There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Matrix Factorization Techniques for Recommender Systems

Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document image the information contents possess various levels of sensitivity. Each level of sensitivity needs different type of protection and this demands multiple watermarking techniques. In this paper, a novel intelligent multiple watermarking techniques are proposed. The sensitivity of the information content of a block is based on the homogeneity and relative energy contribution parameters. Appropriate watermarking scheme is applied based on sensitivity classification of the block. Experiments are conducted exhaustively on documents. Experimental results reveal the accurate identification of the sensitivity of information content in the block. The results reveal that multiple watermarking schemes has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image.

Computer Vision, Graphics, and Image Processing

It is crucial to determine the optimal number of clusters for the clustering quality in cluster analysis. From the standpoint of sample geometry, two concepts, i.e., the sample clustering dispersion degree and the sample clustering synthesis degree, are defined, and a new clustering validity index is designed. Moreover, a method for determining the optimal number of clusters based on an agglomerative hierarchical clustering (AHC) algorithm is proposed. The new index and the method can evaluate the clustering results produced by the AHC and determine the optimal number of clusters for multiple types of datasets, such as linear, manifold, annular, and convex structures. Theoretical research and experimental results indicate the validity and good performance of the proposed index and the method.

Method for Determining the Optimal Number of Clusters Based on Agglomerative Hierarchical Clustering

A new algorithm is proposed for acquiring, processing, analyzing and understanding big image sets using hand gestures. This is based on computer vision through wireless sensing high imagers' techniques. This application is an Image Viewer. Unlike other image viewers application it also have those similar functions (e.g Next, Previous, Zoom IN, Zoom Out), but the specialty of this application is those functions could be control by using simple hand gestures. Here we are going to describe how it's worked as. This application is developed in C++ & OpenCV in Ubuntu 14.04 version.

Kalyani Mali

Papers

A new wireless sensing approach for big images through image viewer control using hand gestures