Abstract: Digital watermarking is a technique developed to help copyright protection of Image, audio & video files. It has been spurred by the growth of the Internet, and the ease of copying. This has led to an unprecedented numbers in copyright infringements. The digital audio & video watermarking is concerned with embedding data in a signal the watermark data can be accurately detected. Watermarking algorithm based on Empirical Mode Decomposition (EMD) is introduced. The signal is divided into frames and each one is decomposed adaptively by Empirical Mode Decomposition into intrinsic oscillatory components. The data embedding rate of EMD algorithm is 46.9-50.3 b/s; the robustness of hidden watermark is identified using Mp3 compression, additive noise, and quantization, cropping, filtering and resampling.
Abstract: Analyzing navigation history of a web site and that of a user reported in log files and web click stream helps in determining the components that shall be needed in the near future and fetch them in advance. Identifying the frequently visited websites from such data enhances the process. Processing and analyzing the vast amounts of click stream data and web logs that are generated at very high rates in a smart and cost-efficient way is a daunting challenge to the data mining community. Mining frequent itemsets plays an important role in analyzing such data streams. In spite of the existence of many such mining algorithms, more time and space efficient algorithms for mining frequent patterns are the need of the hour and are attracting wide attention in web click-stream analysis in recent years. In this paper an effective algorithm for mining frequent itemsets from a time-sensitive sliding window is proposed. It is a one-pass algorithm that uses a circular queue to implement the sliding window. The time-sensitive sliding window stores the web click stream data of various sessions of the web users. The proposed one-pass algorithm, FIM_CQTimeSWin has three phases: representation of time unit, maintenance of the sliding window and generation of frequent itemsets in the current sliding window.
Abstract: Content-based image retrieval (CBIR) system can be used to effectively and precisely retrieve the desired images from a large image database, and the development has become an important research issue.Statistical methods like, gray level co-occurrence matrix (GLCM) and the autocorrelation function are used to extract texture feature. Region-based methods utilize information from both boundaries and interior regions of the shape. Shape features like perimeter, area, centroid, circularity, solidity based on region can be extracted in the feature space. Similar images can be retrieved using minimum distance classifiers with and without clustering algorithm .Time complexity and the retrieval efficiency has been analyed and compared on both the methods. The experiments have been conducted on MPEG-7 dataset.
Abstract: In wireless sensor networks Energy-efficient routing is an important issue due to the limited battery power within the network, Energy consumption is one of the important performance factors. Specifically for the election of cluster head selection and distance between the cluster head node and base station. The main objective of this proposed system is to reduce the energy consumption and prolong the network lifetime. This paper introduces a new clustering algorithm for energy efficient routing based on a cluster head selection
Abstract: Cloud data centers should be flexible and available to the data forever. The replication method is used to achieve high availability and durability of cloud data center, if there is any failure to recover the messages from the cloud databases. The concern of this replication technology is that, the replica size is equal to the size of the original data object. When Error Detection Schemes were used, there is a reduction in the number of cloud distributed storage systems. The scope of this paper is to store the data efficiently in cloud data centers unlike the previous schemes which used erasure codes such as Reed Solomon codes only with a view to store data in datacenters. This paper proposes to encrypt the message using DES and to encode the message using Reed Solomon code before storing the message. Storing time is convincingly good in Reed Solomon code when compared with tornado code.
Abstract: Shadows are viewed as undesired information that strongly affects images. Shadows may cause a high risk to present false color tones, to distort the shape of objects, to merge, or to lose objects. This paper proposes a novel approach for the detection and removal of shadows in an image. Firstly the shadow and non shadow region of the original image is identified by HSV color model. The shadow removal is based on exemplar based image inpainting. Finally, the border between the reconstructed shadow and the non shadow areas undergoes bilinear interpolation to yield a smooth transition between them. They would lead to a better fitting of the shadow and non shadow classes, thus resulting in a potentially better reconstruction quality.
Abstract: Accurate segmentation of brain tumor from MRI is crucial in computer aided diagnosis as well as in other medical imaging applications. Brain tumor segmentation is a challenging task due to the diverse appearance of tumor tissues. A variety of brain tumor segmentation techniques have been explored in the literature. Here, a brief review of different brain tumor segmentation techniques has been discussed with their merits and demerits. We conclude with a discussion on the trend of future research in brain tumor segmentation.
Abstract: Compound image is a combination of natural images, text, and graphics.This paper presents a compression technique for improving coding efficiency. The algorithm first decomposes the compound images by using 3 level biorthogonal wavelet transform and then the transformed image was further compressed by Parallel dictionary based LZW algorithm called PDLZW.In PDLZW algorithm instead of using a unique fixed word width dictionary a hierarchical variable word width dictionary set containing several dictionaries of small address space and increases the word widths used for compression and decompression algorithms. The experimental results show that the PSNR value is increased and the Mean Square error value was improved.
Abstract: In this paper an efficient colour based fire pixel segmentation using image processing is proposed. The proposed method adopts rule based colour model due to its less complexity and effectiveness. YCbCr colour space effectively separates luminance from chrominance compared to other colour spaces like RGB and rgb. The proposed method not only separates fire flame pixels but also separates high temperature fire centre pixels by taking in to account of statistical parameter of fire image in YCbCr colour space like standard deviation. The results obtained are compared with other methods in the literature and shows higher fire detection rate and less false detection rate. The proposed method can be used for real time forest fire detection with moving camera.