Scale Invariant Feature Transform Algorithm Research Articles

User Authentication System Based on Speech and Cascade Hybrid Facial Feature

With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system.

Indoor Positioning Using PnP Problem on Mobile Phone Images

As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management.

Research on velocity measurements of the hypersonic turbulent boundary layer based on the nano-tracer-based planar laser scattering technique

As one of the most typical flow structures in hypersonic flow, the hypersonic turbulent boundary layer (TBL) is of great significance in the optimization of the aerodynamic configuration design of hypersonic vehicles. The nano-tracer-based planar laser scattering technique was utilized to obtain velocity results of the fully developed hypersonic (Mach 6.0) TBL. The scale-invariant feature transformation algorithm was used to obtain velocity information. Based on the results of mean velocity and fluctuating velocity distribution, the feasibility of this method has been verified.

Structure tensor‐based SIFT algorithm for SAR image registration

The scale-invariant feature transform (SIFT) algorithm is the most widely used feature extraction as well as a feature matching method in remote sensing image registration. However, the performance of this algorithm is affected by the influence of speckle noise in synthetic aperture radar (SAR) images. It reduces the number of correct matches as well as the correct matching rate in SAR image registration. Moreover, SAR image registration is considered to be a challenging task as the images generally have significant geometric as well as intensity variations. To address these problems, a structure tensor-based SIFT algorithm is proposed to register the SAR images. At first, the tensor diffusion technique is used to construct the scale layers. Then, the features are extracted in the scale layers. Finally, feature matching is performed between the input SAR images and correct matches are identified. The proposed method can increase the number of correct matches as well as position accuracy in registration. Experiments have been conducted on five SAR image pairs to verify the effectiveness of the method.

Novel Biometric Fusion System using GA-PSO and ANN

Technology advancements have led to the emergence of biometrics as the most relevant future authentication technology. On practical grounds, unimodal biometric authentication systems have inevitable momentous limitations due to varied data quality and noise levels. The paper aims at investigating fusion of face and fingerprint biometric characteristics to achieve a high level personal authentication system. In the fusion strategy face features are extracted using Scale-Invariant Feature Transform (SIFT) algorithm and fingerprint features are extracted using minutiae feature extraction. These extracted features are optimized using nature inspired Genetic Algorithm (GA). The efficiency of the proposed fusion authentication system is enhanced by training and testing the data by applying Artificial Neural Network (ANN). The quality of the proposed design is evaluated against two nature inspired algorithms, namely, Particle Swarm Optimization (PSO)and Artificial Bee Colony (ABC) in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR) and recognition accuracy. Simulation results over a range of image sample from 10 to 100 images have shown that the proposed biometric fusion strategy resulted in FARof 2.89, FAR 0.71and accuracy 97.72%.Experimental evaluation of the proposed system also outperformed the existing biometric fusion system.

A New Sample Consensus Based on Sparse Coding for Improved Matching of SIFT Features on Remote Sensing Images

In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique. To this end, scale-invariant feature transform (SIFT) features are used to select interesting points for image matching. The extracted points contain some differences and similarities in two images captured from the same area (but different in sensor resolution, azimuth, elevation, contrast, illumination, etc.); in such a case, similar points should be extracted and other dissimilar should be eliminated. In this article, we greatly improve the matching between two images using the SCSC through checking points altogether. Moreover, the proposed method is shown to have better results than standard alternative methods such as random sample consensus (RANSAC) when the number of feature points is too much or have noise. However, it should be noted that for a low-noise and distortion rate, the proposed method and the RANSAC yield similar results. In general, the proposed method using sparse coding achieves a higher correct match rate than the SIFT algorithm. In order to illustrate this issue, the proposed method is compared to other updated matching and registration methods based on the SIFT algorithm. The obtained results confirm the accuracy of this claim and show that the proposed algorithm is accurate between 0.48% and 7.68% rather than SVD-RANSAC, Hoge, Stone, Foroosh, Leprince, Nagashima, Guizar, Youkyung, Lowe, Preregistration, IS-SIFT, SPSA, Gong, Standard SIFT, IS-SIFT, UR-SIFT, Sourabh, and Han methods.

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

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

Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information.

Protein-Protein Interactions (PPIs) play an irreplaceable role in biological activities of organisms. Although many high-throughput methods are used to identify PPIs from different kinds of organisms, they have some shortcomings, such as high cost and time-consuming. To solve the above problems, computational methods are developed to predict PPIs. Thus, in this paper, we present a method to predict PPIs using protein sequences. First, protein sequences are transformed into Position Weight Matrix (PWM), in which Scale-Invariant Feature Transform (SIFT) algorithm is used to extract features. Then Principal Component Analysis (PCA) is applied to reduce the dimension of features. At last, Weighted Extreme Learning Machine (WELM) classifier is employed to predict PPIs and a series of evaluation results are obtained. In our method, since SIFT and WELM are used to extract features and classify respectively, we called the proposed method SIFT-WELM. When applying the proposed method on three well-known PPIs datasets of Yeast, Human and Helicobacter.pylori, the average accuracies of our method using five-fold cross validation are obtained as high as 94.83, 97.60 and 83.64 percent, respectively. In order to evaluate the proposed approach properly, we compare it with Support Vector Machine (SVM) classifier and other recent-developed methods in different aspects. Moreover, the training time of our method is greatly shortened, which is obviously superior to the previous methods, such as SVM, ACC, PCVMZM and so on.

Virtual Reality Image Technology Assists Training Optimization of Motion Micro-Time Scale

Virtual reality image local features are highly repetitive and are not easily affected by target occlusion, so research on virtual reality image local feature extraction techniques has received increasing attention. For the purpose of global scale selection, this article considers the virtual reality image in the global scope from the theoretical perspectives of the continuity of virtual reality images, the repeatability of virtual reality image features, the scale of scene virtual reality images in the global scope, and the visual saliency. This paper gives a general framework of multi-scale geometric analysis and particle swarm optimization virtual reality image matching algorithm. For the problem of virtual reality image matching, the original particle swarm optimization algorithm is improved. A more reasonable inertia weight change formula, a method for selecting the optimal solution when two objectives are optimized at the same time, and a method for selecting a reasonable input are designed. In this paper, performance evaluation experiments are performed on several feature description algorithms. The experimental results show that the descriptors constructed by these feature description algorithms are significantly more robust than SIFT (Scale Invariant Feature Transform) descriptors. The improved multi-scale geometric analysis and particle swarm optimization are robust due to the introduction of multiple support domains. It has the best discriminative ability, but at the same time, the introduction of multiple support domains leads to the worst real-time algorithm; DGOH (Dual Gradient Orientation Histogram) algorithm is more robust than FRDOH (Fast Repre-sentation Based on a Double Orientation Histogram) and SIFT algorithm, and DGOH algorithm has the best real-time performance.

Detecting Features via Modified Harris Corners and Matching Them through SIFT

Interpreting images spatially is a daunting task which is achieved by detecting corners and features. The most important task of detecting features is achieved by Harris Corner Algorithm. The algorithm is not robust to different scale of the same image. The algorithm may detect corner but when the image is zoomed in, the corner may appear as ridges. We use the corners detected from Harris Corner algorithm and treat these as key points to pass into Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm extracts descriptor vector of dimension 128 X 1 from these corners and can be used to find similarity between different images. This process is quite robust to noise, intensity, scale and occlusion and is used for matching images from a database of descriptors. We have investigated both the algorithms in this paper and made a modified version of Harris Corner algorithm by performing different kind of thresholding, both of them gave a little different result.

Automatic Classification for Ground Targets under Complex Background Based on Bag of Words Model

In order to improve the performance of the automatic classifier for ground targets under complex background, a new classifier which was based on bag of words model and support vector machine (SVM) was build. Firstly, the keypoints of a target image were extracted into vectors by using Scale-Invariant Feature Transform algorithm, and vectors which were extracted from all trained images were merged into large vectors. Secondly, the large vectors were clustered into small vectors which included K vectors by using K-means clustering algorithm. To analyze how different values of K affected the classifying results, K was set to several different numbers. Thirdly, vectors which were extracted from all trained images were classified, and K-dimensional numerical vectors were obtained. Finally, the classifier was build with SVM algorithm, and the K-dimensional vectors were as input features of the classifier. The training images were randomly chosen from 90% of images dataset which included missile launch vehicles and tanks, and the rest 10% were as the testing images. The trained classifier is tested, and the f1-score value is 0.79. The results show that the new classifier can perform well.

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

With the rapid development of computer networks and multimedia technologies, images, which are important carriers of information dissemination, have made human cognition of things easier. Image recognition is a basic research task in computer vision, multimedia search, image understanding and other fields. This paper proposes a hierarchical feature learning structure that is completely automatically based on the original pixels of the image, and uses the K-SVD (K-Singular Value Decomposition) algorithm with label consistency constraints to train the discriminant dictionary. For different types of image data sets, the algorithm only extracts image blocks. After dense sampling, an efficient OMP (Orthogonal Matching Pursuit) encoder is used to obtain a layered sparse representation. The improved SIFT (Scale Invariant Feature Transform) algorithm is used to solve the difficult problem of multimedia visual image stereo matching. The feature point extraction and stereo matching of multimedia visual images, different scales and different viewpoint images are analyzed separately. Aiming at a large number of low-dimensional geometric features of 3D images, this paper studies the extraction and sorting strategies of low-dimensional geometric features of 3D images. A sparse representation method for 3D images is proposed, and the sparseness of image features is evaluated. This further improves the accuracy of 3D image representation and the robustness of 3D image recognition algorithms.

Sequence Image Registration for Large Depth of Microscopic Focus Stacking

The large depth of images for microscopic measurement can be achieved by using focus stacking techniques with a small depth of field of objective lens. It is implemented by fusing the image sequences of short depth images. However, the non-linear movement of the objective imaging system or the measured object caused by the moving stage straightness error brings the misalignment of the image sequences, such as transversal translation, rotation, and tilting. All of these interferences, as well as the image brightness variation must be corrected by image registration before fusing the image sequences. In this paper, a fast-automatic registration method based on the scale invariant feature transform (SIFT) is proposed. It is achieved by firstly segmenting the focal regions of the image sequences through fast edge detection. Then the image features are extracted within the small segmented focal areas. It greatly reduces the computational cost of feature extraction and the following steps of image correction, and alignment. In the process, the random sampling consistency (RANSAC) algorithm is also used to remove the mistake features. The Laplacian pyramid method is adopted for the large depth of image fusion. The experimental results show that the proposed method is more efficient than the traditional SIFT algorithm. Its registration efficiency is improved by about 60%. This method facilitates the high-precision and real-time imaging of a monocular three-dimensional focus stacking.

Secure outsourcing SIFT: Efficient and Privacy-Preserving Image Feature Extraction in the Encrypted Domain

Multimedia data needs huge storage space, and application of multimedia data needs powerful capability of computing. Cloud computing can help owner of multimedia data to deal with it. But, multimedia data on cloud may reveal privacy of data owner, such as sex, hobbies, address, looks, and so on. Data owner can encrypt multimedia data for confidentiality before uploading it to cloud. However, encrypted multimedia data makes its utilization difficult. In this paper, we first discover pre-existing schemes have problems of huge storage space, security and low efficiency due to their inefficient and insecure algorithms. Then, we provide an effective and practical privacy-preserving scale-invariant feature transform (SIFT) scheme for encrypted image. It uses leveled homomorphic encryption based on our new encoding schemes, our new homomorphic comparison, division and derivative encryption. Our new secure SIFT scheme can realize higher computing efficiency, greatly reduce communication costs and interactive times between user and server, and perform correct feature key point detection, accurate feature point description and image matching. We evaluate security and efficiency of our new secure SIFT scheme, and compare our new secure SIFT scheme with other schemes in detail. The result shows that it is closest to the original SIFT algorithm.

IMPLEMENTING SKETCH-BASED IMAGE RETRIEVAL SYSTEM USING EDGE DETECTION AND SIFT ALGORITHM

Throughout the past few years, several researchers have introduced various methods and various algorithms for a precise and dependable sketch-based image retrieval system. In this paper, a proposed sketch-based image retrieval system is introduced. The framework goes over two phases: creating the sketch dataset phase and implementing SIFT (scale invariant feature transform) algorithm. The sketch dataset was created by selecting 100 colored image passed through canny edge detection operator. The system tends to enter a line-based/hand-drawing sketch and applies the SIFT algorithm to match between the input sketch and all sketches in the dataset. SIFT is one of the main efficient algorithms that are used to make description and matching, since it works on large keypoints. This system retrieves images depending on sketch image, and the result of matching will retrieve images that are approximate the entered sketch. The proposed system is assessed according to the measures that are utilized in detection, description and matching grounds, which are precision, recall and accuracy measures. The system showed (96 %) accuracy for line based sketches and (84%) for hand drawing where the detection was identical.

Digital Image Falsification Detection System for Effective Data Communication

In this proposed system a digital imagefalsification can be identified using the combination of both adaptive over block based segmentation, feature keypointbased feature extraction algorithms(Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF)) and forgery region extraction algorithm. The proposed falsification detection algorithm comprises both block based falsification detection algorithm (adaptive over block based segmentation and block feature matching algorithm) and the keypoint based falsification detection algorithm(forgery region extraction algorithm). Adaptive over block based Segmentation algorithm adaptively segments the input digital image into separate(non overlapped) blocks in irregular manner. Scale Invariant Feature Transform (SIFT) algorithm and Speeded Up Robust Features (SURF) algorithms are used to draw out features from the segmentedblocks as a block features. Then the extracted features are matched with the feature points of other segmented block. If the feature key points are matched with any other feature point presents in the segmented blocks, then the matched feature points are marked as Labeled key Points (LKP), which can be doubted as a forged regions. Finally, the Forgery Region Extraction algorithm can be used to detect the forged region from the input digital image based on the extracted labeled feature points. The experimental outcomesdisplay that the novelfalsification detection system can accomplished the requirements compared with the existing digital imagefalsification detection methods.

Polarization Image Registration Algorithm Based on Improved SIFT Descriptor

With the application of polarization imaging technology more and more widely the research on how to obtain polarization information quickly and efficiently has always been the focus of people’s attention. In view of the numerous edge points generated by DoG (Difference of Gaussian) in the process of feature point extraction by SIFT(Scale-invariant Feature Transform) algorithm, which leads to tedious and time-consuming operation, an improved Sobel algorithm combined with SIFT operator image registration algorithm is proposed in this paper. The concrete implementation steps are as follows: firstly, the traditional two-direction Sobel algorithm is supplemented to eight directions, and then edge points are extracted. Then SIFT algorithm is used to detect feature points. Then, the feature points belonging to edge points are eliminated by Sobel operator. This method reduces the number of matching cycles. The processing results of RGB image and polarization image with this algorithm are compared with those of traditional SIFT algorithm, and the time consumption in the registration process is reduced by 60.5% on the premise of ensuring the accuracy.

Scale Invariant Feature Transform Descriptor Robustness Analysis to Brightness Changes of Robowaiter Vision Sensor System

The purpose of this research is to identify problem detection features in computer vision that are affected by changes in brightness. The presented descriptor is Scale Invariant Feature Transform (SIFT). The method used in this study is an algorithm in computer vision to detect and describe local feature in image which robustly identify object and invariant to uniform scaling, orientation, brightness changes, and partially invariant to affine distortion. We implement this algorithm to Robowaiters object detection system that must detect and recognize objects around its task like food, beverage, refrigerator, and any kitchen objects. For this analysis case, we use beverage box image for sample image. The algorithm detects and recognize the image in normal brightness, and then the image brightness value increased and decreased. The result is that the algorithm successfully detects and recognizes the object presented and distinguishes it with a success rate of 93.5% increase in image brightness and 95.5% decrease in image brightness. it can be concluded that the SIFT algorithm is robust enough to change the lighting for our case.

ASP-SIFT: Using Analog Signal Processing Architecture to Accelerate Keypoint Detection of SIFT Algorithm

The scale-invariant feature transform (SIFT) algorithm is still one of the most reliable image feature extraction methods. Despite its excellent robustness on various image transformations, SIFT’s intensive computational burden has been severely preventing it from being used in real-time and energy-efficient embedded machine vision systems. To reduce processing time and energy cost while executing SIFT, an analog signal processing architecture, analog signal processing (ASP)-SIFT, is proposed in this article. In ASP-SIFT, the Gaussian pyramid construction, difference-of-Gaussian (DoG) pyramid construction and keypoint locating, which are the primary steps of the keypoint detection part of the SIFT algorithm, are done directly with analog circuit networks. Thus, by completing keypoint detection in the analog domain, the total processing time is approximately equal to the settling time of the circuit network. Besides, by adopting a current-mode circuit network operating in the subthreshold region, the power dissipation would be very low. Simulation results show that the total processing speed for a typical video graphics array (VGA)-format ( $640\times480$ ) image is up to 2.3 kframes per second, which is at least $3.26\times $ faster than the state-of-the-art digital hardware accelerators, while the system power is 94.5 mW and the energy consumption is only $40~\mu \text{J}$ per frame.

An Algorithm for Determining SIFT Matching Score for Dorsal Vein Recognition System

Vein pattern recognition has been an increasingly biometric branch nowadays. This technology has many advantages over other biometric technologies. This technology works only on alive person. Vein pattern of every person is unique even in case of twins. Whole procedure of registration for verification of a person gives a hygienic opportunity. Vein pattern of a person remains constant throughout the life until and unless, physically damage. Recognition through veins cannot be affected from aging, color and physical environment because veins are present underneath the skin. NIR cameras of wavelength of 700 nm to 1000 nm are used to capture the images of vein patterns. When infrared radiation falls on the veins, these get illuminated in dark color due to the absorption of radiation by the hemoglobin present in the veins. The SIFT (Scale-Invariant Feature Transform) algorithm has given very good results in feature extortion and matching but it does not provide matching score of features. In this work, we proposed an algorithm to determining the matching score of pattern matched through SIFT algorithm. For experimental purpose, we performed image acquisition, pre-processing, feature extraction and filtering to eliminate noise from the images. We tested our algorithm on the database of 160 persons and we calculated performance of our algorithm in terms of FAR and FRR 2.7% and 4.5% respectively