Statistical Optimization for Geometric Fitting: Theoretical Accuracy Bound and High Order Error Analysis
Abstract
A rigorous accuracy analysis is given to various techniques for estimating parameters of geometric models from noisy data for computer vision applications. First, it is pointed out that parameter estimation for vision applications is very different in nature from traditional statistical analysis and hence a different mathematical framework is necessary in such a domain. After general theories on estimation and accuracy are given, typical existing techniques are selected, and their accuracy is evaluated up to higher order terms. This leads to a “hyperaccurate” method that outperforms existing methods.
Thursday, August 5, 2010
Fast Projective Reconstruction: Toward Ultimate Efficiency
Fast Projective Reconstruction: Toward Ultimate Efficiency
Abstract
We accelerate the time-consuming iterations for projective reconstruction, a key component of self-calibration for 3-D reconstruction from a video sequence. We first introduce the power method for eigenvalue computation. We then accelerate the power method itself. We also introduce SOR for further acceleration. Using simulated and real video images, we demonstrate that our techniques dramatically speed up the computation, in some cases about 8,000 times faster.
Abstract
We accelerate the time-consuming iterations for projective reconstruction, a key component of self-calibration for 3-D reconstruction from a video sequence. We first introduce the power method for eigenvalue computation. We then accelerate the power method itself. We also introduce SOR for further acceleration. Using simulated and real video images, we demonstrate that our techniques dramatically speed up the computation, in some cases about 8,000 times faster.
Sunday, August 1, 2010
APPLICATION OF MACHINE LEARNING & ROBOTICS
APPLICATION OF MACHINE LEARNING & ROBOTICS
ABSTRACT
Machine Learning is a branch of Artificial Intelligence and is gaining importance. If Machine Learning is applied to robots then they can learn by themselves and thus help to reduce the tedious task of monitoring, programming and also will minimize the overhead charges.
The paper takes into consideration the introduction of Machine Learning and its applications to robotics with a case study.
The paper briefly explains the method of learning with a case study of system able of performing complex manipulation task. A manipulation system that can observe dynamic changes in the environment in real time. This consists of a grasping system that uses a high-speed visual and force feedback technique having a multi-fingered hand-arm with a hierarchical parallel processing system and a high-speed vision system SPE-256 and Grasping with high responsiveness and adaptive ness to dynamic changes in the environment is realized by Machine Learning.
ABSTRACT
Machine Learning is a branch of Artificial Intelligence and is gaining importance. If Machine Learning is applied to robots then they can learn by themselves and thus help to reduce the tedious task of monitoring, programming and also will minimize the overhead charges.
The paper takes into consideration the introduction of Machine Learning and its applications to robotics with a case study.
The paper briefly explains the method of learning with a case study of system able of performing complex manipulation task. A manipulation system that can observe dynamic changes in the environment in real time. This consists of a grasping system that uses a high-speed visual and force feedback technique having a multi-fingered hand-arm with a hierarchical parallel processing system and a high-speed vision system SPE-256 and Grasping with high responsiveness and adaptive ness to dynamic changes in the environment is realized by Machine Learning.
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