Feature Point Extraction Algorithm Research Articles

Research on a Feature Point Detection Algorithm for Weld Images Based on Deep Learning

Laser vision seam tracking enhances robotic welding by enabling external information acquisition, thus improving the overall intelligence of the welding process. However, camera images captured during welding often suffer from distortion due to strong noises, including arcs, splashes, and smoke, which adversely affect the accuracy and robustness of feature point detection. To mitigate these issues, we propose a feature point extraction algorithm tailored for weld images, utilizing an improved Deeplabv3+ semantic segmentation network combined with EfficientDet. By replacing Deeplabv3+’s backbone with MobileNetV2, we enhance prediction efficiency. The DenseASPP structure and attention mechanism are implemented to focus on laser stripe edge extraction, resulting in cleaner laser stripe images and minimizing noise interference. Subsequently, EfficientDet extracts feature point positions from these cleaned images. Experimental results demonstrate that, across four typical weld types, the average feature point extraction error is maintained below 1 pixel, with over 99% of errors falling below 3 pixels, indicating both high detection accuracy and reliability.

Comparative Analysis of Image Stitching Algorithms for Bridge Inspection Imaging Systems

This paper discusses an innovative vehicle-mounted device designed for efficient inspection of bridge undersides, aiming to address the challenges of manual inspections, such as being labor-intensive and inefficient. The device uses high-precision cameras to capture detailed images of bridge bottoms, facilitating a thorough condition assessment. It evaluates three feature point extraction algorithms for image stitching—Scale-Invariant Feature Transform (SIFT), Harris corner detection, and Speeded Up Robust Features (SURF)—to create complete visual representations of bridge undersides. Field tests on a prefabricated I girder bridge demonstrated the device's effectiveness in gathering and stitching images, with the SIFT algorithm performing best for girder bottoms, Harris corner algorithm for web and flange surfaces, and SURF for rapid image processing. This research provides a foundation for the rapid identification of visible defects on bridge superstructures, significantly benefiting bridge maintenance and safety evaluations.

Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio

Traditional iterative closest point (ICP) registration algorithms are sensitive to initial positions and easily fall into the trap of locally optimal solutions. To address this problem, a point cloud registration algorithm is put forward in this study based on adaptive neighborhood eigenvalue loading ratios. In the algorithm, the resolution of the point cloud is first calculated and used as an adaptive basis to determine the raster widths and radii of spherical neighborhoods in the raster filtering; then, the adaptive raster filtering is implemented to the point cloud for denoising, while the eigenvalue loading ratios of point neighborhoods are calculated to extract and match the contour feature points; subsequently, sample consensus initial alignment (SAC-IA) is used to carry out coarse registration; and finally, a fine registration is delivered with KD-tree-accelerated ICP. The experimental results of this study demonstrate that the feature points extracted with this method are highly representative while consuming only 35.6% of the time consumed by other feature point extraction algorithms. Additionally, in noisy and low-overlap scenarios, the registration error of this method can be controlled at a level of 0.1 mm, with the registration speed improved by 56% on average over that of other algorithms. Taken together, the method in this study cannot only ensure strong robustness in registration but can also deliver high registration accuracy and efficiency.

A stereovision-based efficient measurement approach for surface flatness of concrete members

The surface flatness of concrete members is an important quality characteristic of buildings and civil infrastructure. Existing measurement methods such as the straightedge method, F-numbers method, and laser scanning method have various limitations such as inefficiency in engineering practices, low accuracy, and high costs. Therefore, this paper presents a novel stereovision-based system comprising two industrial cameras and one projector alongside optimized algorithms to efficiently measure the surface flatness of concrete member. The proposed system is highlighted by effective feature point extraction and matching algorithms, the innovative introduction of feature point extraction error to system measurement error evaluation, and enriched flatness indicators to comprehensively assess surface flatness of concrete members. The outputs of the system, namely, the contour maps as visual demonstrations and the flatness indicator results, can collectively reflect various roughness conditions with high speed and precision. Experiments at multiple levels have validated the proposed algorithms and measurement system, showing that it is capable for providing fast and accurate measures for the surface flatness of concrete members. Further research and developments are sought in system optimization and expanding its applications to other structural members and engineering practices.

Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass

ABSTRACT Objective It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass. Methods An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared. Results The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p < .05). Observation group demonstrated greater alveolar ridge elevation and bone augmentation versus control group, implying superior healing outcomes. Conclusion The feature point extraction algorithm is applicable for CBCT image processing in patients with insufficient maxillary bone mass. Autologous CGF exhibited superior therapeutic efficacy.

A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model

The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for tomato plants based on the VGG16-UNet model. The average intersection and pixel accuracies of the VGG16-UNet model after introducing the pretrained weights were 85.33% and 92.47%, respectively, which were 5.02% and 4.08% higher than those of the VGG16-UNet without pretrained weights, achieving the identification of main branches, side branches, and axillary bud regions. Then, based on the multi-objective segmentation of the tomato plants in the VGG16-UNet model, the regions of the axillary buds in the tomato plants were identified by HSV color space conversion and color threshold range selection. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The endpoints and centroids of the axillary buds were identified using the feature point extraction algorithm. The left and right positions of the axillary buds were judged by the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the coordinate parameters of the axillary bud removal points were calculated using the feature points to determine the relationship between the position of the axillary bud and the position of the branch. Experimental results showed that the average accuracy of the axillary bud pruning point recognition was 85.5%.

Comparison of Four Automatic Detection Algorithm with Initial Parameters for DICOM Artificial Brain Shift

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformities, known as brain shifts, can occur during surgery and deviate from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shift has not yet been elucidated and DICOM data acquired by MRI and CT for surgical planning and post-operative management are stored and archived at hospitals and discarded after a certain period. To address these issues, we started research around 2018 with the goal of modelling brain shifts. This will enable surgical planning to take brain shifts into account during pre-operative conferences. The corresponding OpenCV feature points are extracted from the pre-operative and post-operative DICOM and the brain shift is extracted from their motion vectors. Here, the feature point extraction algorithms BRISK, AKAZE, ORB, and SIFT are compared and it is experimentally confirmed that BRISK and AKAZE have better brain shift extraction capability than the other two algorithms.

LSFP-Tracker: An Autonomous Laser Stripe Feature Point Extraction Algorithm Based on Siamese Network for Robotic Welding Seam Tracking

Laser vision based welding seam tracking has become the key technology to realize intelligent robotic welding, and extracting the feature point of welding seam is an essential step. Aiming at the low flexibility of classical methods, this paper proposed a laser stripe feature point tracker (LSFP-tracker) based on siamese network for robotic welding seam tracking. Firstly, a lightweight feature extraction network (LSFENet) is constructed to produce high resolution feature maps that contain both low-level geometric details and high-level semantic information, which can ensure model's tracking accuracy and anti-noise capability. Moreover, a feature refinement module is designed, which can enhance the tracking stability on specific seams through channel-wise emphasizing useful features. Subsequently, stripe's curvature and three preset templates were introduced to adaptively detect the initial feature point and corresponding bounding box, which is helpful to enlarge the applicability scope of LSFP-tracker. The experimental results show that the LSFP-tracker can maintain satisfying tracking performance on various welding seams, such as butt, V-groove, multi-pass seams, etc., even under the interference of welding noise. And the comparison tests prove the advantage of LSFP-tracker, which has great application potential in practical welding.

The Transformation of Traditional Chinese Painting Elements in Modern Design Contexts

Abstract To ensure the effective preservation and evolution of traditional painting art, it is imperative to integrate contemporary art design concepts and methodologies. This paper focuses on the reconstruction of traditional Chinese painting elements, distilling and adapting its pattern, ink, and calligraphy components into a modern design framework. Specifically, the features of the pattern elements are identified and transformed using a feature point extraction algorithm combined with K-Means clustering. For ink and calligraphy elements, a fusion of self-attention mechanisms and Generative Adversarial Networks (GANs) facilitates the stylistic migration and conversion of these elements through the construction of distinct discriminators. Subsequent evaluation of the transformed painting elements utilized Importance-Performance Analysis (IPA) within a contemporary design context, aiming to assess the practical value of traditional painting elements in modern design. The findings indicate that the clarity of information (C1) and its accuracy and efficacy (C3) in the context of contemporary design achieved importance scores exceeding 3.6. Furthermore, the satisfaction scores for modeling (C10), color (C11), and imagery (C12) each surpassed 3.5, denoting a high level of satisfaction in the IPA of their conversion. The study enriches traditional artistic methods and conceptualizations, thereby enhancing their cultural depth and significance.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

With the development of applications related to image processing technology, the target requirements of data acquisition and analysis systems are constantly being optimized. Research on data acquisition and analysis based on the integrated SIFT (Scale Invariant Feature Transform) algorithm is becoming increasingly important. The construction of a data collection and analysis system focuses on ensuring efficient and accurate data collection and analysis to provide valuable insights and decision support. Key priorities include determining data requirements and objectives, designing appropriate data collection methods and tools, establishing reliable data storage and management systems, developing effective data analysis algorithms and models, and ensuring data security and privacy protection. This article analyzed the theoretical analysis of feature point extraction algorithm and the process steps and application of integrated SIFT algorithm. With the help of dimensionality reduction processing, the SIFT algorithm was improved and optimized. Combined with the scale space calculation formula, The conclusion is as follows: On the basis of image processing, the data collection and analysis system using the improved integrated SIFT algorithm on the five selected image file samples has improved the accuracy of feature point matching, with a comprehensive average improvement of 7.6%. The efficiency of feature point extraction has also been improved, with a comprehensive average improvement of about 8%. This indicated that the data collection and analysis system based on the improved integrated SIFT algorithm has good practical application results in feature point matching and extraction.

SASFF: A Video Synthesis Algorithm for Unstructured Array Cameras Based on Symmetric Auto-Encoding and Scale Feature Fusion.

For the synthesis of ultra-large scene and ultra-high resolution videos, in order to obtain high-quality large-scene videos, high-quality video stitching and fusion are achieved through multi-scale unstructured array cameras. This paper proposes a network model image feature point extraction algorithm based on symmetric auto-encoding and scale feature fusion. By using the principle of symmetric auto-encoding, the hierarchical restoration of image feature location information is incorporated into the corresponding scale feature, along with deep separable convolution image feature extraction, which not only improves the performance of feature point detection but also significantly reduces the computational complexity of the network model. Based on the calculated high-precision feature point pairing information, a new image localization method is proposed based on area ratio and homography matrix scaling, which improves the speed and accuracy of the array camera image scale alignment and positioning, realizes high-definition perception of local details in large scenes, and obtains clearer synthesis effects of large scenes and high-quality stitched images. The experimental results show that the feature point extraction algorithm proposed in this paper has been experimentally compared with four typical algorithms using the HPatches dataset. The performance of feature point detection has been improved by an average of 4.9%, the performance of homography estimation has been improved by an average of 2.5%, the amount of computation has been reduced by 18%, the number of network model parameters has been reduced by 47%, and the synthesis of billion-pixel videos has been achieved, demonstrating practicality and robustness.

A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects

Visual-based positioning technology plays a pivotal role in spatial artificial intelligence, facilitating precise perception and comprehension of the physical world for robotic platforms and augmented reality devices. In this study, we propose a binocular camera-based method for spatial localization of targets using CNN for instance segmentation while simultaneously providing target location information. The method encompasses image acquisition and correction, target recognition and segmentation, and stereo matching, among other components. Building upon this foundation, we introduce a pedestrian recognition segmentation network model with an attention mechanism. To accurately locate the target, we employ a multi-feature fusion feature point extraction and matching algorithm that combines edge information with semantic information. Finally, our proposed method is evaluated for dynamic pedestrian targets in indoor environments, achieving a horizontal positioning error of less than 0.25 m.

A comparison of feature extraction methods in image stitching

The usage of feature detectors for image stitching has become a popular research area in computer vision. Various feature extraction algorithms can be used in the process of image stitching process, but they perform varyingly when handling different images and no single algorithm could outperform all others. This paper focuses on the comparison of feature extraction algorithms used for panoramic image stitching. The research utilizes the SIFT, ORB, AKAZE, and BRISK to conduct feature points and match feature points on a group of image sets. The RANSAC algorithm is then used to filter out the outliers and calculate the homograph matrix. Completes the panoramic with image splicing and smoothing through the matrix transformation. Derived from the comparison of the matching and stitching results, the AKAZE detector is found to be the fastest feature point detection and extraction algorithm, while the SIFT detector will provide more feature points to make more accurate matches possible. These findings have implications for the development of efficient and effective computer vision technologies for various applications.

Machine vision–based automatic planning for microdroplet coating trajectory on irregularly shaped regions of metal surfaces

Metal surface coating technology plays a crucial role in various aspects of metal engineering such as enhancing metal oxidation resistance, corrosion prevention, insulation, decoration, and surface performance. This paper presents a machine vision–based automatic planning algorithm for microdroplet coating trajectory on irregularly shaped regions of metal surfaces, aiming to achieve uniform coating on different surface shapes and sizes. First, the process of microdroplet shape changes on metal surfaces is analyzed, the wetting circle diameter of a fully spread microdroplet is estimated, and the distance between two neighboring microdroplets is confirmed. Then, color pattern image processing (region contour extraction, feature point extraction, deburring, and equidistant offset algorithms) and template matching technology are developed to extract coating trajectory contours and accurately locate the workpieces on the workbench. To ensure a smooth and continuous coating trajectory, rational cubic Bézier curves are employed to interpolate the contour feature points, forming C1 continuous curves. Finally, taking into consideration the distance between two neighboring microdroplets, the continuous curves are equidistant offset and equichord discretized to determine the microdroplet locations. The experimental results demonstrate that the proposed algorithm successfully achieves arbitrary workpiece positioning, automatic planning of coating trajectory and uniform microdroplet coating on different surface shapes and sizes.

Research on the algorithm of image feature detection and matching

In-depth research on feature detection technology affects people's modern life. Modern artificial intelligence can act as the eyes of human beings and efficiently filter out effective information from complex pictures. Corner detection has now evolved into a tool for efficient image scanning. People's increasingly stringent requirements for image processing continue to promote the birth of new technologies. Corner detection methods have been improved and perfected, and have experienced detectors such as Harris, FAST, Scalriant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), KAZE, AKAZE and Oriented FAST and Rotated BRIEF (ORB). In the face of many mainstream detectors, the main research purpose of this paper is to rely on Python and Computer vision to study the detection efficiency of various detectors in different environments. In this experiment, thirteen groups of pictures were selected, and after being flipped, complicated, and blurred respectively, they were detected by different detectors to obtain the results. Finally, by comparing the feature detection points, detection time and other factors, this study found that the ORB detector can be competent in most situations and is currently the fastest and stable feature point detection and extraction algorithm. On the other hand, the BRISK detector can handle highly blurred images more efficiently.

Automated recognition and analysis of body bending behavior in C. elegans

BackgroundLocomotion behaviors of Caenorhabditis elegans play an important role in drug activity screening, anti-aging research, and toxicological assessment. Previous studies have provided important insights into drug activity screening, anti-aging, and toxicological research by manually counting the number of body bends. However, manual counting is often low-throughput and takes a lot of time and manpower. And it is easy to cause artificial bias and error in counting results.ResultsIn this paper, an algorithm is proposed for automatic counting and analysis of the body bending behavior of nematodes. First of all, the numerical coordinate regression method with convolutional neural network is used to obtain the head and tail coordinates. Next, curvature-based feature point extraction algorithm is used to calculate the feature points of the nematode centerline. Then the maximum distance between the peak point and the straight line between the pharynx and the tail is calculated. The number of body bends is counted according to the change in the maximum distance per frame.ConclusionExperiments are performed to prove the effectiveness of the proposed algorithm. The accuracy of head coordinate prediction is 0.993, and the accuracy of tail coordinate prediction is 0.990. The Pearson correlation coefficient between the results of the automatic count and manual count of the number of body bends is 0.998 and the mean absolute error is 1.931. Different strains of nematodes are selected to analyze differences in body bending behavior, demonstrating a relationship between nematode vitality and lifespan. The code is freely available at https://github.com/hthana/Body-Bend-Count.

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.

Research on Underwater Complex Scene SLAM Algorithm Based on Image Enhancement.

Underwater images typically suffer from less explicit feature point information and more redundant information due to wild conditions. To solve these degradation problems, we propose the VINS-MONO algorithm to enhance the quality of the underwater image. Specifically, we first used the FAST feature point extraction algorithm to improve the extraction speed. Then, the inverse optical flow method was used to improve the accuracy of feature extraction. At the same time, several kinds of residual information were extracted and marginalized, separately, in the marginalization part of the back-end, in order to improve the marginalization speed. Extensive experiments on underwater dataset HAUD-Dataset and public dataset EuRoC show that our approach is superior to the original VINS-MONO algorithm. In addition, the original algorithm optimizes the situation in which the feature point information is not obvious, and the redundant information is more complex in the underwater environment, which effectively improves the visual quality of the underwater image.

Research on Improved Algorithm of Mobile Robot Vision SLAM Based on Depth Camera

&lt;p&gt;Aiming at the problems of low accuracy, poor real-time performance and large cumulative error in traditional depth camera visual synchronization positioning and map construction algorithms, this paper proposes an improved visual SLAM algorithm for mobile robots based on depth camera. According to the realization process of robot vision, a feature point extraction algorithm based on feature extraction and image segmentation watershed algorithm is proposed in the front-end process, which improves the real-time performance of the algorithm. Then the RE-RANSAC algorithm is used to eliminate the mismatched feature points to improve the matching accuracy, and then the accumulated error is eliminated through closed loop detection, and finally the process of robot mapping is completed. After simulation experiments, the feasibility of the improved algorithm is proved, and the robot’s mapping and trajectory estimation are completed.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

A depth-colour image registration method based on local feature point extraction

A binocular camera composed of colour and depth cameras is an accurate and inexpensive tool for recognising fruit trees to plan spraying paths. For this purpose, however, the accurate visual registration of the depth and colour cameras is a problem that must be solved. This paper proposes a local feature point extraction (LFPE) algorithm for depth-colour image registration, which automatically extracts the feature points of local regions in the colour and depth images for registration. First, the binarised colour and depth images are divided into local regions after edge extraction, and the regions in which the edge features match at several pixels between images are selected. Then, these regions are again divided into smaller regions to detect local extrema of the pixels as feature points. Finally, the matching feature points are calculated based on the Euclidean distance and are used to optimise the affine transformation matrix. To verify the accuracy and effectiveness of the algorithm, it is compared with the FAST, SURF and BRISK algorithms. The experimental results show that the accuracy of the proposed LFPE algorithm is 1.6 times that of BRISK, 3.5 times that of FAST and 40.1 times that of SURF. Therefore, it is more suitable for registration between depth and colour images. • Proposed a depth-colour image registration method for orchard precision spraying. • A local feature point extraction (LFPE) algorithm is proposed to match images. • The registration accuracy of LFPE is much better than other similar algorithms.