In random sample consensus (RANSAC), the problem of ellipsoid fitting can be formulated
as a problem of minimization of point-to-model distance, which is realized by maximizing …

The performance of ellipse fitting may significantly degrade in the presence of outliers, which
can be caused by occlusion of the object, mirror reflection or other objects in the process of …

The detailed structure information under the forest canopy is important for forestry surveying.
As a high-precision environmental sensing and measurement method, terrestrial laser …

Universal grasping for a diverse range of objects is a challenging problem in robotics,
especially in the presence of mixed properties with fragile/rigid and heavy/light. Toward …

Fitting ellipses from unrecognized data is a fundamental problem in computer vision and
pattern recognition. Classic least-squares based methods are sensitive to outliers. To …

This paper develops a distributed dictionary learning algorithm for sparse representation of
the data distributed across nodes of sensor networks, where the sensitive or private data are …

In today's world, the medical diagnostic system shows a high reliance on medical imagery
and digital nosology. To facilitate the fast and precise screening of samples, technology is …

Ellipse fitting is a fundamental yet critical task in computer vision, and the development of
robust and accurate algorithms is crucial for various applications. In this study, we propose a …

Ellipse fitting is widely applied in the fields of computer vision and automatic manufacture.
However, the introduced edge point errors (especially outliers) from image edge detection …

Standard approaches for ellipse fitting are based on the minimization of algebraic or
geometric distance between the given data and a template ellipse. When the data are noisy …