scholarly journals Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Han Wang ◽  
Zhihuo Xu ◽  
Hanseok Ko
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Coding Method ◽  
Intensity Changes ◽  
Patch Clustering ◽  
Local Patch ◽  
New Space ◽  
New Feature ◽  
Pattern Coding ◽  
Spatial Support

This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes.

scholarly journals A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5105
Author(s):  
Guorong Yu ◽  
Shuangming Zhao
Keyword(s):  
Computational Time ◽  
Feature Descriptor ◽  
Phase Congruency ◽  
Multimodal Image Registration ◽  
Spectral Bands ◽  
Intensity Changes ◽  
New Feature ◽  
Image Pairs ◽  
Invariant Properties ◽  
Minimum Moment

Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.

An Image Matching Algorithm Based on SCCH Feature Descriptor

2011 ◽  
Vol 33 (9) ◽  
pp. 2152-2157 ◽  
Author(s):  
Yong-he Tang ◽  
Huan-zhang Lu ◽  
Mou-fa Hu
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Matching Algorithm

scholarly journals MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0178090 ◽  
Author(s):  
Mingzhe Su ◽  
Yan Ma ◽  
Xiangfen Zhang ◽  
Yan Wang ◽  
Yuping Zhang
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Binary Representation ◽  
Invariant Feature

scholarly journals Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

2019 ◽  
Vol 9 (2) ◽  
pp. 309-314
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Local Features ◽  
Feature Descriptor ◽  
Matching Method ◽  
Multiple Images ◽  
Feature Sets ◽  
Precise Relationship

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

scholarly journals AN INTEGRATED RANSAC AND GRAPH BASED MISMATCH ELIMINATION APPROACH FOR WIDE-BASELINE IMAGE MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 297-300 ◽  
Author(s):  
M. Hasheminasab ◽  
H. Ebadi ◽  
A. Sedaghat
Keyword(s):  
Image Matching ◽  
Graph Transformation ◽  
Feature Point ◽  
Consensus Method ◽  
Feature Descriptor ◽  
Range Image ◽  
Point Sets ◽  
Short Baseline ◽  
Epipolar Constraint ◽  
Baseline Image

In this paper we propose an integrated approach in order to increase the precision of feature point matching. Many different algorithms have been developed as to optimizing the short-baseline image matching while because of illumination differences and viewpoints changes, wide-baseline image matching is so difficult to handle. Fortunately, the recent developments in the automatic extraction of local invariant features make wide-baseline image matching possible. The matching algorithms which are based on local feature similarity principle, using feature descriptor as to establish correspondence between feature point sets. To date, the most remarkable descriptor is the scale-invariant feature transform (SIFT) descriptor , which is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise, and changes in illumination. The epipolar constraint based on RANSAC (random sample consensus) method is a conventional model for mismatch elimination, particularly in computer vision. Because only the distance from the epipolar line is considered, there are a few false matches in the selected matching results based on epipolar geometry and RANSAC. Aguilariu et al. proposed Graph Transformation Matching (GTM) algorithm to remove outliers which has some difficulties when the mismatched points surrounded by the same local neighbor structure. In this study to overcome these limitations, which mentioned above, a new three step matching scheme is presented where the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. Finally, to remove the remained mismatches, based on the adjacent K-NN graph, the GTM is implemented. Four different close range image datasets with changes in viewpoint are utilized to evaluate the performance of the proposed method and the experimental results indicate its robustness and capability.

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