Speeded Up Robust Features Method Research Articles

EEDD: Edge-Guided Energy-Based PCB Defect Detection

Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We focus on the salient edge characteristic of PCB images and regard the functional defects as contour differences and then propose a novel energy measurement method for PCB contour anomaly detection. We introduce the energy transformation using the edge information of the template and test image, then Speeded-Up Robust Features method (SURF) is used for image alignment, and finally achieve defect detection by measuring the energy anomaly score pixel by pixel with the proposed energy-based defect localization and contour flood fill methods. Our method excels in detecting multi-scale defects, particularly tiny defects, and is robust against interferences such as non-finely aligned images and edge spurs. Experiments on the DeepPCB-A dataset and our high-resolution PCB dataset (HDPCB) show that the proposed method outperforms state-of-the-art methods in PCB defect-detection tasks.

Improved SURF method in digital image correlation for estimation of large rotation angle

Digital image correlation (DIC) is widely used in optical measurement techniques with the advantages of high precision, robustness and expansibility. The conventional DIC, however, is highly dependent on the accuracy of initial parameters, and there are reports [1,2] that it may fail when the rotation angle of the target specimen exceeds 7°. The combination of Speeded-Up Robust Features (SURF) with Random Sample Consensus (RANSAC) algorithm has been applied to solve this problem with limited success due to the effects of residual mismatches. An enhanced algorithm based on SURF is proposed in this study to address this problem. The transformation model between two images is obtained by RANSAC similar to existing methods. Then an angle compensation strategy is proposed to estimate the rotation angle of the deformed image to form the compensated image. Finally, an iterative process is proposed to refine the initial parameters from the reference and deformed images with a new convergence threshold. In addition, an evaluation criterion developed from a multiple ring template is proposed to compute the similarity of feature point pairs to improve the speed of convergence and sampling accuracy. Results from numerical simulation and experimental analysis show that the accuracy and robustness of the proposed method are significantly improved. Further application of the proposed method to monitor the rotating blades of a scaled model wind turbine illustrates further the capability of the proposed method.

Development of payload capacity enhanced robust video watermarking scheme based on symmetry of circle using lifting wavelet transform and SURF

Using simple image and video editing software it is relatively easy to pirate the digital contents at the cost of huge revenue loss to the content owner. Entertainment is the key industry that is overwhelmed by huge financial loss due to digital piracy. WhatsApp, Facebook, Instagram, Pinterest and Twitter are some of the massive social media platforms that are used by millions of users. Many of the users share digital content without giving credit to the owner. Digital watermarking is an efficient technique to protect the digital rights and legal compensation of content owners. Due to the poor selection of watermarking locations, most of the state-of-art methods were fall short to preserve the relationships among the host data. Hence such methods were not efficient to provide safeguards against digital piracy. Therefore, this paper proposes a digital video watermarking technique that analyzes, extracts and processes the useful patterns or features of data based on the symmetry of the circle using 2D-Lifting Wavelet Transform (2D-LWT) and Speeded-Up Robust Features (SURF). SURF method is used to obtain five invariant feature points on an approximation part of 2D-LWT utilizing the luminance component of each video frame. The invariant points serve as centers of the circles used to find the embed locations. The maximum intensity value point in all the points on the circumference of the 1/8 part of the circle is then calculated to serve as an embedding location. Three other points in different quadrants are simply obtained using the concept of symmetry of the circle. After applying temporal desynchronization, compression and insertion of noise attacks the video is assessed through subjective and objective evaluation metrics. The results reveal a high degree of correlation (close to one) and good visual transparency which proves that the proposed scheme is superior for practical applications.

Forest 3D Reconstruction and Individual Tree Parameter Extraction Combining Close-Range Photo Enhancement and Feature Matching

An efficient and accurate forest sample plot survey is of great significance to understand the current status of forest resources at the stand or regional scale and the basis of scientific forest management. Close-range photogrammetry (CRP) technology can easily and quickly collect sequence images with high overlapping to reconstruct the 3D model of forest scenes and extract the individual tree parameters automatically and, therefore, can greatly improve the efficiency of forest investigation and has great application potential in forestry visualization management. However, it has some issues in practical forestry applications. First, the imaging quality is affected by the illumination in the forest, resulting in difficulty in feature matching and low accuracy of parameter extraction. Second, the efficiency of 3D forest model reconstruction is limited under complex understory vegetation or the topographic situation in the forest. In addition, the density of point clouds by dense matching directly affects the accuracy of individual tree parameter extraction. This research collected the sequence images of sample plots of four tree species by smartphones in Gaofeng Forest Farm in Guangxi and Wangyedian Forest Farm in Mongolia to analyze the effects of image enhancement, feature detection and dense point cloud algorithms on the efficiency of 3D forest reconstruction and accuracy of individual tree parameter extraction, then proposed a strategy of 3D reconstruction and parameter extraction suitable for different forest scenes. First, we compared the image enhancement effects of median–Gaussian (MG) filtering, single-scale retinex (SSR) and multi-scale retinex (MSR) filtering algorithms. Then, an improved algorithm combining Harris corner detection with speeded-up robust features (SURF) feature detection (Harris+SURF) is proposed, and the feature matching effect is compared with that of a scale invariant feature transform (SIFT) operator. Third, according to the morphological characteristics of the trees in the sequence images, we used the iterative interpolation algorithm of a planar triangulation network based on geometric constraints (GC-based IIPTN) to increase the density of point clouds and reconstruct the 3D forest model, and then extract the position and DBH of the individual trees. The results show that MSR image enhancement can significantly increase the number of matched point pairs. The improved Harris+SURF method can reduce the reconstruction time of the 3D forest model, and the GC-based IIPTN algorithm can improve the accuracy of individual tree parameter extraction. The extracted position of the individual tree is the same as the measured position with the bias within 0.2 m. The accuracy of extracted DBH of Eucalyptus grandis, Taxus chinensis, Larix gmelinii and Pinus tabuliformis is 94%, 95%, 96% and 90%, respectively, which proves that the proposed 3D model reconstruction method based on image enhancement has great potential for tree position and DBH extraction, and also provides effective support for forest resource investigation and visualization management in the future.

A fruit recognition method of green grape images in the orchard

ABSTRACT The work proposed a method for recognising the green grapes in the orchard based on multi-source image fusion. First, the acquired multi-source images were denoised based on median filtering and wavelet transform. After extracting the feature points by the improved SURF (speeded up robust features) method, the registration was completed based on the consistency of feature offset and the affine relationship between images. The registered multi-source images were fused based on the CS (compressed sensing) and NSCT-DWT (non-down sampled contourlet transform-discrete wavelet transform). Then the MI-OPT (mutual-information optimal threshold) and the minimum circumscribed rectangle were used to segment the fused images and recognise fruits. The experimental results showed that the information of the target fruits in the fused images was complete. Therefore, compared with the K-means method using colour components of the visible light image and the OTSU (proposed by Nobuyuki Otsu and named after him) method based on near-infrared image, the fruit region obtained by the algorithm in the work was complete. On this basis, the average recognition rate of green grapes reached 92.1%.

Research on target feature extraction and location positioning with machine learning algorithm

Abstract The accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positioned the location using Back Propagation Neural Networks (BPNN) method, and tested the method through experiments. The experimental results showed that the feature extraction of SURF method was fast, about 0.2 s, and was less affected by noise. It was found from the positioning results that the output position of the BPNN method was basically consistent with the actual position, and errors in X, Y and Z directions were very small, which could meet the positioning needs of the robot. The experimental results verify the effectiveness of machine learning method and provide some theoretical support for its further promotion and application in practice.

Intelligent Visual Tracking in Unstabilized Videos

Visual tracking requirement is increasing day by day due to the availability of high-performance digital cameras at low prices. Visual tracking becomes a complex problem when cameras suffer with unwanted and unintentional motion, resulting in motion-blurred unstabilized video. The problem in hand becomes more challenging when the target of interest is to be detected automatically in this unstabilized video. This paper presents a comprehensive single intelligent solution for these problems. The proposed algorithm auto-detects the camera motion, filters out the unintentional motion while stabilizing the video keeping intentional motion only using speeded-up robust features (SURF) technique. Motion smear due to unstabilization is also removed, providing sharp stabilized video output with video quality enhancement of up to 20dB. Gabor filter is used innovatively for auto-detection of target of interest in each stabilized frame. Then the target is tracked using SURF method.

Enhanced Delaunay Triangulation Sea Ice Tracking Algorithm with Combining Feature Tracking and Pattern Matching

Sea ice drift detection has the key role of global climate analysis and waterway planning. The ability to detect sea ice drift in real-time also contributes to the safe navigation of ships and the prevention of offshore oil platform accidents. In this paper, an Enhanced Delaunay Triangulation (EDT) algorithm for sea ice tracking was proposed for dual-polarization sequential Synthetic Aperture Radar (SAR) images, which was implemented by combining feature tracking with pattern matching based on integrating HH and HV polarization feature information. A sea ice retrieval algorithm for feature detection, matching, fusion, and outlier detection was specifically developed to increase the system’s accuracy and robustness. In comparison with several state-of-the-art sea ice drift retrieval algorithms, including Speeded Up Robust Features (SURF) and the Oriented FAST and Rotated BRIEF (ORB) method, the results of the experiment provided compelling evidence that our algorithm had a higher accuracy than the SURF and ORB method. Furthermore, the results of our method were compared with the drift vector and direction of buoys data. The drift direction is consistent with buoys, and the velocity deviation was about 10 m. It was proved that this method can be applied effectively to the retrieval of sea ice drift.

Improved RANSAC features image‐matching method based on SURF

For the speed of traditional SIFT algorithm in the feature extraction and matching is slow, the article proposes an improved RANSAC features image matching method based on speeded up robust features (SURF). First of all, detect images features and extract with SURF method, use the fast library for approximate nearest neighbours-based matcher method to perform initial matching on image feature points. Improve the RANSAC algorithm to increase the probability of correct matching points being sampled. Experimental results show that the improved RANSAC algorithm has high matching accuracy, good robustness, and short running time. It lays the foundation for the subsequent fast image stitching.

Adaptive digital medical image watermarking approach in 2D-wavelet domain using speed-up robust feature method

In the recent trends, the utilisation of digital images had got their importance in many fields. One of the major fields which had the more prominence utilisation of digital images is health care. The patient's information is stored in digital images for maintaining privacy and there is a need for preserving the content of the images. Watermarking serves well for protecting digital images. In this paper, Adaptive Digital Medical image watermarking approach is proposed. This proposed method utilises the 2D-Wavelet domain for converting the medical image in to number of sub bands. The interesting points are identified using the Speed-Up Robust Feature (SURF) method. The scaling parameter is calculated for the digital images using the bipolar sigmoid function. The control parameter is introduced to adjust the scaling parameter and it plays a crucial role in deciding the strength of the watermark. Experimental evaluation is carried out using three parameters, Peak Signal to Noise Ratio (PSNR), Structure SImilarity Measure (SSIM) and Normalised Correlation Coefficient (NCC). The experimental results proved that the proposed method had superior performance in both visible and invisible watermarking.

Adaptive digital medical image watermarking approach in 2D-wavelet domain using speed-up robust feature method

In the recent trends, the utilisation of digital images had got their importance in many fields. One of the major fields which had the more prominence utilisation of digital images is health care. The patient's information is stored in digital images for maintaining privacy and there is a need for preserving the content of the images. Watermarking serves well for protecting digital images. In this paper, Adaptive Digital Medical image watermarking approach is proposed. This proposed method utilises the 2D-Wavelet domain for converting the medical image in to number of sub bands. The interesting points are identified using the Speed-Up Robust Feature (SURF) method. The scaling parameter is calculated for the digital images using the bipolar sigmoid function. The control parameter is introduced to adjust the scaling parameter and it plays a crucial role in deciding the strength of the watermark. Experimental evaluation is carried out using three parameters, Peak Signal to Noise Ratio (PSNR), Structure SImilarity Measure (SSIM) and Normalised Correlation Coefficient (NCC). The experimental results proved that the proposed method had superior performance in both visible and invisible watermarking.

Implementation of Parallel Processing on Multi-Object Recognition System Software

Multi-object recognition software on Remote Controlled Weapon Station (RCWS) had been implemented in previous paper using Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) methods, but the processing time in one cycle is quite slow so it is need to be optimized using parallel processing. In this paper, implementation of parallel processing on multi-object recognition software has been done on a multicore processor. The Openmp Application Programming Interface (API), C programming language, and Visual studio Integrated Development Environment (IDE) is used to implement the parallel processing in this paper. The parallel processing was implemented in the for loop of the matching process between the capturing object from the camera and the database under two conditions, i.e., the original of the for loop syntax and after optimization of the for loop syntax. Experiments have been done on the core processor i7-4790 @ 3.60Ghz, 8 GB DDR3 of memory, windows 8.1 os using two, four, six, and eight cores to recognize one, two, three and four objects at once using SIFT and SURF methods. Based on the experiments, it was found that the processing time in parallel is faster than sequential process, where the fastest of the processing time is obtained after optimization in the loop syntax, with the processing time in recognizing one to four objects using SIFT method is 927.13 ms (8 core), 1019.31 ms (6 core), 1190.72 ms (8 core), and 1283.05 ms (4 core), where the sequential processing time in recognizing one to four objects is 1067.35 ms, 1164.78 ms, 1352.93 ms, and 1497.35 ms, while the processing time in recognizing one to four objects using SURF method is 1157.13 ms (8 core), 1517.83 ms (6 core), 1572.14 ms (4 core), dan 1472.64 ms (6 core), where the sequential processing time in recognizing one to four objects is 5635.99 ms, 6268.47 ms, 3256.63 ms, dan 3883.78 ms.

New Analysis Method Application in Metallographic Images through the Construction of Mosaics Via Speeded Up Robust Features and Scale Invariant Feature Transform

In many applications in metallography and analysis, many regions need to be considered and not only the current region. In cases where there are analyses with multiple images, the specialist should also evaluate neighboring areas. For example, in metallurgy, welding technology is derived from conventional testing and metallographic analysis. In welding, these tests allow us to know the features of the metal, especially in the Heat-Affected Zone (HAZ); the region most likely for natural metallurgical problems to occur in welding. The expanse of the Heat-Affected Zone exceeds the size of the area observed through a microscope and typically requires multiple images to be mounted on a larger picture surface to allow for the study of the entire heat affected zone. This image stitching process is performed manually and is subject to all the inherent flaws of the human being due to results of fatigue and distraction. The analyzing of grain growth is also necessary in the examination of multiple regions, although not necessarily neighboring regions, but this analysis would be a useful tool to aid a specialist. In areas such as microscopic metallography, which study metallurgical products with the aid of a microscope, the assembly of mosaics is done manually, which consumes a lot of time and is also subject to failures due to human limitations. The mosaic technique is used in the construct of environment or scenes with corresponding characteristics between themselves. Through several small images, and with corresponding characteristics between themselves, a new model is generated in a larger size. This article proposes the use of Digital Image Processing for the automatization of the construction of these mosaics in metallographic images. The use of this proposed method is meant to significantly reduce the time required to build the mosaic and reduce the possibility of failures in assembling the final image; therefore increasing efficiency in obtaining results and expediting the decision making process. Two different methods are proposed: One using the transformed Scale Invariant Feature Transform (SIFT), and the second using features extractor Speeded Up Robust Features (SURF). Although slower, the SIFT method is more stable and has a better performance than the SURF method and can be applied to real applications. The best results were obtained using SIFT with Peak Signal-to-Noise Ratio = 61.38, Mean squared error = 0.048 and mean-structural-similarity = 0.999, and processing time of 4.91 seconds for mosaic building. The methodology proposed shows be more promissory in aiding specialists during analysis of metallographic images.

Efficient Modern Description Methods by Using SURF Algorithm for Recognition of Plant Species

Plants are one of one of most valuable natural resources. There would be no life on earth without plants. They unlike humans and animals manufacture their own food by photosynthesis. In every food chain, the plants occupy the first position and lead the chain as source of food. Environment and climate are largely interlinked with plants. Rainfall, humidity and temperature are influenced by presence of plants. Cutting down plants also imbalance the environment which will indirectly affect human life. Even the economic importance of plants is also quite large to mankind. Plants are great contributors of economy. Many countries rely on agriculture as one of the main source of revenue. Other benefits of plants are significant applications in different fields. Medical and agricultural applications are just some instances of plants application. Plant recognition can be done by using unique characteristic parts of plants. The used part is leaf. Shapes of leaves are useful to do plant recognition and find the species. Bag of words (BoW) and support vector machine (SVM) methods are applied to recognize and identify plants species. Visual contents of images are used and four steps are performed: (i) image preprocessing, (ii) BoW, (iii) train, (v) test. Three combined methods are used on Flavia dataset. The proposed approach is done by Speed-up robust features (SURF) method and two combined method, HARRIS-SURF and features from accelerated segment test-SURF (FAST-SURF). The accuracy of SURF method is higher than other applied methods. It is 92.28395 %. In addition to visional comparison, some quantitative results are measured and compared.

적외선 영상에서 변위추정 및 SURF 특징을 이용한 표적 탐지 분류 기법

본 논문에서는 적외선 영상에서 영상 변위를 이용하여 기동 표적 영역을 탐지하고, SURF(Speeded Up Robust Features) 특징점에 대한 BAS(Beam Angle Statistics)를 이용하여 분류하는 시스템에 대하여 설명한다. 영상 기반 기술 분야에서 대표적인 대응점 정합 알고리즘인 SURF 기법은 SIFT(Scale Invariant Feature Transform) 기법에 비해 정합 속도가 매우 빠르고 비슷한 정합 성능을 보이기 때문에 널리 사용되고 있다. SURF를 이용한 대부분의 객체 인식의 경우 특징점 추출과 정합의 과정을 수행하지만, 제안하는 기법은 표적의 기동 특성을 반영하여 영상의 변위 추정을 통하여 표적의 영역을 탐지하고 SURF 특징점 들의 기하구조를 판단함으로써 표적 분류를 수행한다. 제안하는 기법은 무인 표적 탐지/인지 시스템의 초기모델 구축을 위하여 연구가 진행되었으며, 모의 표적을 이용한 가상 영상과 적외선 실 영상을 이용하여 실험한 결과 약 73~85%의 분류 성능을 확인하였다. In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.

CORRELATION-EXTREME VISUAL NAVIGATION OF UNMANNED AIRCRAFT SYSTEMS BASED ON SPEED-UP ROBUST FEATURES

The peculiarities of correlation-extreme visual navigation are considered. Descriptors with 64 elements of feature points of surface images are selected on the basis of the speed-up robust feature method. An analysis of possible criteria correlation functions is carried out to find the best match between the template descriptors and current images. The use of normalized correlation function is proposed based on the matrix multiplication properties of descriptors. It allows minimizing the number of false matches in comparison with the Euclidean distance in the descriptor space. The proposed matching strategy sufficiently decreases the computation time.

Research of Medical Image Non-Rigid Registration Based on TPS-SEMISURF Algorithm

Aiming at avoiding misregistration in complicated medical image registration based on SURF (Speed-Up Robust Features)-TPS (Thin-Plate Spline), we propose a novel algorithm. This method is based on SURF and human interaction method for feature extraction. Then we improve SURF-TPS and propose an algorithm named TPS-SEMISURF which obtains the deformation field by calculating the Thin-plate spline of the feature points, and finally does the medical image non-rigid registration according to the parameters. Experimental results showed that the proposed method can register medical images effectively. It has a good robustness and owns better precision and rate than traditional algorithm.

ILLUMINATION, SCALE AND ROTATION INVARIANT ALGORITHM FOR VISION-BASED UAV NAVIGATION

Navigation has been a major challenge for the successful operation of an autonomous aircraft. Although success has been achieved using active methods such as radar, sonar, lidar and the global positioning system (GPS), such methods are not always suitable due to their susceptibility to jamming and outages. Vision, as a passive navigation method, is considered as an excellent alternative; however, the development of vision-based autonomous systems for outdoor environments has proven difficult. For flying systems, this is compounded by the additional challenges posed by environmental and atmospheric conditions. In this paper, we present a novel passive vision-based algorithm which is invariant to illumination, scale and rotation. We use a three stage landmark recognition algorithm and an algorithm for waypoint matching. Our algorithms have been tested in both synthetic and real-world outdoor environments demonstrating overall good performance. We further compare our feature matching method with the speed-up robust features (SURF) method with results demonstrating that our method outperforms the SURF method in feature matching as well as computational cost.

Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features

An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system.