Remote Sensing Image Registration Based on Modified SIFT and Feature Slope Grouping

Chang, Herng-Hua; Wu, Guan-Long; Chiang, Mao-Hsiung

doi:10.1109/lgrs.2019.2899123

Cited by 62 publications

(27 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SIFT features are based on the extrinsic view of the object of interest, independent of the size and rotation of the image [23][24]. It also has a high tolerance for light, noise, and micro-angle changes.…”

Section: B Sift Descriptionmentioning

confidence: 99%

“…The left and right coordinate constraint is that the x coordinate of the target point in the left image is greater than the corresponding x coordinate in the right image [32], and the difference can be expressed as equation (23). The principle of uniqueness constraint: a feature point in the left image, if a matching point exists in the right image, it is unique.…”

Section: Matching Of Fuzzy Feature Points In Video Images Of Mobilmentioning

confidence: 99%

See 1 more Smart Citation

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

View full text Add to dashboard Cite

Extracting the fuzzy feature of the mobile video image can effectively improve the low illumination image quality. Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy. Therefore, this paper proposes a method for extracting fuzzy features of mobile terminal video images based on SURF-based virtual reality technology. First, perform video image grayscale extraction on the input mobile terminal video image, and detect the closed area in the mobile terminal video image as the radiation invariant area of the terminal video image. Secondly, Hessian matrix is used to detect the feature points of the image, and the non-maximum suppression method and interpolation operation are used to find and locate the extreme value points. Then, the main direction of feature points was determined, and SURF description operator was used for matching to obtain initial matching point pairs. Finally, the obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction. The experimental results show that the proposed algorithm is effective in feature extraction and matching, stability and speed. The misrecognition rate of the algorithm in this paper is 0.101, and the time used is only 0.41 s, which fully meets the real-time requirements.

show abstract

Section: B Sift Descriptionmentioning

confidence: 99%

Section: Matching Of Fuzzy Feature Points In Video Images Of Mobilmentioning

confidence: 99%

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The second group contains 80 images, which were randomly selected from two public available data sets respectively [6,52,53]. The first public available dataset contains 107 multispectral and multitemporal remote sensing image pairs with 512 × 512 [6,52], obtained from the United States Geological Survey (USGS) website [54]. The pixel resolution of these images is 1 m. The second public available dataset [54,55] contains 12 classes scene and a total of 1200 images with 256 × 256.…”

Section: Data Setsmentioning

confidence: 99%

Optical Remote Sensing Image Registration Using Spatial-Consistency and Average Regional Information Divergence Minimization via Quantum-Behaved Particle Swarm Optimization

et al. 2020

View full text Add to dashboard Cite

Due to invariance to significant intensity differences, similarity metrics have been widely used as criteria for an area-based method for registering optical remote sensing image. However, for images with large scale and rotation difference, the robustness of similarity metrics can greatly determine the registration accuracy. In addition, area-based methods usually require appropriately selected initial values for registration parameters. This paper presents a registration approach using spatial consistency (SC) and average regional information divergence (ARID), called spatial-consistency and average regional information divergence minimization via quantum-behaved particle swarm optimization (SC-ARID-QPSO) for optical remote sensing images registration. Its key idea minimizes ARID with SC to select an ARID-minimized spatial consistent feature point set. Then, the selected consistent feature set is tuned randomly to generate a set of M registration parameters, which provide initial particle warms to implement QPSO to obtain final optimal registration parameters. The proposed ARID is used as a criterion for the selection of consistent feature set, the generation of initial parameter sets, and fitness functions used by QPSO. The iterative process of QPSO is terminated based on a custom-designed automatic stopping rule. To evaluate the performance of SC-ARID-QPSO, both simulated and real images are used for experiments for validation. In addition, two data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. The experimental results demonstrate that SC-ARID-QPSO produces better registration accuracy and robustness than compared methods.

show abstract

“…Tian Zhang et al [ 9 ] proposed an improved SURF operator by calculating the normalized gray-level difference and second-order gradient in the neighborhood. Herng-Hua Chang et al [ 10 ] improved the Scale Invariant Feature Transform (SIFT) operator, feature slope calculation, feature point grouping, the and outlier removal and transformation were adopted. SK sharma et al [ 11 ] utilized AKAZE to detect feature points, obtained corresponding matching pairs by using K-NN algorithm, and removed the false matched points by MSAC.…”

Section: Introductionmentioning

confidence: 99%

Research on Panoramic Stitching Algorithm of Lateral Cranial Sequence Images in Dental Multifunctional Cone Beam Computed Tomography

Liu

Shen

et al. 2021

Sensors

View full text Add to dashboard Cite

In the design of dental multifunctional Cone Beam Computed Tomography, the linear scanning strategy not only saves equipment cost, but also avoids the demand for patients to be repositioned when acquiring lateral cranial sequence images. In order to obtain panoramic images, we propose a local normalized cross-correlation stitching algorithm based on Gaussian Mixture Model. Firstly, the Block-Matching and 3D filtering algorithm is used to remove quantum and impulse noises according to the characteristics of X-ray images; Then, the segmentation of the irrelevant region and the extraction of the region of interest are performed by Gaussian Mixture Model; The locally normalized cross-relation is used to complete the registration with the multi-resolution strategy based on wavelet transform and Particle Swarm Optimization algorithm; Finally, image fusion is achieved by the weighted smoothing fusion algorithm. The experimental results show that the panoramic image obtained by this method has significant performance in both subjective vision and objective quality evaluation and can be applied to preoperative diagnosis of clinical dental deformity and postoperative effect evaluation.

show abstract

Remote Sensing Image Registration Based on Modified SIFT and Feature Slope Grouping

Cited by 62 publications

References 14 publications

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Optical Remote Sensing Image Registration Using Spatial-Consistency and Average Regional Information Divergence Minimization via Quantum-Behaved Particle Swarm Optimization

Research on Panoramic Stitching Algorithm of Lateral Cranial Sequence Images in Dental Multifunctional Cone Beam Computed Tomography

Contact Info

Product

Resources

About