Image Feature Points Research Articles

Fast Automatic Registration of UAV Images via Bidirectional Matching.

Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB-the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.

Image cascade matching method based on an improved random sampling consistency algorithm

In the process of image matching, image quality will have a certain impact on the extraction of image features, directly affecting the matching accuracy of subsequent images. Therefore, an image cascade matching method based on improved random sampling consistency algorithm is proposed. This method first needs to improve the quality of the matched target image through preprocessing results such as image denoising and enhancement, and prepare for feature point extraction during subsequent image cascade matching; Then extract the feature points of the target to be matched in the image, and use the improved random sampling consistency method to quickly estimate and simplify the registration model based on the extraction results. Use similar feature triangles to perform model pre testing, and use the maximum Euclidean distance method to improve the accuracy of image cascade registration, achieving image cascade matching. The experimental results show that the proposed method can achieve image enhancement while removing image noise, improve the quality of the image, comprehensively extract the feature points to be matched in the image, and achieve accurate matching of image feature points.

The integration of traditional lacquerware and modern art design in the context of artificial intelligence

Abstract This paper aims to promote the integration of traditional lacquerware and modern art design. AR technology is used for feature calculation of the appearance contour of traditional lacquerware. In this paper, the BRIEF algorithm is selected as the main algorithm for image feature calculation. The Steer BRIEF algorithm is used for key points, and the image feature points are extracted. The classical optical flow method of CMT is applied to track the appearance contour of traditional lacquerware, and the SSD and NCC are combined with filtering the measurement of tracking points. The results show that 90% of the traditional lacquerware can reflect the cultural connotation of the time, 80% of the traditional lacquerware can reflect the characteristics of the time, 80% of the traditional lacquerware has excellent craftsmanship, and the added value reflected by modern art design is much lower than that of the traditional lacquerware.

Enhancing Visual Feedback Control through Early Fusion Deep Learning.

A visual servoing system is a type of control system used in robotics that employs visual feedback to guide the movement of a robot or a camera to achieve a desired task. This problem is addressed using deep models that receive a visual representation of the current and desired scene, to compute the control input. The focus is on early fusion, which consists of using additional information integrated into the neural input array. In this context, we discuss how ready-to-use information can be directly obtained from the current and desired scenes, to facilitate the learning process. Inspired by some of the most effective traditional visual servoing techniques, we introduce early fusion based on image moments and provide an extensive analysis of approaches based on image moments, region-based segmentation, and feature points. These techniques are applied stand-alone or in combination, to allow obtaining maps with different levels of detail. The role of the extra maps is experimentally investigated for scenes with different layouts. The results show that early fusion facilitates a more accurate approximation of the linear and angular camera velocities, in order to control the movement of a 6-degree-of-freedom robot from a current configuration to a desired one. The best results were obtained for the extra maps providing details of low and medium levels.

Defect enhancement method for ZrO2-bearing spherical surface representation based on complete defect stitching and self-defined background balancing algorithm

To address the problems of difficulty in extracting complete defects and high interference of surface background in the process of ZrO2 bearing ball defect detection. A method of ZrO2 bearing ball surface representation defect enhancement based on complete defect stitching and self-defined background balance algorithm is proposed. To determine the location of complete defects, we propose a scanning and acquisition scheme for sub-aperture images and quickly stitch the edges of sub-aperture images. The image feature point gradient definition is optimized, and a less error Euclidean distance finding method is used for feature point matching. The stitched complete defect image is smooth and has high pixel accuracy. The background feature layer of the complete defect image is extracted for background gray leveling, and this process is introduced into the γ-value calculation process. The γ setting allows the image to be self-defined enhancement, outputting a fully defective image with a balanced background and outstanding defects. The average gradient is reduced by more than 90% after complete defect image enhancement, and the information entropy is preserved by about 40%. The defect edges of the image are intact, and the fine texture of the defect features is preserved.

Dam UAV Image Stitching Technology Studies Based on Weak Texture

Extraction of image feature point pairs is an important step in the image alignment process. In this paper, we propose a grid-based adaptive threshold feature detection method for weakly textured images using a single threshold extraction algorithm that cannot propose uniformly distributed feature points. Finally, the designed experiments show that the feature points extracted by this method have a lower dispersion than the traditional KAZE algorithm by 70% and a higher alignment accuracy than the traditional KAZE algorithm by 10%.

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

AbstractThe goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness.

DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology

Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro brain studies. Current semi-automated alignment workflows require labor intensive labeling of feature points on each slice image, which is time-consuming. To speed up the process in large-scale studies, we propose a method called Deep Learning-Assisted Transformation Alignment (DLATA), which uses deep learning to automatically identify feature points in images after training on a few labeled samples. DLATA only requires approximately 10% of the sample size of other semi-automated alignment workflows. Following feature point recognition, local weighted mean method is used as a geometrical transformation to align slice images for registration, achieving better results with about 4 fewer pixels of error than other semi-automated alignment workflows. DLATA can be retrained and successfully applied to the alignment of other biological tissue slices with different stains, including the typically challenging fluorescent stains. Reference codes and trained models for Nissl-stained coronal brain slices of mice can be found at https://github.com/ALIGNMENT2023/DLATA.

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation.

The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.

Hybrid Model Predictive Control with Penalty Factor Based on Image-Based Visual Servoing for Constrained Mobile Robots

For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive target bias gain when using traditional IBVS control methods. The traditional IBVS control is transformed into an optimization problem with constraints in the finite time domain, by defining the optimization function based on the mobile robot’s positional deviation and image feature point deviation, while using actuator saturation and speed limit as constraints. Based on this, a convex optimization function with penalty factors is defined and combined with incremental model predictive control. This control strategy could ensure the emergency braking performance of the mobile robot when the image feature points are massively obscured by obstacles in dynamic scenes, while improving the accuracy and real-time of its trajectory tracking control. Finally, simulation comparisons are conducted to verify the effectiveness of the proposed control method.

An AAM-Based Identification Method for Ear Acupoint Area.

Ear image segmentation and identification is for the "observation" of TCM (traditional Chinese medicine), because disease diagnoses and treatment are achieved through the massaging of or pressing on some corresponding ear acupoints. With the image processing of ear image positioning and regional segmentation, the diagnosis and treatment of intelligent traditional Chinese medicine ear acupoints is improved. In order to popularize ear acupoint therapy, image processing technology has been adopted to detect the ear acupoint areas and help to gradually replace well-trained, experienced doctors. Due to the small area of the ear and the numerous ear acupoints, it is difficult to locate these acupoints based on traditional image recognition methods. An AAM (active appearance model)-based method for ear acupoint segmentation was proposed. The segmentation was illustrated as 91 feature points of a human ear image. In this process, the recognition effects of the ear acupoints, including the helix, antihelix, cymba conchae, cavum conchae, fossae helicis, fossae triangularis auriculae, tragus, antitragus, and earlobe, were divided precisely. Besides these, specially appointed acupoints or acupoint areas could be prominent in ear images. This method made it possible to partition and recognize the ear's acupoints through computer image processing, and maybe own the same abilities as experienced doctors for observation. The method was proved to be effective and accurate in experiments and can be used for the intelligent diagnosis of diseases.

Leveraging CNNs for Panoramic Image Matching Based on Improved Cube Projection Model

Three-dimensional (3D) scene reconstruction plays an important role in digital cities, virtual reality, and simultaneous localization and mapping (SLAM). In contrast to perspective images, a single panoramic image can contain the complete scene information because of the wide field of view. The extraction and matching of image feature points is a critical and difficult part of 3D scene reconstruction using panoramic images. We attempted to solve this problem using convolutional neural networks (CNNs). Compared with traditional feature extraction and matching algorithms, the SuperPoint (SP) and SuperGlue (SG) algorithms have advantages for handling images with distortions. However, the rich content of panoramic images leads to a significant disadvantage of these algorithms with regard to time loss. To address this problem, we introduce the Improved Cube Projection Model: First, the panoramic image is projected into split-frame perspective images with significant overlap in six directions. Second, the SP and SG algorithms are used to process the six split-frame images in parallel for feature extraction and matching. Finally, matching points are mapped back to the panoramic image through coordinate inverse mapping. Experimental results in multiple environments indicated that the algorithm can not only guarantee the number of feature points extracted and the accuracy of feature point extraction but can also significantly reduce the computation time compared to other commonly used algorithms.

A general geometric transformation model for line-scan image registration

A reasonable geometric transformation model is the key to image registration. When the relative motion direction between the line-scan camera and the object is strictly parallel to the planar object, it is possible to align the image by using the eight-parameter geometric transformation model of the line-scan image. However, it will be invalid when the relative motion direction is arbitrary. Therefore, a new general geometric transformation model of line-scan images is proposed for line-scan image registration in this paper. Considering the different initial poses and motion directions of the line-scan camera, the proposed model is established based on the imaging model of the line-scan camera. In order to acquire line-scan images to verify the proposed model, a line-scan image acquisition system was built. The method based on feature points is used to register the line-scan images. The experimental results show that the proposed geometric transformation model can align the line-scan image collected under arbitrary relative motion direction, not just the parallel case. Besides, the statistical errors of the image feature point coordinates are the best performance after registration. The accuracy of the registration results is better than that of other existing geometric transformation models, which verifies the correctness and generality of the geometric transformation model of the line-scan camera proposed in this paper.

YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise

Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network’s perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model’s robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model’s performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks.

Spatiotemporal Image-Based Flight Trajectory Clustering Model with Deep Convolutional Autoencoder Network

Recent studies in four-dimensional flight trajectories attempted to identify the impacts of various flight trajectories and maneuver parameters on air traffic management efficiency and aviation safety. The previous studies attempted to cluster trajectories based on spatial scales. However, these might require converting the flight trajectories to equal lengths for sequence-based clustering. This paper proposes a novel trajectory three-channel image representation and Gaussian mixture model clustering based on several image-processing methodologies. The aircraft’s latitude, longitude, flight level, and ground speed are represented as corresponding pixel information of the image followed by image-based flight trajectory representation and clustering methods (including deep convolutional autoencoder (DCAE), principal component analysis (PCA) image dimensionality reduction, and image feature points extraction) using a half-year of automatic dependent surveillance-broadcast flight trajectory data in the Hong Kong flight information region. The computational results indicate that the image-based trajectory representation produces more insights for trajectory processing, such as the application of convolutional neural networks and image-processing algorithms. In addition, the DCAE model has better performance and robustness for trajectory feature extraction and similarity analysis than PCA, which will provide ideas for multiparameter trajectory similarity analysis and prediction.

A Backprojection-Based Autofocus Imaging Method for Circular Synthetic Aperture Radar

Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information through 360° observation of the target. However, CSAR is often subject to motion errors due to the sensor’s accuracy limitations and the unique trajectory of the system. The autofocusing back projection algorithm based on the optimization of image sharpness compensates for motion errors through phase-error estimation. This method can attain relatively good performance while assuming the same error for all pixels; it ignores the spatial variance of motion errors. In order to solve this problem, this paper proposes an autofocusing method based on the Prewitt operator and particle swarm optimization (PSO), which first extracts the subaperture image feature points as a new dataset using the Prewitt operator, estimates the radar slope error on the new dataset using PSO, and subsequently compensates the subaperture image. Finally, the subaperture images are synthesized using an image-alignment method. The results of both the simulated experiments and the measured data validate the effectiveness of this method.

Color Machine Vision Design Methodology of a Part-Presentation Algorithm for Automated Poultry Handling

This article offers an image processing method to optimize the design of a three-level color machine vision algorithm illustrated in the context of presenting a whole chicken carcass for subsequent handling, which overcomes some common problems in image segmentation, feature identification, and pose estimation for classifying, identifying, and locating poultry meat products. First, artificial color contrast and principal component analysis are optimized to enhance the contrast between similar colors to extract targeted regions more effectively. Second, the boundaries of poultry products are mostly smooth curves that are proposed as features for object recognition. Third, image feature points are found using template matching, from which the object (size, location, and pose) can be accurately determined. The algorithm simplifies the complex edges of a poultry product into a finite number of arc-centers, which greatly improves the efficiency/accuracy of the matching for part-presentation. Evaluated with 100 randomly posed samples, the algorithm has a success rate of 93%. The 7% failures were primarily due to missing points, which can be eliminated by incorporating more template points.

Research on 3D reconstruction method of cattle face based on image

As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features.

Adaptive Feature Point Image Registration Algorithm with Added Spatial Constraint Model

Image data with different spectral features contain different attribute information of a target, which is naturally complementary and can provide more comprehensive and detailed features after registration and fusion. Image registration methods based on point features have the advantages of high speed and precision, and have been widely used in visible light image registration. For registration of multiscale images and those with different spectral characteristics, the precision of these methods is affected by such factors as complex gradient variation. To this end, we add a spatial constraint model to point feature image registration, and improve the method from the aspects of feature point selection, registration, and image conversion parameter calculation. The method is applied to different types of image registration programs, and the results show that it can effectively improve the registration accuracy of multiscale images with different spectral characteristics.

An adaptive method of measuring the rake face wear of end mills based on image feature point set registration

An acceptable tool wear evaluation and acquisition approach is critical for developing an accurate, rapid, and practical online tool wear prediction system. The one-dimensional crater wear depth is difficult to quantify, and relying solely on flank wear width is very simplistic. External conditions such as vibration and illumination have a large impact on two-dimensional wear area and three-dimensional volume. In practice, these are difficult to successfully characterize tool wear. This work proposes a two-dimensional rake face edge loss area as an additional indicator to assess milling cutter wear. An adaptive measurement approach based on 2D image feature point set matching is presented to suppress the area calculation error caused by image position offset and chips or built-up edges. 201 images were collected in two groups of cutting experiments for comparison with the proposed method and the other two methods, manual registration and non-registration. The results show that the proposed method's measurement values are closer to the actual values and have a higher measurement efficiency. Furthermore, the proposed indicator is sensitive to micro-scale changes to the blade edge, especially at the tip, and can be used as a supplement one to monitor tool wear more comprehensively.