We study a robust version of local linear regression smoothers augmented with variable bandwidth. The proposed method inherits the advantages of local polynomial regression and overcomes lack of robustness of least-squares techniques. The use of variable bandwidth enhances the exibility of the resulting local M-estimators and makes them possible to cope well with spatially inhomogeneous curves,...

A robust, iterative approach is introduced for finding the dominant plane in a scene using stereo vision. Neither camera calibration nor stereo correspondence is required. Iterative two-step methods have been applied to various problems in computer vision, but only recently Cohen has formalized a framework that guarantees (at least local) convergence of such methods. In this paper we adopt this...

In this paper, we present an efficient approach for parameterizing a genus-zero triangular mesh onto the sphere with an optimal radius in an as-rigid-as-possible (ARAP) manner, which is an extension of planar ARAP parametrization approach to spherical domain. We analyze the smooth and discrete ARAP energy and formulate our spherical parametrization energy from the discrete ARAP energy. The solu...

Proc. Computer Vision and Pattern Recognition (CVPR’97), pp. 508-513, June 17-19, 1997, San Juan, Puerto Rico A robust, iterative approach is introduced for finding the dominant plane in a scene using binocular vision. Neither camera calibration nor stereo correspondence is required. Recently Cohen formalized a framework guaranteeing (local) convergence of iterative two-step methods. In this pa...

This paper proposes a multi-step iterative method for solving systems of nonlinear equations with a local convergence order of 3m − 4, wherem (≥2) is the number of steps. The multi-step iterative method includes two parts: the base method and the multi-step part. The base method involves two function evaluations, two Jacobian evaluations, one LU decomposition of a Jacobian, and twomatrix–vector...

This paper introduces a fast image mosaicing technique that does not require costly search on image domain (e.g., pixel-to-pixel correspondence search on the image domain) and the iterative optimization (e.g., gradient-based optimization, iterative optimization, and random optimization) of geometric transformation parameter. The proposed technique is organized in a two-step manner. At both step...

In this paper, in order to solve systems of nonlinear equations, a new class frozen Jacobian multi-step iterative methods is presented. Our proposed algorithms are characterized by highly convergent and an excellent efficiency index. The theoretical analysis presented detail. Finally, numerical experiments for showing the performance methods, when compared with known taken from literature.

Motivated by its broad applications in machine learning and reinforcement learning, we study the linear two-time-scale stochastic approximation, an iterative method using two different step sizes f...

In this paper, the finite-time output consensus problem ofmulti-agent systems is considered by using the iterative learning control (ILC) approach. Two classes of distributed protocols are constructed from the two-dimensional system point of view (with time step and iteration number as independent variables), and are termed as iterative learning protocols. If learning gains are chosen appropria...