Interest Point Detection Based on Monogenic Signal Theory

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.

Automated Design of Image Operators that Detect Interest Points

2008 ◽  
Vol 16 (4) ◽  
pp. 483-507 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague
Keyword(s):  
Computer Vision ◽  
Search Space ◽  
Search Problem ◽  
Interest Points ◽  
Fitness Evaluation ◽  
Modern Computer ◽  
New Perspective ◽  
Processing Techniques ◽  
Point Detection ◽  
Optimization Search

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

Interest Points Guided Mesh Simplification

2012 ◽  
Vol 263-266 ◽  
pp. 2320-2323 ◽  
Author(s):  
Ying Gao ◽  
Rui Zhao Wang ◽  
Jue Yuan
Keyword(s):  
Mesh Simplification ◽  
Experimental Results ◽  
Interest Point ◽  
Interest Points ◽  
Interest Point Detection ◽  
Feature Preserving ◽  
Error Metric ◽  
Point Detection ◽  
Quadric Error Metric

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.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
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.

scholarly journals Interest Point Detection A Computer Vision Approach

2013 ◽  
Vol 75 (17) ◽  
pp. 52-55
Author(s):  
Diptam Dutta ◽  
Priyanka Mukherjee ◽  
Sandeep Kumar Jha
Keyword(s):  
Computer Vision ◽  
Interest Point ◽  
Interest Point Detection ◽  
Point Detection

scholarly journals Spine Localization in X-ray Images Using Interest Point Detection

2008 ◽  
Vol 22 (3) ◽  
pp. 309-318 ◽  
Author(s):  
Mohammed Benjelloun ◽  
Saïd Mahmoudi
Keyword(s):  
Interest Point ◽  
X Ray ◽  
Interest Point Detection ◽  
Point Detection
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