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For the purpose of improving the real-time performance of the SIFT algorithm, an image matching algorithm based on SUSAN-SIFT algorithm is proposed in this paper. Use SUSAN algorithm in the detecting feature points part of the SIFT algorithm, avoiding the time-consuming down-sampling and Gaussian convolution of the algorithm, and remove the feature points of the unstable and low-contrast by the methods of interpolated estimate and the principal curvatures, and then use the SIFT algorithm to achieve the parts of describing the feature points and images matching. experimental verification: the SUSAN-SIFT algorithm has more fast calculation speed than SIFT algorithm ensuring the accuracy at the same time.

Image matching plays an important role in computer vision. The features extracted by SIFT algorithm have high stability invariant to scale, rotation and light, so it is the most popular algorithm for image matching. While SIFT algorithm also has its disadvantages of high dimensional data and time-consuming. To solve this problem, the traditional method employs PCA algorithm to reduce dimensionality of the descriptors. While PCA is a linear dimensionality reduction algorithm which means that it can only be used for linear distributed data. This paper employs the fuzzy K-means algorithm to improve it (referred to as FKPCA) and improved RANSAC algorithm to eliminate false matching points after matching with PCA-SIFT and FKPCA-SIFT. From the experimental results, compared with PCA-SIFT algorithm, it can be seen that FKPCA-SIFT can keep the high matching accuracy for image. Moreover, FKPCA-SIFT can also be applied to non-linear data to expand the scope of PCA-SIFT and provides a better reference platform for further research.

Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved feature-point pair purification algorithm based on SIFT (Scale invariant feature transform) is proposed. Firstly, the K-nearest neighbor-based feature point matching algorithm is used for rough matching. Then RANSAC (Random Sample Consensus) method is used for robustness tests to eliminate mismatched point pairs. The mismatching rate is greatly reduced by combining the two methods. Then, the image transformation matrix is estimated, and the image is determined. The seamless mosaic of endoscopic images is completed by matching the relationship. Finally, the proposed algorithm is verified by real endoscopic image and has a good effect.

The Matching and registration of the satellite imagery play an essential role in many remote sensing and image processing applications. Certain steps, such as analysis of broad regions of interest and for the remotely detection. Pixel values in two or more images were play an important role in matching and detection algorithms between two or more images. In this work an image registration process (detection, extract and matching features) were utilized through using the sift algorithm to registration two image that have different resolutions (Landsat (30m) and Sentinel (10m)) utilize MATLAB program. A geometric correction was applied in this work for detect a correct points between these images. The results shows a corresponding between these two images. A preprocessing were utilized in good identifying features and accuracy of extracting information from satellite images.

This paper applies traditional cultural elements to modern interior design and first analyzes the expression types of traditional cultural elements. Secondly, the color and pattern elements in traditional cultural elements are extracted by color matrix and SIFT algorithm, and the pattern and color elements are applied to modern interior design using an intelligent recommendation algorithm. Next, the layout elements of traditional cultural elements are modeled, and the automatic layout algorithm is created to be utilized in modern interior design. Finally, the performance of extracting traditional cultural elements and their application are analyzed. The results show that the recognition rate of various colors is above 0.88, the extraction accuracy of the overall traditional cultural pattern elements is around 0.9, the overall degree of the graphic color element recommendation is greater than 0.88, the hierarchical difference between the traditional cultural layout and the intelligent layout is between [0.001, 0.01], and the functional difference is between (0, 0.02], and all of the automatic layout algorithms can be completed within 0.2s.

Multi-sensor integrated navigation technology has been applied to the indoor navigation and positioning of robots. For the problems of a low navigation accuracy and error accumulation, for mobile robots with a single sensor, an indoor mobile robot positioning method based on a visual and inertial sensor combination is presented in this paper. First, the visual sensor (Kinect) is used to obtain the color image and the depth image, and feature matching is performed by the improved scale-invariant feature transform (SIFT) algorithm. Then, the absolute orientation algorithm is used to calculate the rotation matrix and translation vector of a robot in two consecutive frames of images. An inertial measurement unit (IMU) has the advantages of high frequency updating and rapid, accurate positioning, and can compensate for the Kinect speed and lack of precision. Three-dimensional data, such as acceleration, angular velocity, magnetic field strength, and temperature data, can be obtained in real-time with an IMU. The data obtained by the visual sensor is loosely combined with that obtained by the IMU, that is, the differences in the positions and attitudes of the two sensor outputs are optimally combined by the adaptive fade-out extended Kalman filter to estimate the errors. Finally, several experiments show that this method can significantly improve the accuracy of the indoor positioning of the mobile robots based on the visual and inertial sensors.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Medical image alignment is an important research field in medical image processing, which is widely used in clinical diagnosis and treatment, such as surgical navigation, lesion tracking, and treatment evaluation. In this paper, an improved algorithm combining the Demons algorithm and SIFT algorithm is proposed, which uses the SIFT algorithm to represent the feature points in non-rigid medical images as a scale space sequence and normalize the descriptors in the scale space sequence. Then, the two-way alignment strategy and multi-resolution strategy are introduced to improve the accuracy of Demons algorithm in the alignment of non-rigid medical images with complex deformation. The study shows that the improved Demons algorithm can achieve better alignment results when the weights of the feature matching terms are taken as −1 and 1, which makes the improved Demons algorithm with the addition of SIFT feature terms perform optimally. Alignment simulation experiments found that the MSE value of this paper’s improved algorithm is only 0.077. The alignment effect of non-rigid medical images is much better than the comparison algorithm and can maintain a shorter running time. The algorithm in this paper can effectively realize the non-rigid alignment of medical images, which provides a reference method for medical diagnosis and the effective formulation of treatment plans.

AIGC is currently a hot field and a future trend in AI applications, and addressing the challenge of digitally reconstructing cultural heritage under the influence of AI technology is a pressing issue that requires immediate resolution. The article proposes an application framework for AIGC technology that is based on refining its meaning and designing a specific process for applying it to the digital reconstruction of cultural heritage. A high-definition camera is used to acquire relevant images of cultural heritage. The image features are extracted by the SIFT algorithm optimized by the PROSAC algorithm. The color features are acquired by combining the color histogram, color moment, and color correlation diagram. The 3D laser scanning technology is used to obtain the 3D point cloud data of the cultural heritage; the KD-tree improved ICP algorithm is introduced to improve the efficiency of point cloud alignment; the dense reconstruction of the 3D point cloud data of the cultural heritage is realized based on CMVS/PMVS; and the immersive 3D experience system of the cultural heritage is constructed by combining with platforms such as Unity3D. The average matching rate of the optimized SITF algorithm to the image features of cultural heritage is about 74.91%, and the maximum alignment time of the ICP algorithm to the cultural heritage point cloud data based on KD-tree is 9.241 s. The cultural heritage immersive 3D experience system has a satisfaction rate of 56.75%, and the density reconstructed model’s surface has an average deviation of only 0.34 mm from the real surface. The user satisfaction rating for the immersive 3D experience system for cultural heritage is 56.75%. Based on AIGC technology, it can revitalize cultural heritage and achieve digital reconstruction and inheritance innovation of cultural heritage.

In this paper, by applying the SIFT algorithm to the color category, texture and shape features of the dress pattern image for feature extraction and eliminating the low contrast texture feature points of the Taylor series expansion, using the statistics of the grayscale covariance matrix as the texture features. Based on the SIFT algorithm, the SURF feature extraction algorithm is proposed. The ideas of the Hessian matrix, determinant value approximation (DoH) and integral image (IGI) classifier are integrated to construct the feature fusion algorithm. Finally, the experimental results of several algorithms are compared and analyzed by taking the traditional dress pattern of the western Fujian Hakka family as the experimental object. The results show that among all the fusion algorithms, the SURF+IGI algorithm achieves an average recognition rate of 0.8914, which is the best performance. And the AUC value of its test set is 0.9445 when the learning rate is 0.005. The research in this paper meets the demand of extracting and applying the cultural elements of the traditional dresses of the Hakka family in western Fujian in modern dress design.