Features From Accelerated Segment Test Research Articles

Polygon graphic recognition based on improved fast corner detection

The accuracy of polygon graphic recognition based on chain code features and Hough transform is low, and the computation is limited. Therefore, this paper proposes a polygon graphic recognition method based on improved features from accelerated segment test (FAST) corner detection. First, hole filling and Freeman chain code are used to segment the image, and the regular geometric features are obtained. Second, in order to improve the performance of the algorithm, an improved FAST corner detection combined with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is proposed. Through clustering, false corner in the image can be eliminated, and the feature points are quickly filtered by local NMS (non-maximum suppression). Finally, polygon graphic recognition is realized by feature points, experimental results illustrate that this method has high calculation efficiency and recognition rate.

Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera.

In this study, an image registration algorithm was applied to calculate the rotation angle of objects when matching images. Some commonly used image feature detection algorithms such as features from accelerated segment test (FAST), speeded up robust features (SURF) and maximally stable extremal regions (MSER) algorithms were chosen as feature extraction components. Comparing the running time and accuracy, the image registration algorithm based on SURF has better performance than the other algorithms. Accurately obtaining the roll angle is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. To acquire the roll angle of agriculture machinery, a roll angle acquisition model based on the image registration algorithm was built. Then, the performance of the model with a monocular camera was tested in the field. The field test showed that the average error of the rolling angle was 0.61°, while the minimum error was 0.08°. The field test indicated that the model could accurately obtain the attitude change trend of agricultural machinery when it was working in irregular farmlands. The model described in this paper could provide a foundation for agricultural equipment navigation and autonomous driving.

Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery.

Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was < mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery.

Detection Algorithm of Infrared Dim Small Target Based on FPGA

The traditional infrared dim and small target detection algorithm is generally implemented by digital signal processing,which is complex and has poor real-time performance.In this study,a features from accelerated segment test(FAST)adaptive-threshold algorithm based on a field programmable gate array(FPGA)is proposed to detect infrared dim and small targets.Based on the characteristics of FPGA parallel processing,the hardware acceleration of the algorithm was realized by a pipeline design.The improved adaptive threshold method can generate appropriate thresholds according to different environments and avoid the loss or redundancy of dim and small infrared targets that is caused by improper threshold selection.Finally,two different groups of infrared images were used for an experiment.The results show that the algorithm can detect dim and small targets in an infrared image in real time and can achieve a high detection rate and low false alarm rate,thereby meeting the requirements of real-time performance and effectiveness.

The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments

Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement units (IMUs), cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to the low-cost and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions. In this study, we proposed a scheme that integrated sensor data from smartphone IMUs, GNSS chipsets, and cameras using an extended Kalman filter (EKF) to enhance the navigation performance. The visual data from the camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration filter. In order to verify the performance of the integrated system, field test data was collected in a downtown area of Tainan City, Taiwan. Experimental results indicated that visual data contributed significantly to improving the accuracy of the navigation performance, demonstrating improvements of 43.0% and 51.3% in position and velocity, respectively. It was verified that the proposed integrated system, which used data from smartphone sensors, was efficient in terms of increasing navigation accuracy in GNSS-challenging environments.

High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features

Content integrity of high-resolution remote sensing (HRRS) images is the premise of its usability. Existing HRRS image integrity authentication methods are mostly binary decision-making processes, which cannot provide a further interpretable information (e.g., tamper localization, tamper type determination). Due to this reason, a robust HRRS images integrity authentication algorithm using perceptual hashing technology considering both global and local features is proposed in this paper. It extracts global features by the efficient recognition ability of Zernike moments to texture information. Meanwhile, Features from Accelerated Segment Test (FAST) key points are applied to local features construction and tamper localization. By applying the concept of multi-feature combination to the integrity authentication of HRRS images, the authentication process is more convincing in comparison to existing algorithms. Furthermore, an interpretable authentication result can be given. The experimental results show that the algorithm proposed in this paper is highly robust to the content retention operation, has a strong sensitivity to the content changing operations, and the result of tampering localization is more precise comparing with existing algorithms.

A Visible and Infrared Fusion Based Visual Odometry for Autonomous Vehicles

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;An accurate and timely positioning of the vehicle is required at all times for autonomous driving. The global navigation satellite system (GNSS), even when integrated with costly inertial measurement units (IMUs), would often fail to provide high-accuracy positioning due to GNSS-challenged environments such as urban canyons. As a result, visual odometry is proposed as an effective complimentary approach. Although it’s widely recognized that visual odometry should be developed based on both visible and infrared images to address issues such as frequent changes in ambient lightening conditions, the mechanism of visible-infrared fusion is often poorly designed. This study proposes a Generative Adversarial Network (GAN) based model comprises a generator, which aims to produce a fused image combining infrared intensities and visible gradients, and a discriminator whose target is to force the fused image to retain as many details that exist mostly in visible images as possible. Based on the fused image, the Features from Accelerated Segment Test (FAST) algorithm is adopted to extract feature points which are then traced with the Lucas-Kanade (LK) algorithm in subsequent images. Furthermore, to remove mismatched feature points, the Random Sample Consensus (RANSAC) method is employed to detect the outliers iteratively and to compute the essential matrix. Experiments are conducted utilizing the KAIST benchmark dataset. A significant improve in positioning accuracy is observed from experimental results, as compared to visual odometry built upon visible and infrared images only. The proposed visual odometry can provide high-accuracy positioning when the GNSS is challenged.&lt;/div&gt;&lt;/div&gt;

Method for Detecting Visible Impurity in Oral Liquid Based on Improved SURF Algorithm

In order to solve the problems that the visible foreign objects in the large bottle oral liquid are small, the detection speed is low and the single feature would be easy to cause the mis-tracking. In this paper, the speed-up robust features (SURF) algorithm is improved and applied to the detection of visible foreign objects. Firstly, features from accelerated segment test (FAST) detection algorithm is used instead of the Hessian matrix for feature point detection to avoid the extraction of numerous and useless feature points in the edge region. Secondly, two-way fast library for approximate nearest neighbours (Flann) algorithm is adopted for the feature matching to accelerate the matching rate and improve the accuracy of matching. The related experiment shows that the proposed algorithm can accurately match the target and effectively improve the detection speed, which meets the requirements of online detection.

Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference.

This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.

Augmented reality using features accelerated segment test for learning tajweed

Currently, education forms students to think creatively and critically, this can be supported by the multimedia technology for education, including Islamic religious education. Islam requires all of its Muslim to read the Qur'an. Tajweed is an important because it is related to the articulation of reading the Qur'an properly and correctly. This article discusses the application of augmented reality (AR) as one of the multimedia technologies that can be used as an interactive educational medium to help study the tajweed of Qur'an. The method used in this research is Features from accelerated segment test (FAST) corner detection. The testing result with 31 tajweed objects show that FAST is able to recognize all Tajweed objects and display their AR. Besides, based on a survey with questionnaires to several students, the result shows that 88.2% of students responded very well and judged that it was sufficient to help study the tajweed.

Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Features from Accelerated Segment Test (FAST)

Nutrient Content NPK is macro nutrient content that important for the growth of a plant. The measurement of NPK conducted periodically, but the measurement using laboratories test need relatively long time. This Research is conducted to determine the nutrient content of the soil, consisted of nitrogen, phosphor, and calcium (NPK) using digital image processing based on Features from Accelerated Segment Test (FAST) and backpropagation artificial neural network. The data sample in this research taken from the rice field soil in Daerah Istimewa Yogyakarta province where the soil taken at the length of 30 cm to 110 cm with l20 cm interval, and -30° to 30° degree with interval 10°. The model from this measurement system based on texture’s characteristic that extracted using Scale Invariant Feature Transform from soil’s image that already passed pre-processing process. The characteristic result will be the input from the artificial neural network with a variation on parameter’s model. The model tested for the purpose of knowing the influence the distance and degree where the image taken and the influence of parameter’s artificial neural network. The result from the research, is a accurate value of the measurement for each nutrient in the soil, nitrogen (94.86%), phosphor (58.93%) and calcium (63.57%), with the mean 72,46%. The corresponding result obtained from image taken with optimal height of 70 cm and degree 0o

Automatic Museum Audio Guide.

An automatic “museum audio guide” is presented as a new type of audio guide for museums. The device consists of a headset equipped with a camera that captures exhibit pictures and the eyes of things computer vision device (EoT). The EoT board is capable of recognizing artworks using features from accelerated segment test (FAST) keypoints and a random forest classifier, and is able to be used for an entire day without the need to recharge the batteries. In addition, an application logic has been implemented, which allows for a special highly-efficient behavior upon recognition of the painting. Two different use case scenarios have been implemented. The main testing was performed with a piloting phase in a real world museum. Results show that the system keeps its promises regarding its main benefit, which is simplicity of use and the user’s preference of the proposed system over traditional audioguides.

A Flexible and Efficient Real-Time ORB-Based Full-HD Image Feature Extraction Accelerator

This article proposes a hardware accelerator for image feature extraction based on the oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (ORB) algorithm. The architecture adopts a hybrid workflow to process three scales in parallel as an octave, and multiple octaves are processed in serial by time-sharing the DRAM. In order to support any image resolution, a block-wise dataflow is used on the serial-parallel architecture. The overlapped data between two blocks is reused. As a result, the on-chip memory is limited to 1.47 Mb, and the DRAM bandwidth is compressed by 33%. Besides, 3×3 nonmaximum suppression with heap sorting is applied to balance keypoint distribution in 2-D. It improves the valid keypoint match ratio by 7.09%. In addition, parallel processing in keypoint detection increases 2× throughout. Moreover, approximate computing and superscalar processing reduce the timing cost in orientation estimation and descriptor generation by 92.78% and 67% respectively. Compared to the non-optimized baseline architecture, the proposed architecture saves the total timing cost by 82.4%. The accelerator is implemented in a Xilinx MPSoC, and it achieves 108 fps on full-HD images at 200 MHz under 873 mW.

Robust and Efficient Corner Detector Using Non-Corners Exclusion

Corner detection is a traditional type of feature point detection method. Among methods used, with its good accuracy and the properties of invariance for rotation, noise and illumination, the Harris corner detector is widely used in the fields of vision tasks and image processing. Although it possesses a good performance in detection quality, its application is limited due to its low detection efficiency. The efficiency is crucial in many applications because it determines whether the detector is suitable for real-time tasks. In this paper, a robust and efficient corner detector (RECD) improved from Harris corner detector is proposed. First, we borrowed the principle of the feature from accelerated segment test (FAST) algorithm for corner pre-detection, in order to rule out non-corners and retain many strong corners as real corners. Those uncertain corners are looked at as candidate corners. Second, the gradients are calculated in the same way as the original Harris detector for those candidate corners. Third, to reduce additional computation amount, only the corner response function (CRF) of the candidate corners is calculated. Finally, we replace the highly complex non-maximum suppression (NMS) by an improved NMS to obtain the resulting corners. Experiments demonstrate that RECD is more competitive than some popular corner detectors in detection quality and speed. The accuracy and robustness of our method is slightly better than the original Harris detector, and the detection time is only approximately 8.2% of its original value.

Wind turbine tower detection using feature descriptors and deep learning

Wind Turbine Towers (WTTs) are the main structures of wind farms. They are costly devices that must be thoroughly inspected according to maintenance plans. Today, existence of machine vision techniques along with unmanned aerial vehicles (UAVs) enable fast, easy, and intelligent visual inspection of the structures. Our work is aimed towards developing a vision-based system to perform Nondestructive tests (NDTs) for wind turbines using UAVs. In order to navigate the flying machine toward the wind turbine tower and reliably land on it, the exact position of the wind turbine and its tower must be detected. We employ several strong computer vision approaches such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Brute-Force, Fast Library for Approximate Nearest Neighbors (FLANN) to detect the WTT. Then, in order to increase the reliability of the system, we apply the ResNet, MobileNet, ShuffleNet, EffNet, and SqueezeNet pre-trained classifiers in order to verify whether a detected object is indeed a turbine tower or not. This intelligent monitoring system has auto navigation ability and can be used for future goals including intelligent fault diagnosis and maintenance purposes. The simulation results show the accuracy of the proposed model are 89.4% in WTT detection and 97.74% in verification (classification) problems.

A Comparative Study of Feature Detection Techniques for Navigation of Visually Impaired Person in an Indoor Environment

A development of automatic location identification and tracking system for visually impaired/ challenged person is a very challenging task in an indoor environment. In this paper, the comprehensive study of different feature detection and matching techniques namely, Minimum Eigenvalue (MinEigen) algorithm, Harris–Stephens (Harris) algorithm, Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Binary Robust Invariant Scalable Keypoints (BRISK) and Maximally Stable Extremal Regions (MSER) is presented. These algorithms are employed to detect and match the features of an image and retrieve the best matched image. Based on our experiments, we compare those algorithms on parameters such as sum of square difference (SDD), precision, recall, number of detected, matched features and processing time. Empirically, we have found that SURF algorithm produce minimum SSD score to achieve best matching. The MSER and MinEign algorithm extracts high and low number of features respectively. In respect of processing time, BRISK takes maximum and FAST method takes minimum time when compared to the other algorithms.

Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

GPS-SLAM: An Augmentation of the ORB-SLAM Algorithm.

This work presents Global Positioning System-Simultaneous Localization and Mapping (GPS-SLAM), an augmented version of Oriented FAST (Features from accelerated segment test) and Rotated BRIEF (Binary Robust Independent Elementary Features) feature detector (ORB)-SLAM using GPS and inertial data to make the algorithm capable of dealing with low frame rate datasets. In general, SLAM systems are successful in case of datasets with a high frame rate. This work was motivated by a scarce dataset where ORB-SLAM often loses track because of the lack of continuity. The main work includes the determination of the next frame’s pose based on the GPS and inertial data. The results show that this additional information makes the algorithm more robust. As many large, outdoor unmanned aerial vehicle (UAV) flights save the GPS and inertial measurement unit (IMU) data of the capturing of images, this program gives an option to use the SLAM algorithm successfully even if the dataset has a low frame rate.

Feature Extraction Acceleration to Stabilize Execution Time for Real-Time Applications in Low-Cost Embedded Systems

Extracted features are widely used for image processing. Many research endeavors have been undertaken to extract significant features of fast moving images. Appropriate algorithm processing is necessary to extract features and provide features to the other modules in real time with low-cost embedded systems. The features from accelerated segment test (FAST) algorithm is renowned for feature extraction. FAST is composed of simple arithmetic operators. In this study, FAST is employed to implement the hardware accelerator in a field-programmable gate array for small embedded systems. Meanwhile, the threshold value in FAST affects the number of extracted features and the execution time. The precarious execution time makes it difficult for the system to schedule the timing of system functions and thus degrades the performance. An appropriate method is necessary to stabilize the execution time. A dynamic threshold controller in a FAST hardware accelerator is thus proposed to enable a stable execution time. A proportional integral controller composed of an adder, subtractor, and shifter is applied for low design implementation costs. The proposed approach occupies 2,263 slice flip-flops, 3,498 look-up tables, and 17 block RAMs in a Xilinx Virtex 5 FX field-programmable gate array. It requires 3.87[Formula: see text]ms for continuous 800×480 images from the KITTI benchmark.

Improved FAST corner‐detection method

An improved features from accelerated segment test (FAST) corner-detection method for suppressing SAR image speckle noise is proposed here. The direction of the corner candidate is first computed by the law of universal gravitation and is introduced into corner detection to divide the 16 pixels around the corner candidate into two groups without repeatedly searching. Then, the similarity between the corner candidate and its surrounding pixels is computed to suppress the points affected by SAR speckle noise. Finally, the phenomenon of corner density is eliminated based on non-maximum suppression. The experimental results show that authors’ method has better suppressing effect compared with FAST-8 and SIFT corner-detection methods.