Digital Color Image Watermarking using DWT-DCT Coefficients in RGB Planes

Digital image watermarking is used to identify the authenticity and integrity and to show the identity of its owners. This paper presents a more secure method for copy right protection. In the proposed method, the color image is decomposed into 3 color channels Red, Green and Blue and then DWT and DCT are applied to B channel of the R, G and B channels. The colored Watermark image is decomposed into R, G, B channels and DCT is applied to all the channels separately. R, G, B channels of watermark image are embedded into mid frequency coefficients of B channel already selected. The performance of proposed algorithm is measured by using Mean Square Error, Peak Signal to Noise Ratio, Standardized Correlation and Normalized Correlation. A comparative study of proposed scheme with the existing methods which uses DWT-DCT transforms is carried here and results shown

An automatic and efficient foreground object extraction scheme

This paper presents a method to differentiate the foreground objects from the background of a color image. Firstly a color image of any size is input for processing. The algorithm converts it to a grayscale image. Next we apply canny edge detector to find the boundary of the foreground object. We concentrate to find the maximum distance between each boundary pixel column wise and row wise and we fill the region that is bound by the edges. Thus we are able to extract the grayscale values of pixels that are in the bounded region and convert the grayscale image back to original color image containing only the foreground object

Color Image Clustering using Block Truncation Algorithm

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters

Color Intensity Projections: A simple way to display changes in astronomical images

To detect changes in repeated astronomical images of the same field of view (FOV), a common practice is to stroboscopically switch between the images. Using this method, objects that are changing in location or intensity between images are easier to see because they are constantly changing. A novel display method, called arrival time color intensity projections (CIPs), is presented that combines any number of grayscale images into a single color image on a pixel by pixel basis. Any values that are unchanged over the grayscale images look the same in the color image. However, pixels that change over the grayscale image have a color saturation that increases with the amount of change and a hue that corresponds to the timing of the changes. Thus objects moving in the grayscale images change from red to green to blue as they move across the color image. Consequently, moving objects are easier to detect and assess on the color image than on the grayscale images. A sequence of images of a comet plunging into the sun taken by the SOHO satellite (NASA/ESA) and Hubble Space Telescope images of a trans-Neptunian object (TNO) are used to demonstrate the method.Comment: 9 pages, 2 figures. Accepted for publication in Publications of the Astronomical Society of the Pacific. The quality of figure 1 been improved from the previous posted versio

Advancement in Color Image Processing using Geometric Algebra

This paper describes an advancement in color image processing, using geometric algebra. This is achieved using a compact representation of vectors within $n$ dimensional space. Geometric Algebra (GA) is a preferred framework for signal representation and image representation. In this context the R, G, B color channels are not defined separately but as a single entity. As GA provides a rich set of operations, the signal and image processing operations becomes straightforward and the algorithms intuitive. From the experiments described in this paper, it is also possible to conclude that the convolution operation with the rotor masks within GA belong to a class of linear vector filters and can be applied to image or speech signals. The usefulness of the introduced approach has been demonstrated by analyzing and implementing two different types of edge detection schemes