Method for Fused Phase and PCA Direction Based on a SIFT Framework for Multi-Modal Image Matching

Abstract

The high noise and local deformation of multi-modal images reduce the accuracy of scale invariant feature transform (SIFT) image matching. To solve this problem, a new method based on the SIFT framework, which fuses the phase consistency optimization strategy and the gradient direction of principal component analysis (PCA) with the 8 direction of latitude reduction, is proposed in this paper. This method fuses the histogram of the orientated phase congruency (HOPC) method to extract the direction of the image, and adopts PCA to extract the main direction, which effectively solves the problem that the matching accuracy decreases due to the inversion of the direction of the image. Using the image phase instead of gradient intensity, the difficult problem of direction extraction is effectively solved when image edge characteristics are not obvious. Finally, the random sample consistency (RANSAC) algorithm is used to eliminate false match points. Simulation and experiments show that compared with the SIFT algorithm and PCA-SIFT algorithm, the proposed method improved the number of match points and matching accuracy, significantly reduced the mismatching rate. The statistical results show that the number of match points raised in this paper increases by 20.1% and 200% respectively compared with the former two algorithms.

Extraction and Reuse of Visual Elements of Tourism Cultural and Creative Products Based on Deep Learning

“…Scene graph is a method of arranging data into hierarchical structure, and scene graph generation is based on scene graph for multi-modal input data such as audio, video, images and paper-cut patterns, and automatically converts unstructured semantic information in the input data into structured triple information. For paper-cut pattern matching and recognition, color is secondary, other information is needed, and the speed of binary image operation is fast, so the pattern image is binarized [11].…”

Nghiên cứu tiếp cận học sâu ứng dụng trong quản lý lớp học hiệu quả ở Trường Đại học Đông Á

“…Để giải quyết vấn đề này, nhóm tác giả H. Liu et al (2019) trong nghiên cứu đề xuất một phương pháp mới dựa trên khung SIFT, kết hợp chiến lược tối ưu hóa nhất quán pha và hướng gradient của phân tích thành phần chính (PCA) với tám hướng giảm vĩ độ.…”

A Novel Approach Extracting Extreme Points Combining Multidirection Phase Superposition and Weighted Moment Diagram for Matching Multi-Modal Images

“…PSWMD calculated the structural direction by using the method presented in the paper [9], which projects the filtered results to the horizontal and vertical directions respectively to obtain the energy in both directions. The calculation formula [15] of the structure method is as follows: 2) Extracting principal directions The principal direction of an extreme point is the direction of the most drastic structural change.…”

“…In [8], the method of HOPC applied phase congruency in the template muti-modal image matching. In [9], the HOPC theory has been further improved and the directional reverse problem of template matching was solved. Because of the complicated computation, when the size of the image enlarges constantly, the searching space for template matching increases exponentially compared to the full-pixel images, magnifying the calculation amount.…”