Features From Accelerated Segment Test Research Articles

Gamma Distribution of FAST Feature for Statistical Image Modelling in Fingerprint Image Classification

A unique method for modelling Features from accelerated segment test (FAST) with the Gamma distribution for statistical image data is introduced. Instead of using an image's FAST feature instantly; we design the FAST function to decrease the other global extraction function. The method of moment is used to predict Gamma distribution parameters. FAST's mathematical depiction is the depiction of matrix and is too complicated to be implemented in the ranking of images. We are therefore proposing a fresh statistical function to display the picture in a few dimensional numbers. Our proposed feature utilizes FAST method of Gamma distribution that can be used directly in the identification of fingerprint images. We demonstrate that the Gamma distribution works with FAST and has been effectively implemented in the identification of fingerprint images. To show the benefits of the proposed feature, Shivang Patel Fingerprint Database is used on which classifier evaluation and state-of - the-art comparison are performed.

The Prototype of Hand Gesture Recognition for Elderly People to Control Connected Home Devices

Nowadays, the technology development makes a human can create a tool which is used to recognize an object and it becomes a popular technology device. It happens because this tool has an important role for interaction between a human and a computer. One example of this technology usage is to recognize a hand gesture for controlling a home automation system. The existing of this technology creates the change related to how the human controls any tools in a house and it also reduces the complexity including an effort when it is used for controlling. This feature is very useful, especially for elderly people who stay in independent living. This study is going to develop a controller prototype by using FAST (Features from Accelerated Segment Test) algorithm to detect hand gesture for operating the connected home devices. This controller uses an embedded system to translate a command which is created by using the hand gesture of senior captured by the cam for controlling the lamps. The lamps itself are represented as several tools in the house. The observation gives a result that the hand gesture is potential to be implemented as a command for controlling the proposed system prototype in the range which is not far than 1 meter with the percentage average recognition accuracy is almost 80%.

Mobile terminal gesture recognition based on improved FAST corner detection

Mobile terminal gesture recognition is an extreme challenge, not only because its limited computing resource make it complicated to identify feature points but also the complex background can easily affect the recognition result. This study proposes a gesture recognition method based on improved features from accelerated segment test (FAST) corner detection. First, in order to eliminate the effects of complex background and light, the intersection of the two frame images is obtained through background subtraction and the multi-colour space to realise the detection of the hand. Second, in order to improve the performance of the algorithm, an improved FAST corner detection method combined with the back propagation neural network (BPNN) is proposed in accordance with the characteristics of fingertips. Subsequently, the feature points are screened by method of non-maximum suppression. Finally, gesture recognition is realised by matching feature points. Experimental results illustrate that this method has strong anti-interference ability in complex background, and it is good at performance.

Sparse Feature Learning for Correlation Filter Tracking Toward 5G-Enabled Tactile Internet

Fifth generation with high dimensions and capabilities is expected to fulfil the requirements of the Tactile Internet. Tracking provides strong support for intuitive interaction with interfaces by hands, eyes, bodies, etc. Such interfaces can be used in the Tactile Internet for interaction with real and virtual objects. The trackers based on tracking-by-detection framework rely on manual feature detectors for robust tracking, which is particularly useful for specific objects like humans but cannot handle generic tracking problems. Therefore, a sparse feature learning method beyond manual design is proposed to learn features from the samples sampled during tracking. The basic idea is to learn a dictionary from the samples in the previous frames and construct feature representations to represent the object for detection of the location in the current frame. The samples are patches centered at the keypoints based on an adaptive features from accelerated segment test (FAST) detector with local threshold. The dictionary is learned with sparse coding for sparse representations and atoms of the dictionary are grouped to describe the local orientation of these samples. The integrated features are built after rectification of the sparse representations. The correlation filter is used to infer the object location from the sparse features. The qualitative and quantitative experimental results on OTB100 show the advantage of the proposed tracker against current state-of-the-art trackers in terms of accuracy.

The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration

This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to the raster form, (2) register of TLS data in pairs in all possible combinations using the SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms, (3) the quality analysis of relative orientation of processed pairs, and (4) the final bundle adjustment. The following two problems, related to the influence of the spherical image, the orthoimage and the Mercator representation of the point cloud, are discussed: The correctness of the automatic tie points detection and distribution and the influence of the TLS position on the completeness of the registration process and the quality assessment. The majority of popular software applications use manually or semi-automatically determined corresponding points. However, the authors propose an original software tool to address the first issue, which automatically detects and matches corresponding points on each TLS raster representation, utilizing different algorithms (SURF and FAST). To address the second task, the authors present a series of analyses: The time of detection of characteristic points, the percentage of incorrectly detected points and adjusted characteristic points, the number of detected control and check points, the orientation accuracy of control and check points, and the distribution of control and check points. Selection of an appropriate method for the TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process, and the accuracy of data orientation. The results of the performed experiments show that fully automatic registration of the TLS point clouds in the raster forms is possible; however, it is not possible to propose one, universal form of the point cloud, because a priori knowledge concerning the scanner positions is required. If scanner stations are located close to one another in raster images or in spherical images, Mercator projections are recommended. In the case where fragments of the surface are measured under different angles from different distances and heights of the TLS, orthoimages are suggested.

Recognition of Sign Language from High Resolution Images Using Adaptive Feature Extraction and Classification

A variety of algorithms allows gesture recognition in video sequences. Alleviating the need for interpreters is of interest to hearing impaired people, since it allows a great degree of self-sufficiency in communicating their intent to the non-sign language speakers without the need for interpreters. State-of-the-art in currently used algorithms in this domain is capable of either real-time recognition of sign language in low resolution videos or non-real-time recognition in high-resolution videos. This paper proposes a novel approach to real-time recognition of fingerspelling alphabet letters of American Sign Language (ASL) in ultra-high-resolution (UHD) video sequences. The proposed approach is based on adaptive Laplacian of Gaussian (LoG) filtering with local extrema detection using Features from Accelerated Segment Test (FAST) algorithm classified by a Convolutional Neural Network (CNN). The recognition rate of our algorithm was verified on real-life data.

Pose Estimation of Automatic Battery-Replacement System Based on ORB and Improved Keypoints Matching Method

This paper presents an improved Oriented Feature from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) keypoints matching method for pose estimation of automatic battery-replacement systems. The key issue of the system is how to precisely estimate the pose of the camera in respect to the battery. In our system, the pose-estimation hardware module is mounted onto the robot manipulator, composed of double high brightness LED light source, one monocular camera, and two laser rangefinders. The camera is utilized to take an image of the battery, the laser rangefinders on both sides of the camera are utilized to detect the real-time distance between the battery and the pose-estimation system. The estimation result is significantly influenced by the matching result of the keypoints detected by the ORB technique. The modified matching procedure, based on spatial consistency nearest hamming distance searching method, is used to determine the correct correspondences. Meanwhile, the iterative reprojection error minimization algorithm is utilized to discard incorrect correspondences. Verified by the experiments, the results reveal that this method is highly reliable and able to achieve the required positioning accuracy. The positioning error is lower than 1 mm.

Image stitching algorithm based on fast feature extraction and perceptual hash

The classic feature point image stitching algorithm is time-consuming in feature extraction, and an image sparse matrix method is proposed to determine the feature points. This method first uses Laplace operator to extract the image gradient and set the threshold to obtain the sparse matrix of image segmentation, then using features from accelerated segment test (FAST) detection algorithm for feature point. Finally, the speeded-up robust features (SURF) descriptor is given to increase the stability of the feature points. This method solves the problem of time-consuming feature extraction, making the real-time image mosaic possible. In the process of image mosaic, this paper presents a method using Kalman filter to predict the overlap region, thus reducing the computational complexity. In addition, because there is always a mismatch between feature points matching, this paper proposes a method using perceptual hashing to refine the matching pair, which solves the error caused by mismatching for the next image registration and improves the registration accuracy. Experimental results show that the proposed algorithm improves the speed and precision of image mosaic.

Image stitching algorithm based on fast feature extraction and perceptual hash

The classic feature point image stitching algorithm is time-consuming in feature extraction, and an image sparse matrix method is proposed to determine the feature points. This method first uses Laplace operator to extract the image gradient and set the threshold to obtain the sparse matrix of image segmentation, then using features from accelerated segment test (FAST) detection algorithm for feature point. Finally, the speeded-up robust features (SURF) descriptor is given to increase the stability of the feature points. This method solves the problem of time-consuming feature extraction, making the real-time image mosaic possible. In the process of image mosaic, this paper presents a method using Kalman filter to predict the overlap region, thus reducing the computational complexity. In addition, because there is always a mismatch between feature points matching, this paper proposes a method using perceptual hashing to refine the matching pair, which solves the error caused by mismatching for the next image registration and improves the registration accuracy. Experimental results show that the proposed algorithm improves the speed and precision of image mosaic.

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

ABSTRACTWith rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.

A FAST-BRISK Feature Detector with Depth Information.

RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance.

Cork as a Unique Object: Device, Method, and Evaluation

Unique Objects (UNOs) are relevant for real-world applications such as anti-counterfeiting systems. In this work, cork is demonstrated as a UNO, part of the Physical Unclonability and Disorder (PUD) system. An adequate measurement kit (illumination device) and recognition method are also devised and evaluated. Natural hills and valleys of the cork are enhanced using the illumination device and the overall robustness of the recognition application inherent to UNOs is presented. The lighting device is based on grazing light and the recognition task is based on a local feature detector and descriptor called ORB - Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The performance evaluation utilizes a private cork database (1500 photos of 500 cork stoppers) and three public iris databases. In the tests carried out on the illumination device, the results clearly show the success of capturing stable/repeatable features needed for the recognition task in the cork database. This achievement is also reflected in the perfect recognition score achieved in the cork database, in the intra-distance measure μ i n t r a , which gives the notion of average noise between measures, and in the inter-distance μ i n t e r which provides hints about the randomness/uniqueness of a cork. Regarding the recognition application, its effectiveness is further tested using the iris databases. Regardless of the fact that the recognition algorithm was not designed for the iris recognition problem, the results show that the proposed approach is capable of competing with the techniques found in the literature specially designed for iris recognition. Furthermore, the evaluation shows that the three requirements that constitute a UNO (Disorder, Operability, and Unclonability) are fulfilled, thus supporting the main assertion of this work: that cork is a UNO.

An FPGA-based implementation of corner detection and matching with outlier rejection

ABSTRACTTo meet the high frame rate requirements of correct point correspondences with a sub-pixel precision, this paper first proposes a Field Programmable Gate Array (FPGA) architecture that consists of corner detection, corner matching, outlier rejection, and sub-pixel precision localisation. In the architecture, a combined Features from Accelerated Segment Test (FAST)+ Binary Robust Independent Elementary Features (BRIEF) algorithm is adopted for detection and matching with pixel precision, a combined algorithm of Slope-based Rejection (SR) and Correlation-Coefficient-based Rejection (CCR) is used to reject the outliers, and a gradient centroid-based algorithm is used for sub-pixel precision localisation. The whole FPGA architecture is implemented on a single FPGA platform (Xilinx XC72K325T). Five image datasets with different spatial resolutions, textures, lights, rotate angles, and viewpoints are used to evaluate the performance of the FPGA-based implementation. The experimental results show that (1) the SR and CCR algorithms are effective for outlier rejection; (2) a higher correct matching rate is achieved for the image pairs that cover artificial textures than for those that cover natural textures; (3) the proposed architecture is also suitable for image pairs with small change of lights, rotate angles, and viewpoints; (4) the speed of the FPGA-based implementation can reach 280 Frames Per Second (FPS), which is 35 times faster than the Personal Computer (PC)-based implementation; and (5) the usage of FPGA resources is acceptable for the selected FPGA platform. The speed and usage of the FPGA resources can be improved when the whole FPGA-based implementation is further optimised.

Image seamless stitching and straightening based on the image block

Traditional image stitch methods based on feature point detection require long registration time for high resolution images. In the SIFT (scale invariant feature transform) algorithm, it builds difference-of-Gaussian by a linear Gauss expansion filter to obtain the feature. SIFT has poor real time. In this study, a novel image registration method based on image block is proposed to make a rough match for the blocked image and fine match in the most similar blocks by taking advantage of the FAST (features from accelerated segment test) algorithm which runs faster. This way can avoid spending a lot of time in the ineffective area, and enhance the precision and efficiency of the feature point. The accumulative error exists in the process of image stitching, so the image stitched by multiple images has wavelike effects, tilt, or distortion. The camera calibration method is utilised to eliminate the tilt and distortion of the image. The algorithm combining the optimal seam and multi-resolution fusion is adopted to fuse the stitched image and realise seamless stitch of multiple images in order to achieve a seamless image of high resolution. Simulation experimental results show that the stitching method could realise seamless stitching and straightening of multiple images.

Building Descriptors from Local Feature Neighborhoods for Applications in Semi-Dense 3D Reconstruction.

This paper introduces a novel class of descriptors constructed solely from the extraction of a feature set's qualities of location, magnitude and orientation. Oriented Feature Constellations (OFC) descriptors are unlike typical feature descriptors that separate feature extraction from definition, where local underlying pixel information is used to form a unique high-dimensional vector for matching. We use other extracted features within the local neighborhood, or local constellation, to define unique descriptors for matching. This idea is demonstrated by creating a corner orientation extension to Features from Accelerated Segment Test (FAST) corners and use these features to create descriptors, called OFC-FAST. Using a standard two stage matcher to generate dense matches across test image sets, we compare our results with those obtained using Scale Invariant Feature Transform (SIFT) features. OFC-FAST descriptors were able to generate disparity maps of higher densities while using less computational time than the comparison method was able to achieve. We believe that the capability of OFC descriptors to quickly generate higher density disparity maps will be useful for applications in semi-dense 3D reconstruction.

Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method can be effective for registering optical multimodal remote sensing images that have been captured with different imaging sensors.

A New FPGA Architecture of FAST and BRIEF Algorithm for On-Board Corner Detection and Matching.

Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC’s and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.

Alignment of UAV-hyperspectral bands using keypoint descriptors in a spectrally complex environment

ABSTRACTNear earth imaging spectroscopy has gained popularity in the recent past due to its increasing capabilities to acquire data with unprecedented abundance of spatial resolution and spectral purity. Unmanned Aerial Vehicle (UAV) based portable hyperspectral sensors employing snapshot based scanning mechanisms further advances these capabilities with improved spatial co-registration of pixels. However, unavoidable motion of UAV pose challenges in spectral co-registration which is pronounced in spectrally complex environments of heterogeneous ecosystems. In this study, the different feature descriptor techniques such as Harris-Stephens Features (HSF), Min Eigen Features (MEF), Scale Invariant Feature Transformation (SIFT), Speeded-Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), and Features from Accelerated Segment Test (FAST) were evaluated to align hyperspectral bands in a spectrally complex environment. A band alignment workflow was devised and operated in different band-wise arrangements (spectral order and temporal order) of the acquired hypercube. The co-registration accuracy between the adjacent band pairs (reference-transformed) upon registration was estimated using Root-Mean-Square Error (RMSE) and Pearson’s Correlation Coefficient (PCC) based approaches. Furthermore, a standard transformation workflow was used to evaluate the efficiency of the different feature descriptor based band-to-band registration approaches.

An efficient content based image retrieval system using BayesNet and K-NN

In the progression of web and multi-media, substantial measure of pictures is created and appropriated, to viably store and offer such vast measure of bulky database is a big issue. In this way, Content Based Image Retrieval (CBIR) techniques are used to retrieve images from the massive database based on the desired information. In this proposed work, we are considering two local image feature extraction methods, namely, SIFT and ORB. Scale Invariant Feature Transform (SIFT) is used for detecting features and feature descriptor of an image. Oriented Fast Rotated and BRIEF (ORB) uses FAST (Features from Accelerated Segment Test) key point detector and binary BRIEF (Binary Robust Independent Elementary Features) descriptor of an image. K-Means clustering algorithm is also used in the present paper for analyzing the data, which generates number of clusters using the descriptor vector. Locality Preserving Projection (LPP) is employed to reduce the length of the feature vector to enhance the performance of image retrieval system. For classification, we have considered two classifiers, namely, BayesNet and K-Nearest Neighbours (K-NN). Wang image dataset has been used for experimentation work. We have accomplished the highest precision rate of 88.9% using proposed CBIR system.