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

Mingming Huang,Hongbo Huang,Zhichun Mu,Hui Zeng

doi:10.1155/2015/685154

Abstract

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.

Highlights

Interest points are local features for which the signal changes twodimensionally
Standard Scale-invariant feature transform (SIFT) algorithm detects candidate points using scale space extrema in the difference-of-Gaussian filter convolved with the image, D(x, y, σ), described in (3)
We evaluate whether the same physical location in the image under different viewing conditions is detected with the interest point detection algorithm and whether the detected scale in each view overlaps over identical image surfaces around the feature regions in both images, making use of groundtruth data

Summary

Introduction

Interest points (together with the small image patch around them) are local features for which the signal changes twodimensionally. Used interest point detectors include Harris-affine detector and its affine normalization [19, 20], maximally stable extremal regions (MSER) [21], features from accelerated segment test (FAST) [22], and the Hessian-affine detector [19] All of these stateof-the-art interest point detectors have different strengths and weaknesses and yield different number of points depending on the image. Scale-invariant feature transform (SIFT) algorithm proposed by Lowe [5] is one of the most famous and popular interest point detectors and has been proven to be robust in many applications [24]. We apply Bilateral filter and Laplacian filter on SIFT algorithm and present a novel method to detect repeatable interest point with Laplacian-of-Bilateral (LoB) filter.

SIFT Algorithm Review

An Overview of the Proposed Approach

Experiments and Results

Experiment Setup

Conclusions