A Local Feature Descriptor Based on Combination of Structure and Texture Information for Multispectral Image Matching

Fu, Zhiyao; Qin, Qianqing; Wu, Chun; Sun, Hui

doi:10.1109/lgrs.2018.2867635

Cited by 29 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use log-Gabor filters and named the descriptor as Multispectral Feature Descriptor (MFD). Similarly, Sobel filters are used in four different directions to compute Histograms of Directional Maps (HoDM) descriptor [25]. Binary descriptors such as Oriented FAST and Rotated BRIEF (ORB) [26] and Binary Robust Invariant Scalable Keypoints (BRISK) [27] are also used to overcome intensity changes between the images.…”

Section: Related Workmentioning

confidence: 99%

Visible Spectrum and Infra-Red Image Matching: A New Method

Saleem

Bais

2020

Applied Sciences

View full text Add to dashboard Cite

Textural and intensity changes between Visible Spectrum (VS) and Infra-Red (IR) images degrade the performance of feature points. We propose a new method based on a regression technique to overcome this problem. The proposed method consists of three main steps. In the first step, feature points are detected from VS-IR images and Modified Normalized (MN)-Scale Invariant Feature Transform (SIFT) descriptors are computed. In the second step, correct MN-SIFT descriptor matches are identified between VS-IR images with projection error. A regression model is trained on correct MN-SIFT descriptors. In the third step, the regression model is used to process the MN-SIFT descriptors of test VS images in order to remove misalignment with the MN-SIFT descriptors of test IR images and to overcome textural and intensity changes. Experiments are performed on two different VS-IR image datasets. The experimental results show that the proposed method works really well and demonstrates on average 14% and 15% better precision and matching scores compared to recently proposed Histograms of Directional Maps (HoDM) descriptor.

show abstract

Section: Related Workmentioning

confidence: 99%

Visible Spectrum and Infra-Red Image Matching: A New Method

Saleem

Bais

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, it has been shown that it is difficult to select an appropriate threshold for the EOH descriptor, in order to ensure that the edges extracted from multispectral images are similar [48]. Thus, Fu et al advocated a local feature descriptor with a combination of structural and textural information for multispectral image matching, which is perfect for the non-linear intensity changes of multispectral images [50].…”

Section: Related Workmentioning

confidence: 99%

Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Liu

Pan

2019

Sensors

View full text Add to dashboard Cite

Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.

show abstract

“…Edge-based methods establish feature descriptions by the image edge orientation according to the consistency of edge structures of infrared and visible images [20]. To compute the edge orientation, multi-oriented Sobel spatial filters are introduced in EOH (Edge Oriented Histogram) [27], PCEHD (Phase Congruency Edge Histogram Descriptor) [28], HoDMs (Histogram of Directional Maps) [29], and HOSM (Histogram of Oriented Structure Maps) [30]. EOH calculates edge orientation responses by multi-oriented Sobel spatial filters and use the orientation corresponding to the maximum response as the edge orientation.…”

Section: Introductionmentioning

confidence: 99%

Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

et al. 2020

View full text Add to dashboard Cite

Infrared and visible image match is an important research topic in the field of multi-modality image processing. Due to the difference of image contents like pixel intensities and gradients caused by disparate spectrums, it is a great challenge for infrared and visible image match in terms of the detection repeatability and the matching accuracy. To improve the matching performance, a feature detection and description method based on consistent edge structures of images (DDCE) is proposed in this paper. First, consistent edge structures are detected to obtain similar contents of infrared and visible images. Second, common feature points of infrared and visible images are extracted based on the consistent edge structures. Third, feature descriptions are established according to the edge structure attributes including edge length and edge orientation. Lastly, feature correspondences are calculated according to the distance of feature descriptions. Due to the utilization of consistent edge structures of infrared and visible images, the proposed DDCE method can improve the detection repeatability and the matching accuracy. DDCE is evaluated on two public datasets and are compared with several state-of-the-art methods. Experimental results demonstrate that DDCE can achieve superior performance against other methods for infrared and visible image match.

show abstract

A Local Feature Descriptor Based on Combination of Structure and Texture Information for Multispectral Image Matching

Cited by 29 publications

References 13 publications

Visible Spectrum and Infra-Red Image Matching: A New Method

Visible Spectrum and Infra-Red Image Matching: A New Method

Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

Contact Info

Product

Resources

About