Feature Point Registration Research Articles

Virtual Sound Image Reconstruction Method for Multi-objective Optimization of Folk Music Based on Evolutionary Algorithm

At present, the exchanges in various fields such as culture, economy, and politics are becoming more and more close in the world. From the perspective of the relationship between national music and cultural development, the development of national music has also received more and more attention, and it has become an inevitable trend in the development of today's era. The purpose of this paper was to perform virtual reconstruction of the sound image of folk music through multiple objective optimizations, and to model the virtual sound image of folk music as a multi-objective optimization problem. According to the research on sound image positioning, the relevant noise factors were removed, so as to achieve the playback of ethnic music that enhanced the surround effect and visual enjoyment. The evolutionary algorithm in this paper was mainly based on the multi-objective optimized sound image localization technology. For the music virtual sound image, an improved FCM algorithm and an evolutionary multi-objective optimization algorithm combining local and non-local information of the sound image were proposed, respectively. Through the analysis of the traditional sound image algorithm method, the accuracy of the sound image localization on the horizontal plane could be effectively improved. After the conversion, the audience could feel the better stereo image and surround feeling of the folk music. Through experimental analysis, it can be seen that the system can not only perform virtual conversion of audio-visual signals of different frequencies, but also provide data for different audio playback systems. The feature point registration error is low, and the reconstruction effect is good, especially the random delay processing in the range of 0∼20m, and the performance is better than the traditional method. Finally, virtual left and right surround sound image signals were obtained, which effectively improved the three-dimensional surround feeling of folk music.

AUV SLAM method based on SO-CFAR and ADT feature extraction.

Due to the exceptional detection capabilities, the forward-looking sonar could be adopted in simultaneous localization and mapping (SLAM) for autonomous underwater vehicle (AUVs). This paper primarily investigates the application of the factor graph optimization SLAM algorithm based on feature maps in AUV. It achieves this by combining the smallest of constant false alarm rate (SO-CFAR) and adaptive threshold (ADT) to filter noise from the forward-looking sonar and extract feature point clouds. Furthermore, a weighted iterative closest point (WICP) algorithm is employed for feature point registration, which is extracted from the sonar image. The experimental result based on field data demonstrates that the proposed method, with an 8.52% improvement in root mean square error (RMSE) compared with dead reckoning (DR).

A continuation method for image registration based on dynamic adaptive kernel

Image registration is a fundamental problem in computer vision and robotics. Recently, learning-based image registration methods have made great progress. However, these methods are sensitive to abnormal transformation and have insufficient robustness, which leads to more mismatched points in the actual environment. In this paper, we propose a new registration framework based on ensemble learning and dynamic adaptive kernel. Specifically, we first use a dynamic adaptive kernel to extract deep features at the coarse level to guide fine-level registration. Then we added an adaptive feature pyramid network based on the integrated learning principle to realize the fine-level feature extraction. Through different scale, receptive fields, not only the local geometric information of each point is considered, but also its low texture information at the pixel level is considered. According to the actual registration environment, fine features are adaptively obtained to reduce the sensitivity of the model to abnormal transformation. We use the global receptive field provided in the transformer to obtain feature descriptors based on these two levels. In addition, we use the cosine loss directly defined on the corresponding relationship to train the network and balance the samples, to achieve feature point registration based on the corresponding relationship. Extensive experiments on object-level and scene-level datasets show that the proposed method outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability in unknown scenes with different sensor modes.

A new registration method with improved phase congruency for application to GPR images

ABSTRACT In this letter, we propose an edge detection algorithm and a framework of points matching for ground penetrating radar (GPR) images to achieve the B-Scan image registration of GPR. In our proposed algorithm, the robust edge feature information of GPR images is detected by the improved phase congruency model. Then, the feature points are detected by the GPR image’s edge feature in the curvature scale space. Finally, a novel similarity metric is defined for feature point registration. Experimental results show that compared to the classic registration method and the state-of-the-art feature points registration methods, the proposed method shows the best matching performance and robustness in GPR images.

Application of Artificial Neural Network Algorithm in Facial Biological Image Information Scanning and Recognition.

In order to solve the problem that the accuracy of face recognition is not good due to the influence of jitter and environmental factors in the mobile shooting environment, this paper proposes the application of an artificial neural network algorithm in the information scanning and recognition of face biological images. The spatial neighborhood information is integrated into the amplitude detection of multipose face images, and the dynamic corner features of multipose face images are extracted. The structural texture information of multipose face images is compared to a global moving RGB three-dimensional bit plane random field, and the multipose face images are detected and fused. At different scales, appropriate feature registration functions are selected to describe the feature points of multipose face images. The parallax analysis of target pixels and key feature detection of multipose face images are carried out. Image stabilization and automatic recognition are realized by combining artificial neural network learning and feature registration methods. The experimental results show that the experiment is designed with MATLAB, the frame frequency of dynamic face image acquisition is 1200 kHz, the number of collected samples of the multipose face image is 2000, the training sample set is 200, the noise coefficient = 0.24, the number of multipose face image blocks is 120, and the structural similarity is 0.12. It is found that the output signal-to-noise ratio of multipose face image recognition using the method in this paper is high. Conclusion. This method has good performance in feature point registration and high recognition accuracy for multipose face image recognition.

Feature Description with Feature Point Registration Error Using Local and Global Point Cloud Encoders

Feature Description with Feature Point Registration Error Using Local and Global Point Cloud Encoders

Panoramic image mosaic method based on image segmentation and Improved SIFT algorithm

Aiming at the problem of large amount of calculation in extracting image feature points in panoramic image mosaic by SIFT algorithm, a panoramic image mosaic algorithm based on image segmentation and Improved SIFT is proposed in this paper. The algorithm fully considers the characteristics of panoramic image stitching. Firstly, the stitched image is divided into blocks, and the maximum overlapping block of image pairs is extracted by using mutual information. The SIFT key points are extracted by SIFT algorithm, and the dog is filtered before the spatial extreme value detection of SIFT algorithm to eliminate the feature points with small intensity value; When establishing the feature descriptor, the 128 dimension of the original algorithm is reduced to 64 dimensions to reduce the amount of calculation. In the feature point registration process, the feature descriptor is reduced to 32 dimensions, the feature point pairs are roughly extracted by the optimal node first BBF algorithm, and the feature point pairs are registered and screened by RANSAC; Finally, the image transformation matrix is obtained to realize panoramic image mosaic. The experimental results show that the proposed algorithm not only ensures the panoramic mosaic effect, but also extracts the feature points in 11% of the time of the traditional SIFT algorithm, and the feature point registration speed is 27.17% of the traditional SIFT algorithm.

Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera.

In this study, an image registration algorithm was applied to calculate the rotation angle of objects when matching images. Some commonly used image feature detection algorithms such as features from accelerated segment test (FAST), speeded up robust features (SURF) and maximally stable extremal regions (MSER) algorithms were chosen as feature extraction components. Comparing the running time and accuracy, the image registration algorithm based on SURF has better performance than the other algorithms. Accurately obtaining the roll angle is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. To acquire the roll angle of agriculture machinery, a roll angle acquisition model based on the image registration algorithm was built. Then, the performance of the model with a monocular camera was tested in the field. The field test showed that the average error of the rolling angle was 0.61°, while the minimum error was 0.08°. The field test indicated that the model could accurately obtain the attitude change trend of agricultural machinery when it was working in irregular farmlands. The model described in this paper could provide a foundation for agricultural equipment navigation and autonomous driving.

Research on feature point registration method for wireless multi-exposure images in mobile photography

In the mobile shooting environment, the multi-exposure is easy to occur due to the impact of the jitter and the sudden change of ambient illumination, so it is necessary to deal with the feature point registration of the multi-exposure image under mobile photography to improve the image quality. A feature point registration technique is proposed based on white balance offset compensation. The global motion estimation of the image is carried out, and the spatial neighborhood information is integrated into the amplitude detection of the multi-exposure image under mobile photography, and the amplitude characteristics of the multi-exposure image under the mobile shooting are extracted. The texture information of the multi-exposure image is compared to that of a global moving RGB 3D bit plane random field, and the white balance deviation of the multi-exposure image is compensated. At different scales, suitable white balance offset compensation function is used to describe the feature points of the multi-exposure image, the parallax analysis and corner detection of the target pixel of the multi-exposure image are carried out, and the image stabilization is realized by combining the feature registration method. The simulation results show that the proposed method has high accuracy and good registration performance for multi-exposure image feature points under mobile photography, and the image quality is improved.

An augmented reality image registration method based on improved ORB

In the process of Augmented Reality (AR) image registration, the traditional ORB (oriented FAST and rotated BRIEF) algorithm has low registration rate and poor real-time performance. In this paper, an improved AR image registration method based on improved ORB is proposed. Firstly, the calibration image and video frame image feature points are obtained by the improved FAST feature detection algorithm. Then, the binary descriptor of BRISK, which using the custom domain sampling pattern is used for feature description, and the scale invariance of the traditional ORB algorithm is improved. Finally, the random sampling consistency (RANSAC) algorithm is used to eliminate the wrong matching point pairs and optimize the feature matching. Experiments show that compared with the AR image registration method described by the traditional ORB algorithm and the FREAK feature, the registration rate of the proposed algorithm is increased by 1.1% and 8.4%, and the generation time is reduced by 0.13s and 0.12s, respectively. The experimental results show that the AR image registration method proposed in this paper can obtain higher feature point registration rate, and has better real-time performance, which can better meet the application needs of AR image registration.

A point cloud registration algorithm based on normal vector and particle swarm optimization

Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.

Small UAV-based multi-temporal change detection for monitoring cultivated land cover changes in mountainous terrain

ABSTRACTLand degradation, soil erosion and illegal occupation in mountainous terrain of southern China have led to an ever-decreasing stock of cultivated land. Small unmanned aerial vehicles (UAVs) are used to collect images with very fine spatial and temporal resolutions. However, acquired image pairs of the same scene often contain scale changes, noises and rotated changes at different temporal scales. To address these problems, we propose a small UAV-based multi-temporal change detection for cultivated land cover in mountainous terrain which contains the following contributions. First, the multi-scale feature description includes convolutional neural network (CNN)-based feature descriptor (CFD) and neighbouring structure descriptor (NSD), where CFD is generated using layers formed via a pretrained Visual Geometry Group (VGG)-16 architecture. Second, a gradually increasing selection of inliers is defined for improving the robustness of feature point registration. Finally, intuitionistic fuzzy C-Means (IFCM) classifier is adopted to generate a similarity matrix between image pair of geometric correction process. The performance of proposed method is validated on multi-temporal image pairs taken by the small UAV. Experimental results show that the proposed method can detect cultivated land cover change at small size and scattered distribution landscapes, obtain satisfactory change detection results.

Registration of pre- and postoperative surface scans for pediatric neurosurgery

Abstract A software for automatic registration and differential visualization of pre- and postoperative surface scans for use in pediatric neurosurgery is developed and implemented. The core operations of the registration include an automatic head extraction, a rough registration by aligning the main axes, a depth map-based registration for robust determination and registration of feature points in the facial area, and a fine registration. A graphical user interface is implemented for the difference visualization and program control.

Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features

Registration of multi-temporal remote sensing images has been widely applied in military and civilian fields, such as ground target identification, urban development assessment, and geographic change assessment. Ground surface change challenges feature point detection in amount and quality, which is a common dilemma faced by feature-based registration algorithms. Under severe appearance variation, detected feature points may contain a large proportion of outliers, whereas inliers may be inadequate and unevenly distributed. This paper presents a convolutional neural network (CNN) feature-based multi-temporal remote sensing image registration method with two key contributions: (i) we use a CNN to generate robust multi-scale feature descriptors and (ii) we design a gradually increasing selection of inliers to improve the robustness of feature point registration. Extensive experiments on feature matching and image registration are performed over a multi-temporal satellite image data set and a multi-temporal unmanned aerial vehicle image dataset. Our method outperforms four state-of-the-art methods in most scenarios.

AUTOMATIC GEO-REFERENCING MOBILE LASER SCANNING DATA TO UAV IMAGES

Abstract. In this paper, a framework for adjusting mobile laser scanning point cloud data to improve the accuracy is proposed by integrating high resolution UAV images and MLS. First, aerial triangulated images with a few high accuracy ground control points are taken as control information. Then, a hierarchical strategy is proposed for robust pairwise registration of feature points between point cloud and images, so as to find the deviation of the point cloud. In the next step, a shape-preserving piecewise cubic interpolating method is employed to fit the time dependent error model of the trajectory. Finally, experiments are given to prove the effectiveness of proposed framework.

Quick Registration Method Based on Image Region Correlation

Binocular vision system can be widely used in CNC machine tools chatter monitoring, due to its simple system and automatic measurement function. Traditional registration method cannot balance the contradiction between precision and speed of registration; restrict its application in high speed monitoring system. So based on traditional feature point registration method, it proposes a new method to obtain more accurate matching feature points by using complexity distribution feature of image region to determine the distribution of feature region and the bidirectional similarity and triangle similar method, which realize quick registration. From the simulation and implementation effect perspective, this method is feasible for the image registration in high-speed monitoring system.

Rigid Point Feature Registration for MRI Brain Images based on Fuzzy Transform

Radiological images are gradually more being used in healthcare and medical research. There is, accordingly, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. In this paper we formulate feature registration problems by using F-transform (Fuzzy-Transform) to extract the salient edges and extracted control points (CP) of medical Images. The MRI brain Images were first decomposed using F-transform, then Edge and CP were extracted from F-transform error function, and some proposed rules. After edge and CP extraction, mutual information (MI) was adopted for the registration of feature points, and translation parameters are calculated by using particle swarm optimization (PSO). We implement experiments to evaluate the performance of the FTransform and MI similarity measures for 2-D/3-D rigid point feature registration. The experimental results showed that the proposed method produces totally accurate performance for rigid point feature registration for MRI brain images.

MRI Monomodal Feature-Based Registration Based on the Efficiency of Multiresolution Representation and Mutual Information

Image registration methods based on mutual information criteria have been widely used in monomodal medi- cal image registration and have shown promising results. Feature-based registration is an efficient technique for clinical use, because it can significantly reduce computational costs. In general, the majority of registration methods consist of the fol- lowing four steps: feature extraction, feature matching, transformation of the models and, finally, resampling the image. It was noted that the accuracy of the registration process depends on matching a feature and control points (CP) detection. Therefore in this paper has been to rely on this feature for magnetic resonance image (MRI) monomodal registration. We have proposed to extract the salient edges and extracted a CP of medical images by using efficiency of multiresolution rep- resentation of data nonsubsampled contourlet transform (NSCT). The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information (MI) was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). We implement experiments to evaluate the per- formance of the NTSC and MI similarity measures for 2-D monomodal registration. The experimental results showed that the proposed method produces totally accurate performance for MRI monomodal registration.

Structure-oriented contour representation and matching for engineering shapes

Conventional shape matching for engineering models primarily considers rigid shape similarity. They do not seek global shape similarity while considering large local deformations. However, engineering models created by some parametric-based design can involve large parametric changes. As a result, they do not share similarity in their global shape. Hence our goal is to develop shape representations for global matching of part models that can have large dissimilarity through stretching and/or bending. This paper presents a strategy of an integrated shape matching for contours of engineering drawings inspired by the divide-and-conquer paradigm. The original shape is decoupled into two levels of shape representations namely, higher-level structure and lower-level geometry. The higher-level structure matching is then achieved driven by optimal integrated solutions from matching of lower-level local geometry. Feature points are first extracted using curve evolution to attain the two levels of representations. In order to suit engineering semantics, a new significance function for a point is defined to suppress small features using discrete curve evolution. To conduct the integrated shape matching, a mechanism of using lookup tables is employed to associate these two levels of representations. Dynamic Time Warping and Elastic Matching are employed at different levels of shape representations in order to achieve the optimal integration. To demonstrate the advantages of the proposed work for engineering shapes, experiments for contour evolution, feature point registration, and shape-based similarity for retrieval are conducted. They are also compared with the existing methods. The experimental results show that the structure-oriented contour representation and matching are more meaningful and consistent from an engineering perspective.

Image registration and object recognition using affine invariants and convex hulls

This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering L feature points on the test image with N feature points of the reference image is of order O(N x L). The method has been tested on real indoor and outdoor images and performs well.