Sanjeev Kumar

Bio: Sanjeev Kumar is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Encryption & Optical flow. The author has an hindex of 11, co-authored 83 publications receiving 457 citations. Previous affiliations of Sanjeev Kumar include Dharamsinh Desai University & University of Udine.

A novel fractional order chaos-based image encryption using Fisher Yates algorithm and 3-D cat map

Abstract: This paper presents a novel image encryption scheme using 3-D Arnold cat map and Fisher-Yates shuffling algorithm. A plain image is divided into various slices of equal size and then the 3-D representation of the image is shuffled by the 3-D chaotic map. A fractional order system of nonlinear differential equations is used to implement the diffusion in the intensity values of the shuffled image pixels. The solution of this fractional order system develops a strange attractor which is the onset of the chaos. Fisher-Yates is used to make a chaotic matrix which is used for arranging the data points. Experimental results are given on various images with comprehensive analysis which demonstrates the high security and sensitivity of the scheme.

An optimization approach to solve the inverse kinematics of redundant manipulator

Abstract: In this paper, we propose an optimization based approach to solve inverse kinematics problem by converting it into a nonlinear optimization prob- lem. An improved energy function is defined to solve the optimization problem even in the case when the matrix associated with objective function is not pos- itive definite. The stability analysis has been done for the proposed algorithm using Lyapunov method. The result has been illustrated through simulation of inverse kinematics solution for a seven arm redundant manipulator.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Corporate citizenship as a marketing instrument: concepts, evidence and research directions

Abstract: Confronted with increasing pressures to limit government spending on social welfare, more and more public policy makers welcome the growing social involvement of corporations. Yet, inasmuch as corporate citizenship may be desirable for society as a whole, it is unlikely to be embraced by a large number of organizations unless it is associated with concrete business benefits. This paper presents past findings and proposes future research directions useful for understanding the potential value of corporate citizenship as a marketing tool. Specifically, after examining the nature of corporate citizenship, the paper discusses its potential impact, first on consumers, then on employees. Two conceptual frameworks are introduced to guide research on the value of corporate citizenship in terms of external and internal marketing respectively.

What Is the Range of Surface Reconstructions from a Gradient Field

Abstract: We propose a generalized equation to represent a continuum of surface reconstruction solutions of a given non-integrable gradient field. We show that common approaches such as Poisson solver and Frankot-Chellappa algorithm are special cases of this generalized equation. For a N x N pixel grid, the subspace of all integrable gradient fields is of dimension N 2 - 1. Our framework can be applied to derive a range of meaningful surface reconstructions from this high dimensional space. The key observation is that the range of solutions is related to the degree of anisotropy in applying weights to the gradients in the integration process. While common approaches use isotropic weights, we show that by using a progression of spatially varying anisotropic weights, we can achieve significant improvement in reconstructions. We propose (a) α-surfaces using binary weights, where the parameter a allows trade off between smoothness and robustness, (b) M-estimators and edge preserving regularization using continuous weights and (c) Diffusion using affine transformation of gradients. We provide results on photometric stereo, compare with previous approaches and show that anisotropic treatment discounts noise while recovering salient features in reconstructions.