Binary Robust Independent Elementary Features Research Articles

Comprehensive empirical evaluation of feature extractors in computer vision

Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications. This study offers a comprehensive evaluation of traditional feature detections and descriptors, analyzing methods such as Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Invariant Scalable Keypoints (BRISK), KAZE, Accelerated KAZE (AKAZE), Fast Retina Keypoint (FREAK), Dense and Accurate Invariant Scalable descriptor for Yale (DAISY), Features from Accelerated Segment Test (FAST), and STAR. Each feature extractor was assessed based on its architectural design and complexity, focusing on how these factors influence computational efficiency and robustness under various transformations. Utilizing the Image Matching Challenge Photo Tourism 2020 dataset, which includes over 1.5 million images, the study identifies the FAST algorithm as the most efficient detector when paired with the ORB descriptor and Brute-Force (BF) matcher, offering the fastest feature extraction and matching process. ORB is notably effective on affine-transformed and brightened images, while AKAZE excels in conditions involving blurring, fisheye distortion, image rotation, and perspective distortions. Through more than 2 million comparisons, the study highlights the feature extractors that demonstrate superior resilience across various conditions, including rotation, scaling, blurring, brightening, affine transformations, perspective distortions, fisheye distortion, and salt-and-pepper noise.

Real-time indoor tracking for augmented reality using computer vision technique

In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces

Image quality evaluation: evaluation of the image quality of actual images by using machine learning models

Evaluating image features is a significant step in image processing in applications like number plate detection, vehicle tracking and many image processing-based applications. Image processing-based applications need accurate parts to get the best outcomes. Feature detection is done based on various feature detection techniques. The proposed system aims to get the best feature detector based on the input images by evaluating the image features. For assessing the image features, the proposed system worked on various descriptors like oriented FAST and rotated brief (ORB), learned arrangements of three patch codes (LATCH), binary robust independent elementary features (BRIEF), and binary robust invariant scalable keypoints (BRISK) to extract and evaluate the features using K-nearest neighbor (KNN)-matching and retrieve the inliers of the matching. Each descriptor produces different matching features and inliers; with the matchings and inliers, the inlier ratio calculates to show the analysis. To increase performance, we also examine adding depth information to descriptors.

Visual Odometry Based on Improved Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features

To address the problem of system instability during vehicle low-speed driving, we propose improving the visual odometer using ORB (Oriented FAST and Rotated BRIEF) features. The homogeneity of ORB features leads to poor corner point properties of some feature points. When the environmental texture lacks richness, it leads to poor matching performance and low matching accuracy of the feature points. We solve the problem of the corner point properties of feature points using weight calculation for regions with different textures. When the vehicle speed is too low, the continuous frames captured by the camera will overlap significantly, causing large fluctuations in the system error. We use motion model estimation to solve this problem. Meanwhile, experimental validation using the KITTI dataset achieves good results.

A Synchronous Localization Method for Multiple Mobile Robots Based on Visual SLAM Algorithm in the Context of the Internet of Things

Abstract Aiming at the problem that traditional methods cannot meet the problem of synchronous positioning of multi-mobile robots in different motion states, a synchronous positioning method of multi-mobile robots based on visual simultaneous localization and mapping (SLAM) algorithm is proposed. Analyze the general model of SLAM problem, realize visual SLAM feature detection through scale invariant feature transform algorithm, and match image features based on active vision. A multi-mobile robot motion state estimation model is constructed, an octree model is used to construct a multi-robot synchronous localization map, and the camera pose tracking is realized by using the oriented fast accelerated segment test and rotated binary robust independent elementary features points that are not in the target bounding box. The three-dimensional points in the environment recovered by visual SLAM separate the background target and the moving target, and match the corresponding target in the previous frame through the feature to realize the synchronous positioning of the multi-mobile robot. The experimental results show that the method can realize the synchronous positioning of multi-robots under the state of multi-robot linear motion, curvilinear motion and mixed motion, and the positioning accuracy is high.

Convex-based lightweight feature descriptor for Augmented Reality Tracking.

Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. Noteworthy is CBFD's recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. Finally, the plot of location feature distance illustrates that CBFD exhibits minimal distance compared to DITF and Histogram of Oriented Gradients (HOG). These results highlight the speed and robustness of CBFD across various transformations.

An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis

To propose a bio-inspired multi-variant feature extraction technique in retinal fundus images to improve glaucoma detection for easy diagnosis. To improve the classification accuracy of glaucoma disease, a machine learning approach is employed based on the features extracted in RFI. Methods: The Fish School Search MOFS (FSS-MOFS) bio-inspired technique is employed to extract the multivariable features from digital RFI and spot the glaucoma variations to maximize the discrimination power and minimize the dimensionality. The Binary Robust Independent Elementary Feature (BRIEF-LD) technique is employed to identify the latent spots in RFI. The PAPILA dataset is utilized for this research study, which includes the clinical records of 244 patients and 488 images of the right and left eye of all the patients under 2 categories (male and female) with results in healthy, glaucoma, and suspect categories. In order to perform deep pixel identification and RFI segmentation, the Densenet-121 architecture is used along with a deep-lab model to incarcerate the built-in fine points of RFI with the help of dilated convolutions. The MATLAB tool is used to assess the suggested bio-inspired technique, and comparative analysis is done against the prevailing glaucoma detection and feature extraction methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. Findings: The suggested FSS-MOFS multi-variant feature extraction technique outperforms the prevailing ML methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. The result of the comparative study shows 94% accuracy, 96% sensitivity, 97% specificity, 96% F-measure, 96.8% Precision, 97% recall, 95% detection speed, 92.06% TPR, 92.15% TNR, and 8% FPR & FNR. Novelty: The promising result of the FSS-MOFS method boosts the accuracy level of glaucoma detection and classification compared to other classification techniques. The suggested model helps the ophthalmologists identify and diagnose glaucoma in a robust way. FSS-MOFS overcomes the shortcomings of prevailing feature extraction methods.

Hybrid Navigation of an Autonomous Mobile Robot to Depress an Elevator Button

The development of an autonomous mobile robot (AMR) with an eye-in-hand robot arm atop for depressing elevator button is proposed. The AMR can construct map and perform localization using the ORB-SLAM algorithm (the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features) feature detector-Simultaneous Localization and Mapping). It is also capable of real-time obstacle avoidance using information from 2D-LiDAR sensors. The AMR, robot manipulator, cameras, and sensors are all integrated under robot operating system (ROS). In experimental investigation to dispatch the AMR for depressing an elevator button, AMR navigation initiating from the laboratory is divided into three parts. First, the AMR initiated navigation using ORB-SLAM for most of the journey to a waypoint nearby the elevator. The resulting mean absolute error (MAE) is 8.5 cm on the x-axis, 10.8 cm on the y-axis, 9.2-degree rotation angle about the z-axis, and the linear displacement from the reference point is 15.1 cm. Next, the ORB-SLAM is replaced by an odometry based 2D-SLAM method for further navigating the AMR from waypoint to a point facing the elevator between 1.5 to 3 meter distance, where the ORB-SLAM is ineffective due to sparse feature points for localization and where the elevator can be clearly detected by an eye-in-hand machine vision onboard the AMR. Finally, the machine vision identifies space position of the elevator and again the odometry based 2D SLAM method is employed for navigating the AMR to front of the elevator between 0.3 to 0.5 meter distance. Only at this stage, the small-size elevator button can be detected and reached by the robot arm on the AMR. An averaged 60% successful rate of button depressing by the AMR starting at the laboratory is obtained in the experiments. Improvements of successful elevator button depressing rate are also pointed out.

An accurate novel circular hole inspection method for sheet metal parts using edge-guided robust multi-view stereo

Sheet metal parts are frequently used in the industrial field, and the circular holes on these parts are commonly employed as assembly references. Therefore, precise measurement of these circular holes is essential. Because of the scratches, rust, and complex reflection around the edges, the measurement of circular holes on sheet metal parts is a challenging task. In this research, an accurate circular hole inspection method for sheet metal parts is proposed to realize online inspection using a multi-camera system. After capturing images in a single shot, edge detection is adopted to obtain sub-pixel circular hole contours. A new adaptive matching cost based on the descriptor of binary robust independent elementary features and bilinear normalized cross-correlation is introduced to the PatchMatch-based multi-view stereo method to obtain dense and accurate point clouds. To improve the accuracy and integrity of circular hole measurement, a new edge-guided multi-scale geometric consistency matching strategy is proposed accompanied by depth detail restoration. Finally, the points around the circular hole in a spherical range are sampled using the trinocular constraint principle and fitted with the circular equation. The experimental results show that the proposed algorithm performs well, with an error of approximately 0.04 mm, and improves the measurement accuracy compared with the state-of-the-art algorithms.

BRIEFBCS: binary robust independent elementary features based fuzzy vault scheme in BCS

BRIEFBCS: binary robust independent elementary features based fuzzy vault scheme in BCS

UVS: underwater visual SLAM—a robust monocular visual SLAM system for lifelong underwater operations

AbstractIn this paper, a visual simultaneous localization and mapping (VSLAM/visual SLAM) system called underwater visual SLAM (UVS) system is presented, specifically tailored for camera-only navigation in natural underwater environments. The UVS system is particularly optimized towards precision and robustness, as well as lifelong operations. We build upon Oriented features from accelerated segment test and Rotated Binary robust independent elementary features simultaneous localization and mapping (ORB-SLAM) and improve the accuracy by performing an exact search in the descriptor space during triangulation and the robustness by utilizing a unified initialization method and a motion model. In addition, we present a scale-agnostic station-keeping detection, which aims to optimize the map and poses during station-keeping, and a pruning strategy, which takes into account the point’s age and distance to the active keyframe. An exhaustive evaluation is presented to the reader, using a total of 38 in-air and underwater sequences.

Development of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Images

Product recognition on grocery shelf images is a compelling task of object detection because of the similarity between products, the presence of the different scale of product sizes, and the high number of classes, in addition to constantly renewed packaging and added new products’ difficulty in data collection. The use of conventional methods alone is not enough to solve a number of retail problems such as planogram compliance, stock tracking on shelves, and customer support. The purpose of this study is to achieve significant results using the suggested multi-stage end-to-end process, including product detection, product classification, and refinement. The comparison of different methods is provided by a traditional computer vision approach, Aggregate Channel Features (ACF) and Single-Shot Detectors (SSD) are used in the product detection stage, and Speed-up Robust Features (SURF), Binary Robust Invariant Scalable Key points (BRISK), Oriented Features from Accelerated Segment Test (FAST), Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB), and hybrids of these methods are used in the product classification stage. The experimental results used the entire Grocery Products dataset and its different subsets with a different number of products and images. The best performance was achieved with the use of SSD in the product detection stage and the hybrid use of SURF, BRISK, and ORB in the product classification stage, respectively. Additionally, the proposed approach performed comparably or better than existing models.

Image matching algorithm based on improved AKAZE and Gaussian mixture model

Simultaneous localization and mapping (SLAM) technology has gained popularity as a result of technological advancements, and image matching—the labor-intensive and crucial component of SLAM technology—has emerged as a key area of research. Hence, the purpose of this study is to develop an image matching method based on improved accelerated-KAZE (AKAZE) and Gaussian mixed model (GMM) segmentation to guarantee the successful completion of subsequent vision challenges. (i) For feature extraction and feature description, first, the AKAZE algorithm was used to extract the key points of the gray-scale image, and second, the binary robust independent elementary features (BRIEF) algorithm was used to obtain the descriptors. Introduction of gray-scale prime method and convolution kernel function to enhance the robustness of features in the BRIEF calculation process. (ii) For feature matching, the gray-scale image is pseudocolored, and then all the pixels in its R channel are segmented by GMM, retaining the features in the same segmented region after brute-force matching. The experimental results demonstrate that, in comparison to oriented FAST and rotated BRIEF and AKAZE, the proposed algorithm in this work provides higher matching accuracy for images of different data sets. That is, the matching approach can be used more effectively for subsequent vision tasks.

Performance Evaluation of State-of-the-Art Texture Feature Extraction Techniques on Medical Imagery Tasks

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.

Development of an Omnidirectional AGV by Applying ORB-SLAM for Navigation Under ROS Framework

The development of an automated guided vehicle with omni-wheels for autonomous navigation under a robot operating system framework is presented in this paper. Specifically, a laser rangefinder-constructed two-dimensional environment map is integrated with a three-dimensional point cloud map to achieve real-time robot positioning using the oriented features from accelerated segment test and rotated binary robust independent elementary feature detector-simultaneous localisation and mapping algorithm. In the path planning for autonomous navigation of the omnidirectional mobile robot, we applied the A* global path search algorithm, which uses a heuristic function to estimate the robot position difference and searches for the best direction. Moreover, we employed the time-elastic-band method for local path planning, which merges the time interval of two locations to realise time optimisation for dynamic obstacle avoidance. The experimental results verified the effectiveness of the applied algorithms for the omni-wheeled mobile robot. Further, the results showed a superior performance over the adaptive Monte Carlo localisation for robot localisation and dynamic window approach for local path planning.

Texture-Mapping Error Removal Based on the BRIEF Operator in Image-Based Three-Dimensional Reconstruction

In image-based three-dimensional (3D) reconstruction, texture-mapping techniques can give the model realistic textures. When the geometric surface in some regions is not reconstructed, such as for moving cars, powerlines, and telegraph poles, the textures in the corresponding image are textured to other regions, resulting in errors. To solve this problem, this letter proposes an image consistency detection method based on the Binary Robust Independent Elementary Features (BRIEF) descriptor. The method is composed of two parts. First, each triangle in the mesh and its neighboring triangles are sampled uniformly to obtain sampling points. Then, these sampled points are projected into the visible image of the triangle, and the corresponding sampled points and their RGB color values are obtained on the corresponding image. Based on the sampled points on these images, a BRIEF descriptor is calculated for each image corresponding to that triangle. In the second step, the Hamming distance between these BRIEF descriptors is calculated, outliers are removed according to the method, and noisy images are also removed. In addition, we propose adding semantic information to Markov energy optimization to reduce errors further. The two methods effectively reduced errors in texture mapping caused by objects not reconstructed, improving the texture quality of 3D models.

Automated SAR Image Segmentation and Classification Using Modified Deep Learning

Synthetic Aperture Radar (SAR) represents a type of active remote sensing technology that uses microwave electromagnetic radiation to produce and send data to the surface of a target location. SAR imaging is frequently used in national security applications since it is unaffected by weather, geographical location, or time. In this system, many approaches are examined, to improve automation for segmentation and classification. The utilization of Deep Neural Networks (DNNs) to classify SAR images has gotten a lot of attention, and it usually requires several layers of deep models for feature learning. With insufficient training data, however, the DNN will get affected by the overfitting issue. The major purpose of this work is to make a development on introducing a new framework for SAR image segmentation and categorization using deep learning. Owing to the coherent nature of the backscattering signal, SARs create speckle noise in their images. If the image has noisy material, classification becomes more challenging. Hence, the pre-processing of the images is employed by linear spatial filtering to remove the noise. Further, the Optimized U-Net is used for the segmentation. For the segmented images, the Binary Robust Independent Elementary Features (BRIEF) concept is adopted as the feature descriptor. These features are inputted to the Convolutional Neural Network (CNN) with Tuned Weight DNN (C-TWDNN) for the classification. In both segmentation and classification, the parameter tuning is employed by the combination of Galactic Swarm Optimization (GSO) and Deer Hunting Optimization Algorithm (DHOA) called the Self-adaptive-Galactic Deer Hunting Optimization (SA-GDHO). Experiments are conducted on a variety of public datasets, demonstrating that our method is capable of outperforming various expert systems and deep structured architectures.

Feature Descriptor based on Differential Evolution for Visual Navigation in Dynamic Environments

This work proposes to use a teach-and-repeat method to navigate in an outdoor environment. Instead of using state-of-the-art feature point descriptors to match the current images to the database, we rely on the Generated Binary Robust Independent Elementary Features (GRIEF), a model robust to changes in illumination and weather. These descriptors are obtained via a training process employing evolutionary methods and binary comparison tests. The objective is to obtain visual feature descriptors designed for a given task. For the teach-and-repeat task, they are trained to optimize the estimate of the robot heading error with respect to a learned path. In this work, we propose to investigate how Differential Evolution (DE) can improve the performance of the GRIEF in terms of the convergence rate during the training phase and of fitness score. The new approach, named GRIEF-DE, is trained with the Michigan data set, made of pictures of an outdoor environment with significant changes in appearance caused by seasonal weather variations and illumination, and compared with the original GRIEF. The proposed model was applied to 4 different data sets and the experimental results suggest that the use of Differential Evolution improves the training performance as well as the estimation of the robot orientation with respect to the path of reference.

A monocular SLAM system based on SIFT features for gastroscope tracking

During flexible gastroscopy, physicians have extreme difficulties to self-localize. Camera tracking method such as simultaneous localization and mapping (SLAM) has become a research hotspot in recent years, allowing tracking of the endoscope. However, most of the existing solutions have focused on tasks in which sufficient texture information is available, such as laparoscope tracking, and cannot be applied to gastroscope tracking since gastroscopic images have fewer textures than laparoscopic images. This paper proposes a new monocular SLAM framework based on scale-invariant feature transform (SIFT) and narrow-band imaging (NBI), which extracts SIFT features instead of oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) features from gastroscopic NBI images, and performs feature retention based on the response sorting strategy for achieving more matches. Experimental results show that the root mean squared error of the proposed algorithm can reach a minimum of 2.074mm, and the pose accuracy can be improved by up to 25.73% compared with oriented FAST and rotated BRIEF (ORB)-SLAM. SIFT features and response sorting strategy can achieve more accurate matching in gastroscopic NBI images than other features and homogenization strategy, and the proposed algorithm can also run successfully on real clinical gastroscopic data. The proposed algorithm has the potential clinical value to assist physicians in locating the gastroscope during gastroscopy.

Research on an environmental adaptive feature extraction algorithm

The visual simultaneous localization and mapping (SLAM) system is prone to tracking failure in complex and variable environments and lighting change environments. Therefore, an adaptive S-piecewise from an accelerated segment test (FAST) and boosted efficient binary local image descriptor or BEBLID (ASFB) feature extraction algorithm is proposed to obtain better matching results and more accurate poses. The algorithm designs a segment function adaptive thresholding FAST feature extraction algorithm based on the sigmoid function trajectory. It also calculates different thresholds for each pixel point in the image to complete the feature extraction, which adaptively calculates the threshold value for different luminance environments, to improve the quality of feature points, and uses BEBLID instead of binary robust independent elementary feature (BRIEF) to complete the description of feature points, which improves the accuracy of description information. The experiment selects KITTI and Euroc datasets, and the results show that the ASFB feature extraction algorithm performs better than the oriented FAST and rotated BRIEF algorithm in both the illumination changing environment and the normal environment. In addition, the bit pose accuracy is also improved. Moreover, it can meet the operational efficiency requirements of the SLAM algorithm.