Abstract Ground filtering is an essential step in the comprehensive processing of airborne LiDAR point clouds. However, performances existing ground algorithms are usually affected by manual thresholds, and many have high complexity not suitable for applications with timeliness requirements. In this paper, a fast algorithm clouds based on Random Sample Consensus (RANSAC) adaptive threshold acqu...

RANdom SAmple Consensus (RANSAC) is widely used in computer vision and automotive related applications. It an iterative method to estimate parameters of mathematical model from a set observed data that contains outliers. In vision, such usually features (such as feature points, line segments) extracted images. applications, RANSAC can be lane vanishing point, camera rotation angles, ground plan...

RANSAC (Random Sample Consensus) [2] is a popular algorithm in computer vision for fitting a model to data points contaminated with many gross outliers. Traditionally many small hypothesis sets are chosen randomly; these are used to generate models and the model consistent with most data points is selected. Instead we propose that each hypothesis set chosen is the one most likely to be correct,...

In the landmark-based localization problem, movement and ambiguity of landmarks and imperfect identification process make measurements of the landmarks completely different from its true value. The incorrect measured data have degraded existing localization methods in the practical applications. This paper proposes a framework to improve accuracy of the existing landmark-based localization meth...

In this paper we show how based on a number of different techniques it is possible to fully automatically generate basic ingredients for high quality visualizations of urban areas characterized by vertical facade planes from nothing but uncalibrated wide-baseline image sequences without using any markers or ground control. At the core of our algorithms are least-squares matching, projective geo...

As Light Detection And Ranging (LiDAR) (point) data sets increase in resolution, earth scientists become more interested in detecting and delineating trees using LiDAR. The majority of conventional methods which detect and delineate trees convert point data into gridded surfaces. Unfortunately, this conversion process has the potential to introduce error. We improve a point-based geometric mode...

The ability for an autonomous agent to selflocalise is directly proportional to the accuracy and precision with which it can perceive salient features within its local environment. The identification of such features by recognising geometric profile allows robustness against lighting variations, which is necessary in most industrial robotics applications. This paper details a framework by which...

The field of computer vision is undergoing tremendous development in recent years. Computer vision concerns with developing systems that can interpret the content of natural scenes. Robust statistical methods were first adopted in computer vision to improve the performance of feature extraction algorithms at the bottom level of the vision hierarchy. These methods tolerate the presence of data p...

We propose a method to determine the light direction and diffuse reflectance property from two images with different light conditions. In our method, it is assumed that the shape of the target object is given. Using the relationships between light direction and diffuse reflectance, we can estimate both of them simultaneously from more than five points on two images. While speculars and shadows ...

Sub-pixel accuracy is the vital requirement of remote sensing optical image registration. For this purpose, a coarse-to-fine registration algorithm is proposed to register the remote sensing optical images. The coarse registration operation is performed by the scale-invariant feature transform (SIFT) approach with an outlier removal method. The outliers are removed by the Random sample consensu...