. The classification analyses such as supervised and unsupervised are performed on the local descriptors.

The advanced applications and services to incorporate the context awareness are required by the persisting advances in the fields of wireless network and mobile computing. In distributed mobile networks, context awareness is considered as a vital and beneficial feature. Generally, mobile devices have certain constraints such as processing, storage space etc which can be overcome by using context awareness. Context plays a major role in filtering the data and services transmitted to the devices which results in reduced processing cost. In this paper, we design ontology based middleware architecture for context aware service discovery. The architecture uses contextual ontologies to allow the semantically enhanced contextual requests for services.

The existing mobile service discovery approaches do not completely address the issues of service selection and the robustness faced to mobility. The infrastructure of mobile service must be QoS-aware plus context-aware (i.e.) aware of the user's required-QoS and the QoS offered by the other networks in user's context. In this paper, we propose a cluster based QoS-aware service discovery architecture using swarm intelligence. Initially, in this architecture, the client sends a service request together with its required QoS parameters like power, distance, CPU speed etc. to its source cluster head. Swarm intelligence is used to establish the intra and inter cluster shortest path routing. Each cluster head searches the QoS aware server with matching QoS constraints by means of a service table and a server table. The QoS aware server is selected to process the service request and to send the reply back to the client. By simulation results, we show that the proposed architecture can attain a good success rate with reduced delay and energy consumption, since it satisfies the QoS constraints. KEYWORDS: QoS-Aware, Ant Colony Optimization (ACO), Swarm Intelligence, Mobile Ad Hoc Networks (MANETs)

This paper proposes a Full Range Gaussian Markov Random Field (FRGMRF) model for monochrome image compression, where images are assumed to be Gaussian Markov Random Field. The parameters of the model are estimated based on Bayesian approach. The advantage of the proposed model is that it adapts itself according to the nature of the data (image) because it has infinite structure with a finite number of parameters, and so completely avoids the problem of order determination. The proposed model is fitted to reconstruct the image with the use of estimated parameters and seed values. The residual image is computed from the original and the reconstructed images. The proposed FRGMRF model is redefined as an error model to compress the residual image to obtain better quality of the reconstructed image. The parameters of the error model are estimated by employing the Metropolis-Hastings (M-H) algorithm. Then, the error model is fitted to reconstruct the compressed residual image. The Arithmetic coding is employed on seed values, average of the residuals and the model coefficients of both the input and residual images to achieve higher compression ratio. Different types of textured and structured images are considered for experiment to illustrate the efficiency of the proposed model. The results obtained by the FRGMRF model are compared to the JPEG2000. The proposed approach yields higher compression ratio than the JPEG whereas it produces Peak Signal to Noise Ratio (PSNR) with little higher than the JPEG, which is negligible. Keywords: Image Compression; FRGMRF Model; Bayesian Approach; Seed Values; Error Model

This paper introduces a novel approach, i.e. block oriented-restoration, based on a Family of Full Range Autoregressive (FRAR) model to restore the information lost, and this adopts the Bayesian approach to estimate the parameters of the model. The Bayesian approach, by combining the prior information and the observed data known as posterior distribution, makes inferences. The loss of information caused is due to errors in communication channels, through which the data are transmitted. In most applications, the data are transmitted block wise. Even if there is loss of a single bit in a block, it causes loss in the whole block and the impact may reflect on its consecutive blocks. In the proposed technique, such damaged blocks are identified, and to restore it, a priori information is searched and extracted from uncorrupted regions of the image; this information and the pixels in the neighboring region of the damaged block are utilized to estimate the parameters of the model. The estimated parameters are employed to restore the damaged block. The proposed algorithm takes advantage of linear dependency of the neighboring pixels of the damaged block and takes them as source to predict the pixels of the damaged block. The restoration is performed at two stages: first, the lone blocks are restored; second, the contiguous blocks are restored. It produces very good results and is comparable with other existing schemes.

We use Low Density Parity Check (LDPC) error correction code to solve fuzziness i. e. , the variability and noise in iris code generated from Iris Recognition System (IRS) and Secure Hash Algorithm (SHA-512) to transform unique iris code into hash string to make them as a Cancellable Biometric. SHA-512 hash string is used as a key for 512 bits Advanced Encryption Standard (AES) encryption process. In the decryption process, Hash comparison is made between new hash generated by SHA-512 from error corrected iris code and hash stored in smart card. If authentic, 512 bits AES decryption is accomplished. Experiments indicate that, use of LDPC code results in better separation between genuine and impostor users which improves the performance of this novel cryptosystem. Security of this system is very high, which is 2256 under birthday attack. AES algorithm is enhanced by using 512 bits key and increasing the number of rounds.

We introduce a novel way to authenticate an image using Low Density Parity Check (LDPC) and Secure Hash Algorithm (SHA) based iris recognition method with reversible watermarking scheme, which is based on Integer Wavelet Transform (IWT) and threshold embedding technique. The parity checks and parity matrix of LDPC encoding and cancellable biometrics i.e., hash string of unique iris code from SHA-512 are embedded into an image for authentication purpose using reversible watermarking scheme based on IWT and threshold embedding technique. Simply by reversing the embedding process, the original image, parity checks, parity matrix and SHA-512 hash are extracted back from watermarked-image. For authentication, the new hash string produced by employing SHA-512 on error corrected iris code from live person is compared with hash string extracted from watermarked-image. The LDPC code reduces the hamming distance for genuine comparisons by a larger amount than for the impostor comparisons. This results in better separation between genuine and impostor users which improves the authentication performance. Security of this scheme is very high due to the security complexity of SHA-512, which is 2256 under birthday attack. Experimental results show that this approach can assure more accurate authentication with a low false rejection or false acceptance rate and outperforms the prior arts in terms of PSNR.

This paper introduces an efficient approach to protect the ownership by hiding iris code from iris recognition system into digital image for an authentication purpose using the reversible watermarking scheme. This scheme embeds bookkeeping data of histogram modification and iris code into the first level high frequency sub-bands of images found by Integer Wavelet Transform (IWT) using threshold embedding technique. The watermarked-image carrying iris code is obtained after applying inverse IWT. Simply by reversing the embedding process, the original image and iris code are extracted back from watermarked-image. Authentication is done using the metric called Hamming Distance. Experimental results show that this approach outperforms the prior arts in terms of PSNR. Also, we tested with different attacks on watermarked-image for showing the sustainability of the system.

This paper proposes a simple but efficient scheme for colour image retrieval, based on statistical tests of hypothesis, namely test for equality of variance, test for equality of mean. The test for equality of variance is performed to test the similarity of the query and target images. If the images pass the test, then the test for equality of mean is performed on the same images to examine whether the two images have the same attributes / characteristics. If the query and target images pass the tests then it is inferred that the two images belong to the same class i.e. both the images are same; otherwise, it is assumed that the images belong to different classes i.e. both the images are different. The obtained test statistic values are indexed in ascending order and the image corresponding to the least value is identified as same / similar images. The proposed system is invariant for translation, scaling, and rotation, since the proposed system adjusts itself and treats either the query image or the target image is sample of other. The proposed scheme provides cent percent accuracy if the query and target images are same, whereas there is a slight variation for similar, transformed. Keywords: Variance, Mean, Query Image, Target Image, Tests of Hypothesis

In this paper, a novel technique is proposed based on a Family of Full Range Autoregressive (FRAR) models to extract edges in 2D monochrome images. The model parameters are estimated based on Bayesian approach and is used to smooth the input images. At each pixel location, residual value is calculated by differentiating the original image and its smoothed version. Edge magnitudes and its directions are measured based on the residual. The edge magnitudes are squared to enhance the edges whereas the other values are suppressed by using confidence limit is based on the global descriptive statistics. Threshold value is fixed automatically based on the autocorrelation value calculated on the smoothed image. This extracts the thick edges. To obtain thin and continuous edges, the nonmaxima suppression algorithm is applied with the confidence limit based on the local descriptive statistics. Then the performance of the proposed technique is compared with that of the existing standard algorithms including Canny's algorithm. Since Canny's algorithm oversmoothes across the edges, it detects the spurious and weak edges. This problem is overcome in the proposed technique because it smoothes minimally across the edges. The extracted edge map is superimposed on its original image to justify that the proposed technique is locally characterize the edges correctly. Also, the proposed technique is experimented on synthetic images such as concentric circle and square images to prove that it detects the edges in all directions and edge junctions.

In this paper, we propose a family of stochastic models for image compression, where images are assumed to be Gaussian Markov random field. This model is based on stationary full range autoregressive (FRAR) process. The parameters of the model are estimated with the Monte-Carlo integration technique based on Bayesian approach. The advantage of the proposed model is that it helps to estimate the finite number of parameters for the infinite number of orders. We use arithmetic coding to store seed values and parameters of the model as it gives furthermore compression. We also studied the use of Metropolis-Hastings algorithm to update the parameters, through which some image contents such as untexturedness are captured. Different types-both textured and untextured images-are used for experiment to illustrate the efficiency of the proposed model and the results are encouraging

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