Interest Points Guided Mesh Simplification

Based on interest point detection, a feature preserving mesh simplification algorithm is proposed. The Harris operator values of all vertices in the mesh were computed firstly. On the base of Garland’s simplification algorithm, we combine the Harris operator value with quadric error metric and change the order of edge collapsing in the simplification. The experimental results show that the proposed algorithm is effective and feature preserving.

Download Full-text

Interest Point Detection Based on Monogenic Signal Theory

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.3451 ◽

2014 ◽

Vol 926-930 ◽

pp. 3451-3454

Author(s):

Li Juan Wang ◽

Chang Sheng Zhang

Keyword(s):

Computer Vision ◽

Signal Theory ◽

Good Precision ◽

Interest Point ◽

Phase Congruency ◽

Interest Points ◽

Monogenic Signal ◽

Modern Computer ◽

Interest Point Detection ◽

Point Detection

A new algorithm is proposed for interest point detection based on monogenic signal theory in this paper. The detection of stable and informative image points is one of the most important problems in modern computer vision. Phase congruency is a dimensionless measure that remains invariant to changes in image illumination and contrast. A monogenic phase congruency function is constructed using the characteristics to detect interest points in image. The experiment results indicate that different kinds of interest points can be detected and located with good precision, thus the proposed method can be applied over wide classes of images.

Download Full-text

A Novel Approach for Interest Point Detection via Laplacian-of-Bilateral Filter

Journal of Sensors ◽

10.1155/2015/685154 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Mingming Huang ◽

Zhichun Mu ◽

Hui Zeng ◽

Hongbo Huang

Keyword(s):

Bilateral Filter ◽

Scale Space ◽

Detection Algorithm ◽

Interest Point ◽

Interest Points ◽

Strong Response ◽

Scale Invariant ◽

Sift Algorithm ◽

Interest Point Detection ◽

Point Detection

Scale-invariant feature transform (SIFT) algorithm, one of the most famous and popular interest point detectors, detects extrema by using difference-of-Gaussian (DoG) filter which is an approximation to the Laplacian-of-Gaussian (LoG) for improving speed. However, DoG filter has a strong response along edge, even if the location along the edge is poorly determined and therefore is unstable to small amounts of noise. In this paper, we propose a novel interest point detection algorithm, which detects scale space extrema by using a Laplacian-of-Bilateral (LoB) filter. The LoB filter, which is produced by Bilateral and Laplacian filter, can preserve edge characteristic by fully utilizing the information of intensity variety. Compared with the SIFT algorithm, our algorithm substantially improves the repeatability of detected interest points on a very challenging benchmark dataset, in which images were generated under different imaging conditions. Extensive experimental results show that the proposed approach is more robust to challenging problems such as illumination and viewpoint changes, especially when encountering large illumination change.

Download Full-text