Feature Point Matching Method Research Articles

The Design of a Video Reflection Removal Method Based on Illumination Compensation and Image Completion Fusion

Our objective is to develop a video reflection removal algorithm that is both easy to compute and effective. Unlike previous methods that depend on machine learning, our approach proposes a local image reflection removal technique that combines image completion and lighting compensation. To achieve this, we utilized the MSER image region feature point matching method to reduce image processing time and the spatial area of layer separation regions. In order to improve the adaptability of our method, we implemented a local image reflection removal technique that utilizes image completion and lighting compensation to interpolate layers and update motion field data in real-time. Our approach is both simple and efficient, allowing us to quickly obtain reflection-free video sequences under a variety of lighting conditions. This enabled us to achieve real-time detection effects through video restoration. This experiment has confirmed the efficacy of our method and demonstrated its comparable performance to advanced methods.

Thermal Imaging Detection Device Based on Infrared Photoelectric Sensor and Its Application in Fault Detection of Transformer Bushing Insulation

Due to the unique advantages of infrared thermal imaging system compared with visible light, infrared photoelectric sensors can convert the perceived infrared light into electrical signals, which can help people to observe and analyze. Thermal imaging detection devices are developing towards miniaturization, low power consumption and high resolution. According to this requirement, the hardware design of thermal imaging detection system for infrared detection is carried out, and 640×512 uncooled infrared focal plane array is adopted. ADV7390 is selected as the video coding chip, and the low power consumption characteristics of this device are optimized. Selecting reference source+digital potentiometer+operational amplifier as analog bias scheme can make the output analog bias within a certain voltage range. The signal processing method of the detector is improved, that is, divide the voltage and form a differential signal with VREF, and then send it to the ADC chip. In the experiment, DC/DC switching power supply, LDO output voltage and ripple are tested. The results show that the measured voltage value is close to the standard voltage value, the signal voltage is basically around 2 V, and the signal voltage is lower than the signal range fluctuation, which is in line with expectations. It is used to detect transformer bushing insulation defects, and an infrared thermal imaging device is designed to capture bushing insulation defects. Combined with the feature point matching method, the infrared feature distribution of bushing insulation defects is obtained, and the infrared spectrum of the outside of transformer bushing can be obtained through continuous tracking to determine the abnormal temperature position.

Robot Target Location Based on the Difference in Monocular Vision Projection

Visual guidance is widely used in industrial robots. At present, the traditional method of template matching and positioning is often used in industrial robots under monocular vision guidance. However, for complex workpieces with height differences, if the angle and position of the workpiece are not consistent with the template, the projection will be different, and the positioning accuracy will be obviously reduced. A new method is proposed to solve this problem by dividing the whole contour of the workpiece and using the weighted method of the contour module to correct and match the workpiece image. First, the contour region of the template image is divided according to the position of the grasping points. Then the weight is assigned according to the distance from each contour region to the grasping points. Then, a fast feature point matching method is used to match the initial image of the workpiece to be measured. Finally, accurate contour matching is carried out for each weighted contour region so that the robot can grasp the object accurately. A large number of experiments show that the method has the design requirements of high stability and high accuracy.

Attention-Based Matching Approach for Heterogeneous Remote Sensing Images

Heterogeneous images acquired from various platforms and sensors provide complementary information. However, to use that information in applications such as image fusion and change detection, accurate image matching is essential to further process and analyze these heterogeneous images, especially if they have significant differences in radiation and geometric characteristics. Therefore, matching heterogeneous remote sensing images is challenging. To address this issue, we propose a feature point matching method named Cross and Self Attentional Matcher (CSAM) based on Attention mechanisms (algorithms) that have been extensively used in various computer vision-based applications. Specifically, CSAM alternatively uses self-Attention and cross-Attention on the two matching images to exploit feature point location and context information. Then, the feature descriptor is further aggregated to assist CSAM in creating matching point pairs while removing the false matching points. To further improve the training efficiency of CSAM, this paper establishes a new training dataset of heterogeneous images, including 1,000,000 generated image pairs. Extensive experiments indicate that CSAM outperforms the existing feature extraction and matching methods, including SIFT, RIFT, CFOG, NNDR, FSC, GMS, OA-Net, and Superglue, attaining an average precision and processing time of 81.29% and 0.13 s. In addition to higher matching performance and computational efficiency, CSAM has better generalization ability for multimodal image matching and registration tasks.

A Light Field Full-Focus Image Feature Point Matching Method with an Improved ORB Algorithm

Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms.

Underwater multi-frame target images mosaic method based on adaptive image enhancement

The severe attenuation and scattering of light in the water reduces the effective field of view of the underwater camera, making the scene information contained in a single image limited, and it is difficult to meet the application requirements of the large-scale underwater scenes. To solve this problem, an underwater multi-frame target images mosaic method based on adaptive image enhancement is proposed in this paper. Firstly, the image blur prior is used to achieve the adaptive enhancement of underwater images to suppress the blur and colour distortion of underwater images. Then, the feature point matching method based on the improved SURF realizes the extraction and matching of the feature points of underwater multi-frame target images. Finally, combining with the fusion strategy of gradual in and out, the seamless splicing and fusion of underwater multi-frame target images is realized. The pool and shallow sea tests were carried out respectively, and the results show that the method proposed in this paper increases the number of effective feature point matching and improves the splicing effect.

AdaSG: A Lightweight Feature Point Matching Method Using Adaptive Descriptor with GNN for VSLAM.

Feature point matching is a key component in visual simultaneous localization and mapping (VSLAM). Recently, the neural network has been employed in the feature point matching to improve matching performance. Among the state-of-the-art feature point matching methods, the SuperGlue is one of the top methods and ranked the first in the CVPR 2020 workshop on image matching. However, this method utilizes graph neural network (GNN), resulting in large computational complexity, which makes it unsuitable for resource-constrained devices, such as robots and mobile phones. In this work, we propose a lightweight feature point matching method based on the SuperGlue (named as AdaSG). Compared to the SuperGlue, the AdaSG adaptively adjusts its operating architecture according to the similarity of input image pair to reduce the computational complexity while achieving high matching performance. The proposed method has been evaluated through the commonly used datasets, including indoor and outdoor environments. Compared with several state-of-the-art feature point matching methods, the proposed method achieves significantly less runtime (up to 43× for indoor and up to 6× for outdoor) with similar or better matching performance. It is suitable for feature point matching in resource constrained devices.

A night pavement crack detection method based on image‐to‐image translation

AbstractDeep learning provides an efficient automated method for pavement condition surveys, but the datasets used for this model are usually images taken in good lighting conditions. If images are taken at night, this model cannot work effectively. This paper proposes a method for normalizing pavement images at night, which includes three main steps. First, the image feature point detection and matching method is used to process images taken during the day and night. Then, paired images of pavement during the day and night are obtained. Second, with the help of the image‐to‐image translation model, those paired images are used for training, and the best model for converting night images into day images is selected. Third, a convolutional neural network (CNN) based on VGGNet is constructed, and pavement images taken during the day are used for training. After that, six types of images are used and tested separately, namely, those taken during the day and the night, converted by the proposed method and converted by traditional methods. As evaluated by various evaluation indices and visualization methods, the detection performance of the CNN model can be significantly improved by using the proposed method of converted night‐to‐day images.

FEATURE FUSION-BASED FEATURE POINT EXTRACTION AND MATCHING OF THE THREE-DIMENSIONAL HUMAN VERTEBRAE MODEL

In the positioning and registration of a three-dimensional human vertebrae model, the main difficulty encountered is the extraction and matching of the model feature points. To accurately locate the vertebrae, it is necessary to optimize the existing feature point extraction and matching method. In this context, the present study was envisaged to achieve the purpose of accurate extraction and matching by integrating the features of curvature and Euclidean distance. It was found that this method removed the redundant point cloud data in the 3D model, thereby effectively improving the matching rate. Initially, the feature points were manually picked as the circle center, and the mean value ([Formula: see text]) of average curvature ([Formula: see text]) of all points in the specified infinitesimal radius ([Formula: see text]) sphere neighborhood were calculated. When [Formula: see text], it implied the point [Formula: see text] tended to be flat relative to the whole point cloud surface, and was not regarded as a feature point. When [Formula: see text], the point [Formula: see text] exhibited a greater curvature relative to the whole point cloud surface, that is, a more prominent point. In this case, point [Formula: see text] met the basic conditions for becoming a feature point and was included in the candidate feature point set. Subsequently, the Euclidean distance between the points in the candidate feature point set and the manually picked circle center points was calculated individually. The smaller the Euclidean distance between the two points, the higher the degree of feature similarity between the two points, and the stronger the matching of feature points. Therefore, the manually picked points were replaced with the candidate feature point with the closest distance that accurately reflected the change of the local geometric characteristics of the point. It was found that the accuracy of the improved method of the feature point extraction and matching was about 18% higher than that before the improvement, which verifies the effectiveness and robustness of the proposed method.

A Demodulation Algorithm for Periodically In-Plane Vibrating MEMS Based on a Stroboscopic Micro-Visual System.

A high-frequency short-pulsed stroboscopic micro-visual system was employed to capture the transient image sequences of a periodically in-plane working micro-electro-mechanical system (MEMS) devices. To demodulate the motion parameters of the devices from the images, we developed the feature point matching (FPM) algorithm based on Speeded-Up Robust Features (SURF). A MEMS gyroscope, vibrating at a frequency of 8.189 kHz, was used as a testing sample to evaluate the performance of the proposed algorithm. Within the same processing time, the SURF-based FPM method demodulated the velocity of the in-plane motion with a precision of 10−5 pixels of the image, which was two orders of magnitude higher than the template-matching and frame-difference algorithms.

Research on feature point generation and matching method optimization in image matching algorithm

Research on feature point generation and matching method optimization in image matching algorithm

Image feature point matching method based on improved BRISK

In order to improve the accuracy of the BRISK algorithm of feature point matching, an improved BRISK feature point matching method is proposed by combining the SIFT algorithm idea with high matching accuracy. Firstly, the Gaussian image pyramid is established, then the FAST algorithm is used to detect the feature points of each image layer in the image pyramid, and the intensity centroid method is used to determine the direction of the feature points, so the feature points obtained have scale information and direction information. Then, using the BRISK descriptor to describe the feature points, using the Hamming distance to measure the similarity of the feature descriptors, and calculating the matching accuracy and matching time. The experimental results show that the accuracy of the method proposed in this paper is higher than that of the BRISK algorithm and the time is better than that of the SIFT algorithm and the BRISK algorithm.

Image tracking and matching algorithm of semi-dense optical flow method

The traditional optical flow method is based on the assumption of spatial consistency of the optical flow field. It is easy to reduce the tracking quality and even lead to the loss of target tracking in the areas of image feature missing, boundary and occlusion. A semi-dense optical flow method is proposed to realise stable tracking of image features. Firstly, the feature points are preserved by calculating the pixel points with large change of pixel gradient in the image; Secondly, according to the principle of grey level invariance, the grey level difference function between the corresponding feature points of adjacent frames is constructed; Finally, the gradient descent principle is used to optimise the grey difference function and realise the accurate matching of feature points of adjacent frames. The results show that compared with the traditional LK optical flow method, this algorithm can effectively improve the feature tracking capability, and at the same time can effectively retain the useful information in the image. Compared with the traditional feature point matching method, the algorithm presented in this paper has an efficient operation rate.

Image feature point matching method based on improved BRISK

Image feature point matching method based on improved BRISK

Design of visual odometer based on RANSAC optical flow method and feature point matching method

Design of visual odometer based on RANSAC optical flow method and feature point matching method

Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor

Autonomous aerial refueling is a significant technology that can significantly extend the endurance of unmanned aerial vehicles. A reliable method that can accurately estimate the position and attitude of the probe relative to the drogue is the key to such a capability. A drogue pose estimation method based on infrared vision sensor is introduced with the general goal of yielding an accurate and reliable drogue state estimate. First, by employing direct least squares ellipse fitting and convex hull in OpenCV, a feature point matching and interference point elimination method is proposed. In addition, considering the conditions that some infrared LEDs are damaged or occluded, a missing point estimation method based on perspective transformation and affine transformation is designed. Finally, an accurate and robust pose estimation algorithm improved by the runner-root algorithm is proposed. The feasibility of the designed visual measurement system is demonstrated by flight test, and the results indicate that our proposed method enables precise and reliable pose estimation of the probe relative to the drogue, even in some poor conditions.

Multimode image matching based on maximum similarity model and scale invariant feature transform for islands

Different from the images of villages or towns where many homologous features can be extracted easily, the extraction of homologous features from multimode images located of sea islands is more difficult because the types of objects on sea islands are hard to recognize. When applying scale invariant feature transform (SIFT) to the image matching of sea islands, only sparsely matched couples with an uneven distribution are obtained. To resolve this problem, a new feature point matching method that combines a maximum similarity model and scale invariant feature transform (MS-SIFT) is proposed. This method can solve the primary difficulty of matching multimode images, which is to extract the massive feature points that are invariant to differences in spectrum, scale, and view angles between multimode images, and successfully obtain many precise matched couples of images located in areas with less remarkable features, especially for matching images of sea islands with less features. The application of the proposed algorithm shows that a large number of accurately matched couples could be identified. Additionally, the matched accuracy, space uniformity of the matched couples, and the running time computed based on the MS-SIFT and the traditional SIFT are analyzed and compared, which further demonstrate the effectiveness of the proposed algorithm.

A comparative study of feature point matching versus foreground detection for computer detection of dairy cows in video frames

Behaviours of dairy cows reflect their health and emotions. Behavioural analysis by video surveillance is an accepted technique for helping cow-keepers to spot their cows' health problems. To perform a behavioural analysis, the presence and location of the cows need to be detected first. In this study, we used feature point matching method and foreground detection method to detect them. Two experiments were conducted in a dairy farm to detect cows in video frames recorded by a video camera installed over the top of a free-stall barn. A total of 800 frames of recorded cows' activities were captured. True and false positive and negative results were statistically confirmed by t test. We found that the accuracies of the feature point matching and foreground detection methods were 38.55 and 75.95 %, respectively; hence, for our setup, the foreground detection was a better method.

Reliable Spatial Relationship Constrained Feature Point Matching of Oblique Aerial Images

This paper proposes a reliable feature point matching method for oblique images using various spatial relationships and geometrical information for the problems resulted by the large view point changes, the image deformations, blurring, and other factors. Three spatial constraints are incorporated to filter possible outliers, including a cyclic angular ordering constraint, a local position constraint, and a neighborhood conserving constraint. Other ancillary geometric information, which includes the initial exterior orientation parameters that are obtained from the platform parameters and a rough DEM, are used to transform the oblique images geometrically and reduce the perspective deformations. Experiment results revealed that the proposed method is superior to the standard SIFT regarding both precision and correct matches using images obtained by the SWDC-5 system.

Robust feature point matching with sparse model.

Feature point matching that incorporates pairwise constraints can be cast as an integer quadratic programming (IQP) problem. Since it is NP-hard, approximate methods are required. The optimal solution for IQP matching problem is discrete, binary, and thus sparse in nature. This motivates us to use sparse model for feature point matching problem. The main advantage of the proposed sparse feature point matching (SPM) method is that it generates sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. Therefore, it can optimize the IQP matching problem in an approximate discrete domain. In addition, an efficient algorithm can be derived to solve SPM problem. Promising experimental results on both synthetic points sets matching and real-world image feature sets matching tasks show the effectiveness of the proposed feature point matching method.