Oriented FAST And Rotated BRIEF Algorithm Research Articles

A novel occluded face detection approach using Enhanced ORB and optimized GAN

Objectives:This research presents an approach to detect occluded face images. In order to achieve this, the research presents a novel technique that involves feature extraction and occluded face recognition. Methods: Feature extraction is performed by the enhanced ORB algorithm, which is proposed by the modification of the Oriented Fast and Rotated Brief (ORB) algorithm, by adding a phase for contrast adjustment, together with CNN features. For occluded face recognition, a Generative Adversarial Network (GAN) optimized by the proposed SR-SSA is designed. SR-SSA is proposed by the integration of Search and Rescue Optimization (SAR) in the Sparrow Search Algorithm (SSA). Results: The experimental results demonstrate that the SR-SSA-based GAN algorithm outperforms existing methods in terms of accuracy of 0.956, FAR of 0.045 and FRR of 0.021.

An Improved Visual SLAM Method with Adaptive Feature Extraction

The feature point method is the mainstream method to accomplish inter-frame estimation in visual Simultaneous Localization and Mapping (SLAM) methods, among which the Oriented FAST and Rotated BRIEF (ORB) feature-based method provides an equilibrium of accuracy as well as efficiency. However, the ORB algorithm is prone to clustering phenomena, and its unequal distribution of extracted feature points is not conducive to the subsequent camera tracking. To solve the above problems, this paper suggests an adaptive feature extraction algorithm that first constructs multiple-scale images using an adaptive Gaussian pyramid algorithm, calculates adaptive thresholds, and uses an adaptive meshing method for regional feature point detection to adapt to different scenes. The method uses Adaptive and Generic Accelerated Segment Test (AGAST) to speed up feature detection and the non-maximum suppression method to filter feature points. The feature points are then divided equally by a quadtree technique, and the orientation of those points is determined by an intensity centroid approach. Experiments were conducted on publicly available datasets, and the outcomes demonstrate the algorithm has good adaptivity and solves the problem of a large number of corner point clusters that may result from using manually set detection thresholds. The RMSE of the absolute trajectory error of SLAM applying this method on four sequences of TUM RGB-D datasets is decreased by 13.88% when compared with ORB-SLAM3. It is demonstrated that the algorithm provides high-quality feature points for subsequent image alignment, and the application to SLAM improves the reliability and accuracy of SLAM.

SMCEWS: Binary Robust Multicentre Features

The Oriented FAST and Rotated BRIEF (ORB) algorithms have been improved, and a new method for calculating descriptors based on symmetrical multicentric weighted binary encoding is proposed, which enhances the robustness of feature points. This method employs a string of binary descriptors to encode the features and uses the multiple descriptor centre strategy to sample descriptors at the feature point and on the symmetrical circumference around it. Furthermore, a weighted summation is introduced in the descriptor calculation process to address the noise in the image during the sampling process. Specifically, the pixel values around the sampled point and the sampled point itself are combined using a certain weight to produce the final pixel value of the sampled point. The reliability of the descriptor is enhanced by introducing the pixel information around the sample point while solving the noise problem. Our method makes full use of the pixel information in the various parts of the descriptor sampling region to improve the distinguishability of the descriptors. We compare it with the ORB algorithm and experimentally show that the feature extraction method achieves better matching results with almost constant computation time.

An Improved ORB Feature Extraction Algorithm Based on Enhanced Image and Truncated Adaptive Threshold

The ORB (Oriented fast and rotated brief, ORB) algorithm is limited by its inability to extract feature points or extract only a small number of feature points when a fixed threshold is used in complex lighting conditions. To address this issue, this study proposes a method that combines image enhancement with truncated adaptive threshold to improve the ORB feature extraction algorithm. Firstly, the original image is converted to grayscale. Secondly, the image is enhanced by applying Gaussian filtering for noise reduction, truncated adaptive gamma brightness adjustment, and unsharp masking operation. Then, the enhanced image is segmented into subregions of a specified size, and an improved truncated OTUS method is employed to calculate the adaptive threshold for each subregion. Finally, ORB feature points are extracted using the adaptive threshold. Experimental results show that the improved ORB algorithm can significantly increase the number of feature points in complex lighting conditions, with good accuracy, real-time performance, and robustness.

Application of machine vision image feature recognition in 3D map construction

Simultaneous localization and mapping algorithm is a technique to simultaneously construct a map of the environment by the sensors it carries. In this paper, we focus on the SLAM (Simultaneous Localization and Mapping) algorithm based on machine vision-depth information, overlap region determination among local sub-maps, and fusion algorithm of 3D dense maps, and analyze four modules of front-end visual odometry, back-end optimization, loopback detection, and map construction. The front-end visual odometry adopts the ORB (Oriented FAST and Rotated BRIEF) algorithm based on the feature point method to extract and match the features between image frames and uses the iterative nearest point method to solve the camera motion between adjacent images. Meanwhile, a 3D feature descriptor based on the histogram of spatial distribution is designed to encode the feature information around the key points, and the Kd-Tree algorithm is used for radial nearest neighbor search to complete the feature matching. Finally, the 3D map is fused by the 3D-ICP algorithm. The experiments prove that the low-dimensional local feature descriptor designed has good descriptiveness and is superior, and the 3D map fusion based on this descriptor has a good fusion effect and can meet the accuracy requirements of practical applications.

Research on Positioning Method in Underground Complex Environments Based on Fusion of Binocular Vision and IMU.

Aiming at the failure of traditional visual slam localization caused by dynamic target interference and weak texture in underground complexes, an effective robot localization scheme was designed in this paper. Firstly, the Harris algorithm with stronger corner detection ability was used, which further improved the ORB (oriented FAST and rotated BRIEF) algorithm of traditional visual slam. Secondly, the non-uniform rational B-splines algorithm was used to transform the discrete data of inertial measurement unit (IMU) into second-order steerable continuous data, and the visual sensor data were fused with IMU data. Finally, the experimental results under the KITTI dataset, EUROC dataset, and a simulated real scene proved that the method used in this paper has the characteristics of stronger robustness, better localization accuracy, small size of hardware equipment, and low power consumption.

Color Image Feature Matching Method Based on the Improved Firework Algorithm

An improved ORB feature point purification method has been proposed to address the problem of large feature point matching error and low image registration rate in the oriented FAST and rotated BRIEF (ORB) feature point color image matching algorithm. Pure virtual quaternions were used to represent the color image pixels in this method, and an improved FAST algorithm was used to detect the color feature points at first. The firework algorithm has been used to divide the detected feature points into key areas, auxiliary areas, and small influence areas, depending on the degree of attachment. The feature points of the key area and the subsidiary area are the required feature points. In order to further improve the efficiency of matching the color image feature points, the firework explosion radius formula and the explosion number formula in the firework algorithm have been improved, and an improved firework algorithm is proposed. This improved algorithm purifies the quaternion-represented color image feature points. For feature matching, the hamming distance was used. Experiments show that, when compared to the traditional ORB algorithm, the improved algorithm retains the key feature points of the color image with high accuracy, removes a large number of irrelevant feature points and noise points, and provides significantly higher accuracy and efficiency of color image matching.

Measuring Method of Slip Ratio for Tractor Driving Wheels Based on Machine Vision

Tractors are prone to large slips when they are in field operation. The degree of slip plays a vital role in traction efficiency and fuel efficiency. This paper presents a method for measuring the slip ratio of tractors in field operation based on machine vision. The accurate measurement of slip ratio needs to obtain actual velocity and theoretical velocity separately. For obtaining the actual velocity, a monocular camera mounted on the tractor vertically faces down at the ground to collect images. Then, the feature points of inter-frame ground images are matched by the ORB (Oriented FAST and Rotated BRIEF) algorithm for calculating the translational displacement. Next, a homography matrix based on camera calibration is proposed to complete the transformation of a point from the pixel coordinate system to the world coordinate system. Aiming to acquire the theoretical velocity, a method that takes the variations in tire radius into account is proposed, and the tire radii of the driving wheels are indirectly determined by the tire inflation pressure in real-time. The proposed measurement method was verified with an experimental tractor. The results show that the mean absolute errors of the tractor driving wheels’ slip ratio measured by the machine vision method are less than 0.75%, and the maximum of the absolute errors is not more than 2.22%, which shows good performance.

Fast Target Recognition Based on Improved ORB Feature

A novel fast target recognition algorithm is proposed under the dynamic scene moving target recognition. Aiming at the poor matching effect of the traditional Oriented Fast and Rotated Brief (ORB) algorithm on underexposed or overexposed images caused by illumination, the idea of combining adaptive histogram equalization with the ORB algorithm is proposed to get better feature point quality and matching efficiency. First, the template image and each frame of the video stream are processed by grayscale. Second, the template image and the image to be input in the video stream are processed by adaptive histogram equalization. Third, the feature point descriptors of the ORB feature are quantized by the Hamming distance. Finally, the K-nearest-neighbor (KNN) matching algorithm is used to match and screen feature points. According to the matching good feature point logarithm, a reasonable threshold is established and the target is classified. The comparison and verification are carried out by experiments. Experimental results show that the algorithm not only maintains the superiority of ORB itself but also significantly improves the performance of ORB under the conditions of underexposure or overexposure. The matching effect of the image is robust to illumination, and the target to be detected can be accurately identified in real time. The target can be accurately classified in the small sample scene, which can meet the actual production requirements.

Prototipe Detektor Mata Uang Kertas Untuk Modul Vending Machine Automatic Cash Money Menggunakan Rapsberry Pi

Vending Machines are strategic trading machines when it comes to social and physical distancing policies during the current Covid 19 pandemic. The ability to trade independently in trading without a physical meeting is one of its advantages. On the other hand, the Vending Machine payment system has developed with many variations ranging from payment models using coins, banknotes, payment gateway systems capable of serving debit and credit payments and the ability to make payments via fintech. This study focuses on the banknote detection module using the Oriented FAST and Rotated BRIEF (ORB) algorithm. In order to become a prototype that is ready to be developed in the future, the banknote detection module needs to be made in a concise and independent form. The use of the Rapsberry Pi and the ability to work on complex algorithms is one of the goals that will be achieved from this research. This research is expected to be able to apply the ORB algorithm for the classification of Rupiah banknotes and at the same time be able to further develop it to sort (sort) for banknotes that meet the requirements (accept) and reject those that do not meet the requirements (reject).

Person Verification Based on Multimodal Biometric Recognition

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.

An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques

This paper presents a fast and accurate system to determine the type of blood automatically based on image processing. Blood type determination is important in emergency situations, where there is a need for blood transfusion to save lives. The traditional blood determination techniques are performed manually by a specialist in medical labs, where the result requires a long time or may be affected by human error. This may cause serious consequences or even endanger people’s lives. The proposed approach performs blood determination in real-time with low cost using any available mobile device equipped with a camera. A total of 500 blood samples were processed in this study using different image matching techniques including oriented fast and rotated brief (ORB), scale invariant feature transform (SIFT), and speed-up robust feature (SURF). The evaluation results show that our proposed system, which adopts the ORB algorithm, is the fastest and the most accurate among the state-of-the-art systems. It can achieve an accuracy of 99.6% in an average time of 250 ms.

Homogenized ORB Algorithm Using Dynamic Threshold and Improved Quadtree

The Oriented FAST and Rotated BRIEF (ORB) algorithm has the problem that the extracted feature points are overconcentrated or even overlapped, leading to information loss of local image features. A homogenized ORB algorithm using dynamic thresholds and improved quadtree method is proposed in this paper, named Quadtree ORB (QTORB). In the feature point extraction stage, a new dynamic local threshold calculation method is proposed to enhance the algorithm’s ability to extract feature points at homogeneous regions. Then, a quadtree method is improved and adopted to manage and optimize feature points to eliminate those excessively concentrated and overlapping feature points. Meanwhile, in the feature points optimization process, different quadtree depths are set at different image pyramid levels to prevent excessive splitting of the quadtree and increase calculation speed. In the feature point description stage, local gray difference value information is introduced to enhance the saliency of the feature description. Finally, the Hamming distance is used to match points and RANSAC is used to avoid mismatches. Two datasets, namely, the optical image dataset and SAR image dataset, are used in the experiment. The experimental result shows that, considering accuracy and real-time efficiency, the QTORB can effectively improve the distribution uniformity of feature points.

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.

IMAGE MOSAIC ALGORITHM BASED ON PCA-ORB FEATURE MATCHING

Abstract. In the process of image stitching, the ORB (Oriented FAST and Rotated BRIEF) algorithm lacks the characteristics of scale invariance and high mismatch rate. A principal component invariant feature transform (PCA-ORB, Principal Component Analysis- Oriented) is proposed. FAST and Rotated BRIEF) image stitching method. Firstly, the ORB algorithm is used to optimize the feature points to obtain the feature points with uniform distribution. Secondly, the principal component analysis (PCA) method can reduce the dimension of the traditional ORB feature descriptor and reduce the complexity of the feature point descriptor data. Thirdly, KNN (K-Nearest Neighbor) is used, and the k-nearest neighbor algorithm performs roughly matching on the feature points after dimensionality reduction. Then the random matching consistency algorithm (RANSAC, Random Sample Consensus) is used to remove the mismatched points. Finally, the fading and fading fusion algorithm is used to fuse the images. In 8 sets of simulation experiments, the image stitching speed is improved relative to the PCA-SIFT algorithm. The experimental results show that the proposed algorithm improves the image stitching speed under the premise of ensuring the quality of stitching, and can play a role in fast, real-time and large-scale applications, which are conducive to image fusion.

UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM

Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.

A Method for Real-Time Patient Positioning Verification in Radiotherapy Using Multi-Angular Images with Conventional Video Cameras

Background: Accurate patient positioning and the corresponding position verification play a key role in precision radiotherapy. The conventional position verification techniques, such as cone-beam computed tomography (CBCT), used in radiotherapy deliver additional radiation doses to patients; thus some complications may occur in the surrounding normal tissue. Additionally, the present registration and fusion algorithms require a long time to verify and monitor the patient's position in real-time. Method: A new simple method using three conventional video cameras from different directions for observing the changes in the patient's position and taking the multiangular surface images of the patient without delivering additional doses of radiation is proposed in this paper. Real-time patient position verification could be achieved through this Oriented FAST and Rotated BRIEF (ORB) algorithm modified by us. Results: A plastic patient model immobilized with thermoplastic shell on a simulation couch was used to verify the accuracy and feasibility of our method. Testing results showed that the position and angle offset errors identified with our system were less than 1 mm and 1°, respectively. Moreover, the modified ORB algorithm was fast enough for real-time images comparison. Conclusion: This proposed method shows a great potential for application in verifying and monitoring the patient's position in real time during radiotherapy.

Real-time correction of image drift in scanning electron microscope

In order to solve the problem of imaging drift in scanning electron microscope (SEM) that caused by electron beam drift, electromagnetic interference and other reasons, an image shift correction algorithm based on ORB (oriented FAST and rotated BRIEF) combing the PROSAC (progressive sample consensus) is proposed in this paper. Firstly, the ORB algorithm is used to detect the feature between the reference image and real-time image. Then the initial matching of the feature is implemented by using the Hamming distance and cross-matching. Moreover, the RANSAN (random sample consensus) optimization algorithm PROSAC is used to calculate the homography matrix between frames and the final exact homography matrix is re-iterated after eliminating exterior point. Finally, the SEM image drift is corrected in real time using the perspective transformation of the homography matrix. The experiments show that the proposed algorithm is high precision and satisfies the requirement of SEM real-time processing.

Effective Descriptors based Face Recognition Technique for Robotic Surveillance Systems

The aim is to propose SPHORB Face Recognition technique for Robotic Surveillance systems. Surveillance bots with efficient face recognition system becomes even more powerful and adds to the applications of the bots. In this paper we compare the results of face recognition using SPHORB (a new fast and robust binary feature detector and descriptor for spherical panoramic image) [1] algorithm with ORB (Oriented Fast and Rotated Brief) [4] algorithm for classifying 2D and 3D images Database. In the first part 2D face images are randomly selected from LFW [32] with good sample of images across gender and ethnicity. The number of keypoints that were identified and number of matching keypoints between the 2D images using SPHORB and ORB were compared. From the results we determine that the SPHORB algorithm has identified more keypoints for the same 2D image than ORB, it has processed more images but ORB gives better accuracy than SPHORB for 2D images. In the next part we investigated the image matching between a front face 2D image with a 3D image. The front face image and 3D images are randomly selected from the ThatsMyFace.com [2] with good sample of images across genders, ethnicity and age. The front face image is compared with a randomly selected 3D image from the sample class set using SPHORB and ORB algorithms. The number of keypoints that were identified and number of matching keypoints between images using SPHORB and ORB were compared. From the results we determine that the SPHORB algorithm has identified more keypoints for the same image than ORB, it has processed more images and has accuracy comparable to the ORB algorithm.

Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery

Abstract. A computationally efficient, open-source feature-tracking algorithm, called ORB, is adopted and tuned for sea ice drift retrieval from Sentinel-1 SAR (Synthetic Aperture Radar) images. The most suitable setting and parameter values have been found using four Sentinel-1 image pairs representative of sea ice conditions between Greenland and Severnaya Zemlya during winter and spring. The performance of the algorithm is compared to two other feature-tracking algorithms, namely SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features). Having been applied to 43 test image pairs acquired over Fram Strait and the north-east of Greenland, the tuned ORB (Oriented FAST and Rotated BRIEF) algorithm produces the highest number of vectors (177 513, SIFT: 43 260 and SURF: 25 113), while being computationally most efficient (66 s, SIFT: 182 s and SURF: 99 s per image pair using a 2.7 GHz processor with 8 GB memory). For validation purposes, 314 manually drawn vectors have been compared with the closest calculated vectors, and the resulting root mean square error of ice drift is 563 m. All test image pairs show a significantly better performance of the HV (horizontal transmit, vertical receive) channel due to higher informativeness. On average, around four times as many vectors have been found using HV polarization. All software requirements necessary for applying the presented feature-tracking algorithm are open source to ensure a free and easy implementation.