Keypoint Matching Research Articles

Color correction and geometric calibration for multi-view images with feature correspondence

Color and geometry inconsistency between different views is an urgent problem in multi-view imaging applications. In this paper, we present a color correction and geometric calibration method for multi-view images on the basis of feature correspondences between views. First, keypoints in views are detected by using scale invariant feature transform, and accurately matched by bi-directional feature matching between difference views. Then multiplicative and additive errors between matching keypoints are calculated to achieve color correction. In addition, an affine transformation between minimum cost matching keypoints is established to achieve geometric calibration. The experimental results verify the effectiveness of the proposed method in color correction and geometric calibration, and a higher coding efficiency is obtained.

Robust Scale-Invariant Feature Matching for Remote Sensing Image Registration

When the scale-invariant feature transform (SIFT) is adopted in the registration of remote sensing images, a lot of incorrect matches of keypoints will appear owing to the significant difference in the image intensity between remote sensing images compared to visible images. Scale-orientation joint restriction criteria are proposed to achieve robust feature matching for keypoints in remote sensing images. Moreover, the feature descriptor of each keypoint is also refined to overcome the difference in the gradient intensity and orientation between remote image pairs. Experimental results for multidate, multispectral, and multisensor remote images indicate that the proposed method improves the match performance compared to intensity- and SIFT-based methods in terms of correct-match rate and aligning accuracy.

Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection

Near-duplicate (ND) detection appears as a timely issue recently, being regarded as a powerful tool for various emerging applications. In the Web 2.0 environment particularly, the identification of near-duplicates enables the tasks such as copyright enforcement, news topic tracking, image and video search. In this paper, we describe an algorithm, namely Scale-Rotation invariant Pattern Entropy (SR-PE), for the detection of near-duplicates in large-scale video corpus. SR-PE is a novel pattern evaluation technique capable of measuring the spatial regularity of matching patterns formed by local keypoints. More importantly, the coherency of patterns and the perception of visual similarity, under the scenario that there could be multiple ND regions undergone arbitrary transformations, respectively, are carefully addressed through entropy measure. To demonstrate our work in large-scale dataset, a practical framework composed of three components: bag-of-words representation, local keypoint matching and SR-PE evaluation, is also proposed for the rapid detection of near-duplicates.

基于偏最小二乘方法的谱特征匹配方法

We investigate the spectral approaches to the problem of point pattern matching, and present a spectral feature descriptors based on partial least square (PLS). Given keypoints of two images, we define the position similarity matrices respectively, and extract the spectral features from the matrices by PLS, which indicate geometric distribution and inner relationships of the keypoints. Then the keypoints matching is done by bipartite graph matching. The experiments on both synthetic and real-world data corroborate the robustness and invariance of the algorithm.

Improvement in the Accuracy of Matching by Different Feature Subspaces in Traffic Sign Recognition

A technique for recognizing traffic signs from an image taken with an in-vehicle camera has already been proposed as driver's drive assist. SIFT feature is used for traffic sign recognition, because it is robust to changes in scaling and rotating of the traffic sign. However, it is difficult to process in real-time because the computation cost of the SIFT feature extraction and matching is expensive. This paper presents a method of traffic sign recognition based on keypoint classifier by AdaBoost using PCA-SIFT features in different feature subspaces. Each subspace is constructed from gradients of traffic sign images and general images respectively. A detected keypoint is projected to both subspaces, and then the AdaBoost employs to classy into whether the keypoint is on the traffic sign or not. Experimental results show that the computation cost for keypoint matching can be reduced to about 1/2 compared with the conventional method.

Fast phase-based registration of multimodal image data

An interesting problem in pattern recognition is that of image registration, which plays an important role in many vision-based recognition and motion analysis applications. Of particular interest among registration problems are multimodal registration problems, where the images exist in different feature spaces. State-of-the-art phased-based approaches to multimodal image registration methods have provided good accuracy but have high computational cost. This paper presents a fast phase-based approach to registering multimodal images for the purpose of initial coarse-grained registration. This is accomplished by simultaneously performing both globally exhaustive dynamic phase sub-cloud matching and polynomial feature space transformation estimation in the frequency domain using the fast Fourier transform (FFT). A multiscale phase-based feature extraction method is proposed that determines both the location and size of the dynamic sub-clouds being extracted. A simple outlier pruning based on resampling is used to remove false keypoint matches. The proposed phase-based approach to registration can be performed very efficiently without the need for initial estimates or equivalent keypoints from both images. Experimental results show that the proposed method can provide accuracies comparable to the state-of-the-art phase-based image registration methods for the purpose of initial coarse-grained registration while being much faster to compute.

Shot Change Detection via Local Keypoint Matching

Shot change detection is an essential step in video content analysis. However, automatic shot change detection often suffers from high false detection rates due to camera or object movements. To solve this problem, we propose an approach based on local keypoint matching of video frames. This approach aims to detect both abrupt and gradual transitions between shots without modeling different kinds of transitions. Our experiment results show that the proposed algorithm is effective for most kinds of shot changes.

Multi-spectral remote image registration based on SIFT

The number of incorrect matches of SIFT keypoints will sharply increase when the SIFT-based registration algorithm is adopted for pairs of multi-spectral remote images, owing to the significant difference in the image intensity between multi-spectral images. Scale restriction criteria for keypoint matching are proposed, and experimental results demonstrate that it will greatly improve match performance.

Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm, including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8% of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.

Keypoint recognition using randomized trees

In many 3D object-detection and pose-estimation problems, runtime performance is of critical importance. However, there usually is time to train the system, which we will show to be very useful. Assuming that several registered images of the target object are available, we developed a keypoint-based approach that is effective in this context by formulating wide-baseline matching of keypoints extracted from the input images to those found in the model images as a classification problem. This shifts much of the computational burden to a training phase, without sacrificing recognition performance. As a result, the resulting algorithm is robust, accurate, and fast-enough for frame-rate performance. This reduction in runtime computational complexity is our first contribution. Our second contribution is to show that, in this context, a simple and fast keypoint detector suffices to support detection and tracking even under large perspective and scale variations. While earlier methods require a detector that can be expected to produce very repeatable results, in general, which usually is very time-consuming, we simply find the most repeatable object keypoints for the specific target object during the training phase. We have incorporated these ideas into a real-time system that detects planar, nonplanar, and deformable objects. It then estimates the pose of the rigid ones and the deformations of the others.