Scale Invariant Feature Transform Algorithm Research Articles

An improved SIFT algorithm for robust emotion recognition under various face poses and illuminations

To address the variabilities of the number and position of extracted feature points for the traditional scale-invariant feature transform (SIFT) method, an improved SIFT algorithm is proposed for robust emotion recognition. Specifically, shape decomposition is first performed on the detected facial images by defining a weight vector. Then, a feature point constraint algorithm is developed to determine the optimum position of the feature points that can effectively represent the expression change regions. On this basis, the SIFT descriptors are applied to extract the regional gradient information as feature parameters. Finally, the support vector machine classifier combined with the principal component analysis method is used to reduce the feature dimensions and facial expression recognition. Experiments have been performed under different conditions, i.e., varied illuminations, face poses and facial moisture levels, using 15 participants. In the cases of frontal face and 5-degree face rotation views, the average recognition accuracies are 98.52% and 94.47% (no additional light sources), as well as 96.97% and 95.40% (two additional light sources), respectively. In addition, as an effective supplement to the problem of changes in illumination, the average recognition ratios are 96.23% and 96.20% under dry and wet face conditions, respectively. The experimental results reveal the robust performance of the proposed method in facial expression recognition.

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Road visual navigation relies on accurate road models. This study was aimed at proposing an improved scale-invariant feature transform (SIFT) algorithm for recovering depth information from farmland road images, which would provide a reliable path for visual navigation. The mean image of pixel value in five channels (R, G, B, S and V) were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm, for achieving precise location of the feature points and ensuring the uniformity and density of the feature points. The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45o in eight directions was then treated as the feature vector, and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points. In order to achieve the depth information of farmland road images, the energy method of feature points was used for eliminating the mismatched points. Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48% and 5.6 s, respectively, with the accuracy for depth recovery of –7.17%-2.97% in a certain sight distance. The mean uniformity, time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%, 5.0-6.5 s, and higher than 88%, respectively, indicating that performance for achieving the feature points (e.g., uniformity, matching accuracy, and algorithm real-time) of the improved SIFT algorithm were superior to that of conventional SIFT algorithm. This study provides an important reference for navigation technology of agricultural equipment based on machine vision. Keywords: scale-invariant feature transform (sift), feature matching, canny operator, energy method of feature point, farmland road, depth recovery, visual navigation DOI: 10.25165/j.ijabe.20191204.4821 Citation: Yao L J, Hu D, Yang Z D, Li H B, Qian M B. Depth recovery for unstructured farmland road image using an improved SIFT algorithm. Int J Agric & Biol Eng, 2019; 12(4): 141–147.

Optimal Threshold Based Brain Image Fusion for Brain Cancer Detection using Firefly algorithm

In this paper an attempt is made to diagnose brain disease like neoplastic disease, cerebrovascular disease, Alzheimer disease, fatal disease, Sarcoma disease by effective fusion of two images. Two images are fused in three steps: Step 1.Segmentation: The images are segmented on the basis of optimal thresholding; thresholds are optimized with natural inspired firefly algorithm by assuming fuzzy entropy as objective function. Image thresholding is one of the segmentation techniques which is flexible, simple and has less convergence time as compared to others. Step 2: the segmented features are extracted with Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: Finally fusion rules are made on the basis of interval type-2 fuzzy (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark Image fusion data set and proved better in all measuring parameters.

A COMPACT SIFT-BASED STRATEGY FOR VISUAL INFORMATION RETRIEVAL IN LARGE IMAGE DATABASES

This paper applies the Standard Scale Invariant Feature Transform (S-SIFT) algorithm to accomplish the image descriptors of an eye region for a set of human eyes images from the UBIRIS database despite photometric transformations. The core assumption is that textured regions are locally planar and stationary. A descriptor with this type of invariance is sufficient to discern and describe a textured area regardless of the viewpoint and lighting in a perspective image, and it permits the identification of similar types of texture in a figure, such as an iris texture on an eye. It also enables to establish the correspondence between texture regions from distinct images acquired from different viewpoints (as, for example, two views of the front of a house), scales and/or subjected to linear transformations such as translation. Experiments have confirmed that the S-SIFT algorithm is a potent tool for a variety of problems in image identification.

Memristor bridge-based low pass filter for image processing

This paper highlights the memristor bridge-based lowpass filter (LPF) and improved image processing algorithms along with a novel adaptive Gaussian filter for denoising image and a new Gaussian pyramid for scale invariant feature transform (SIFT). First, a novel kind of LPF based on the memristor bridge is designed, whose cut-off frequency and other traits are demonstrated to change with different time and memristance. In light of the changeable parameter of the memristor bridge-based LPF, a new adaptive Gaussian filter and an improved SIFT algorithm are presented. Finally, experiment results show that the peak signalto-noise ratio (PSNR) of our denoising is bettered more than 2.77 dB compared to the corresponding of the traditional Gaussian filter, and our improved SIFT performances including the number of matched feature points and the percent of correct matches are higher than the traditional SIFT, which verifies feasibility and effectiveness of our algorithm.

Personal Authentication Mechanism Based on Finger Knuckle Print

For authentication purposes, the identification and verification of a user is done by biometric traits like finger print, face, iris and gait, etc. Among the various traits finger print is mostly used in commercial applications for recognizing user's identity. The other hand based modalities such as vein, and finger knuckle are gaining importance. This paper proposes a methodology for secure biometrics authentication using Finger Knuckle Print (FKP). The texture patterns from finger knuckle are extracted using Gabor with Exception-Maximization (EM) algorithm and the feature vectors from these texture patterns are acquired using Scale Invariant Feature Transform (SIFT) algorithm. The main focus is to reduce the false rejection rate without increasing the false acceptance rate and to improve the performance over the conventional hand based modalities. The performance is compared with Genuine Acceptance Rate (GAR) and False Rejection Rate (FRR). One of the advantages of FKP authentication is its user friendliness in data collection.

Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

Pose detection of parallel robot based on improved RANSAC algorithm

For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the detection speed, and accuracy cannot meet the requirement of the closed-loop control. So a pose detection method based on improved RANSAC algorithm is presented. First, considering that the image of parallel robot is rigid and has multiple corner points, the Harris–Scale Invariant Feature Transform algorithm is adopted to realize image prematching. The feature points are extracted by Harris and matched by Scale Invariant Feature Transform to realize good accuracy and real-time performance. Second, for the mismatching from prematching, an improved RANSAC algorithm is proposed to refine the prematching results. This improved algorithm can overcome the disadvantages of mismatching and time-consuming of the conventional RANSAC algorithm by selecting feature points in separated grids of the images and predetecting to validate provisional model. The improved RANSAC algorithm was applied to a self-developed novel 3-degrees of freedom parallel robot to verify the validity. The experiment results show that, compared with the conventional algorithm, the average matching time decreases by 63.45%, the average matching accuracy increases by 15.66%, the average deviations of pose detection in Y direction, Z direction, and roll angle [Formula: see text] decrease by 0.871 mm, 0.82 mm, and 0.704°, respectively, using improved algorithm to refine the prematching results. The real-time performance and accuracy of pose detection of parallel robot can be improved.

Distinctive image features from illumination and scale invariant keypoints

This paper proposes a novel local feature descriptor of the image, which is named iSIFT (illumination and Scale Invariant Feature Transform), based on SIFT (Scale Invariant Feature Transform) improved by LBP (Local Binary Pattern), in order to combine the robustness advantages of LBP descriptor for illumination change and that of SIFT for scaling. It addresses the following problems: (1) SIFT algorithm is poor in describing the local feature extraction from an image when lighting condition changes; (2) SIFT algorithm cannot accurately extract the feature points or can only extract only few of them from the blurred image and the image of an object with smooth edges. Each of the scale-space representation, namely, L(x, y, kσ), in Gaussian pyramid of the image I(x, y) on SIFT descriptor is calculated by using LBP in order to obtain the corresponding LBP image, which is denoted by LBP(L(x, y, kσ)). The obtained LBP(L(x, y, kσ)) replaces the original corresponding scale-space representation L(x, y, kσ) to construct the LBP-Gaussian pyramid, and the difference between each two neighboring LBP(L(x, y, kσ)) in LBP-Gaussian pyramid is used to replace the original DoG pyramid in SIFT descriptor to detect extreme points. The results of the experiments suggest that iSIFT descriptor improves the precision of image feature matching and the robustness under changed lighting conditions compared with that of SIFT algorithm, and iSIFT descriptor can extract more feature points from the blurred image and the image with smooth edges as well as having stronger robustness for lighting, rotation and scaling.

GPU-based parallel optimization for real-time scale-invariant feature transform in binocular visual registration

Scale-invariant feature transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping, and large-scale image retrieval. Although SIFT descriptor is highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real-time response and in algorithms dealing with very large-scale databases. In order to be effective for image matching process in near real-time, the Compute Unified Device Architecture (CUDA) application programming interface of a graphics processing unit (GPU) is incorporated to speed up or improve the SIFT method. Experimental results show that the proposed GPU-based SIFT framework is suitable for image application in real time. It can improve the image matching process both in time and accuracy compared with conventional SIFT method.

Optimal flight parameters for unmanned aerial vehicles collecting spatial information for estimating large-scale waste generation

ABSTRACT Large-scale waste and debris from disasters and building construction can cause secondary damage and environmental pollution. Satellite images and the Global Positioning System are being used to calculate the amounts of such waste. However, efficiency in terms of accuracy and cost remains problematic. To solve this problem, this study used an unmanned aerial vehicle (UAV) to build three-dimensional (3D) spatial information for large-scale waste generation. Appropriate parameters were selected to quickly and accurately estimate the amount of waste. Fifty-six scenarios were distinguished according to parameters such as flight altitude, image overlap, and number of ground control points (GCPs). Images were then registered by implementing the SIFT (Scale Invariant Feature Transform) and SfM (Structure from Motion) algorithms to build 3D spatial information and estimate the amount of waste. The accuracy was generally high when flight altitude and image overlap were high, and the number of GCPs was specific. Scenario A, with the lowest flight altitude and overlap, did not yield any registrable images. The results of the flight altitude showed a different tendency from those of existing studies. This could be attributable to the difference in the distance covered on the ground by an image in the width direction, which depended on the characteristics of study sites. Among all the scenarios, D-7 had the highest accuracy, with a root-mean-square error of about 0.08 m. In this scenario, the amount of waste was estimated to be approximately 767,571.3 m3. The top 10 scenarios in terms of the accuracy of x, y, and z locations displayed similar tendencies with regard to the amount of waste. In contrast, the bottom 15 scenarios showed a variety of results. When the accuracy and flight time of each scenario were considered, scenarios H-3, H-4, and H-5 achieved the highest accuracy in a short time. Thus, these scenarios were the most effective and applicable for estimating the amount of waste. Consequently, parameters such as flight altitude, image overlap, and GCPs need to be considered when estimating large-scale waste associated with a difference in elevation using a UAV.

Judgement of critical state of water film rupture on corrugated plate wall based on SIFT feature selection algorithm and SVM classification method

As an essential steam-water separation device in the secondary loop of nuclear power plant, the steam-water separation efficiency of corrugated plate dryer plays a decisive role in the economic indicators and safety of a nuclear power plant. Thus the research on the internal steam-water separation mechanism is of extremely significant value and the flow characteristics of the free falling water film on the corrugated plate wall are the basis of the research on water film rupture. The phenomenon of water film rupture on the wall surface will have a great impact on the secondary carrying, gas-water separation efficiency and even the economic indicators of nuclear power plants. However, it is meaningful to define whether the water film has broken and the fracture criterion because the method of defining the water film breakdown by the naked eye is extremely unreliable and inaccurate. Therefore, this paper proposes a method for defining the critical point of water film rupture on corrugated plate wall by the application of the SIFT (Scale-invariant feature transform) feature selection algorithm and SVM (Support Vector Machine) classification method. The SIFT (Scale-invariant feature transform) algorithm is applied to obtaining the characteristic information of the wall water film rupture image when the airflow velocity in the flow channel of the experimental section of the corrugated plate captured by the high-speed camera is gradually increased. Meanwhile, the two-class model of SVM (Support Vector Machine) is used to classify water film images and define the critical point of water film rupture on the corrugated plate wall by training, detection and inversion. The results show that the algorithm based on SIFT feature selection and SVM classification method can well define the critical point of water film rupture, keep the certainty and consistency of the water film rupture determination benchmark, and avoid the introduction of human identification error. It greatly improved the stability of the measurement and provides a reference for the determination of the baseline of water film rupture.

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs.

A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m Geoeye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements.

A novel SIFT architecture and ASIC implementation for real time SOC application

Local feature detection and description algorithms such as scale invariant feature transform (SIFT) algorithm are among the most commonly used techniques in computer vision. They are used mainly to detect and extract high-level information from low-level (pixel) information in images. These algorithms are computationally intensive and its pure software implementations are far from reaching real-time performance especially on embedded systems with limited computational power. In this paper, an Application-Specific Integrated Circuit (ASIC) implementation of the SIFT algorithm is proposed that is suitable for real-time image processing. In the Gaussian scale space generation step, several techniques were used to substantially reduce the hardware complexity: the multiplierless multiple constant multiplication, the symmetrical property of Gaussian mask in addition to the common sub expression elimination algorithm. Through the re-arrangement of SIFT’s 128 values in a specific approach, a new multi-ported memory is implemented to reduce the memory size. Furthermore, a novel mechanism is applied to continuously monitor the active window in the input and the intermediate results. The proposed technique does not only improve the performance of the SIFT implementation, but it also keeps the features extraction accuracy exactly the same as the software implementation. In this work, for an image size of $$256 \times 256$$ a frame rate of 56 fps was achieved. Additionally, the results confirm that the proposed architecture has lower hardware and memory costs compared to other works in the literature (FPGA based implementations). Lastly this architecture was implemented using Standard Cell Library Based ASIC flow using 65 nm low power GF library, it occupied an area of 1.4 $${{{\mathrm{mm}}}^2}$$ and consumed a total power of 30.4604 mW which makes it very suitable for integrated System on Chips (SoC) applications.

A new feature-based self-embedding image watermarking scheme

The performance of digital watermarking algorithm is mainly manifested in invisibility and robustness. At present, many algorithms improve these two aspects by sacrificing embedding capacity for the reason that if watermark embedding amount is small, the watermark has little influence on the visuality of the cover image, and its robustness is also higher. Therefore, how to improve the anti-attack performance of the algorithm without reducing the amount of embedding has become a major research direction. This paper proposed a new robust watermarking scheme based on feature extraction techniques. Firstly, the SIFT (scale invariant feature transform) algorithm is used to extract the stable feature points from cover image to form embedding region. Then watermark is composed of the cover’s texture features obtained by LBP (local binary pattern) extracting method. Finally, embedding region is transformed by singular value decomposition (SVD) and the watermark is embedded into its singular value vector. The simulation results showed that the watermark embedding capacity of this algorithm is relatively high, and it has good resistance to common image processing and geometric transformation.

Enhanced Fiilltterr-based SIFT Apprroach fforr Copy-Move Forrgerry Dettecttiion

Image forgeries are applied to give the digital images othermeanings or to deceive the viewers. Image forgeries appear inmany cases such as judges in courts, cybercrimes, military andintelligence deception, or defamation of important characters.There are many different types of image forgeries such as copymove forgery, image retouching, image splicing, image morphing,and image resampling. Copy move forgery is the widest type andeasy to apply between all digital image forgeries. Scale InvariantFeatures Transform (SIFT) algorithm is used strongly to detectcopy move forgeries due to its efficiency in digital image analysis.SIFT algorithm is extracting image features, which are invariant togeometrical transformations such as scaling, translation, androtation. These features are used in performing the matchingbetween different views of a scene or an object. This paperenhances the efficiency of using SIFT algorithm in detecting copymove forgery by two ways. Firstly, it enhances the image itself byapplying different types of digital filters to reinforce the imagefeatures giving the ability to detect forgeries. Butterworth low-passfilter, a high-pass filter, and the combination of them are appliedto this task. Secondly, the matching strategy is adapted based ona new thresholding approach to increase the true positive rateand decrease the false positive rate. Experimental results showthat the proposed approach gives better results compared withtraditional copy-move detection approaches. In addition, it gives better stability and reliability to different copy-move forgery conditions.

Method for Fused Phase and PCA Direction Based on a SIFT Framework for Multi-Modal Image Matching

The high noise and local deformation of multi-modal images reduce the accuracy of scale invariant feature transform (SIFT) image matching. To solve this problem, a new method based on the SIFT framework, which fuses the phase consistency optimization strategy and the gradient direction of principal component analysis (PCA) with the 8 direction of latitude reduction, is proposed in this paper. This method fuses the histogram of the orientated phase congruency (HOPC) method to extract the direction of the image, and adopts PCA to extract the main direction, which effectively solves the problem that the matching accuracy decreases due to the inversion of the direction of the image. Using the image phase instead of gradient intensity, the difficult problem of direction extraction is effectively solved when image edge characteristics are not obvious. Finally, the random sample consistency (RANSAC) algorithm is used to eliminate false match points. Simulation and experiments show that compared with the SIFT algorithm and PCA-SIFT algorithm, the proposed method improved the number of match points and matching accuracy, significantly reduced the mismatching rate. The statistical results show that the number of match points raised in this paper increases by 20.1% and 200% respectively compared with the former two algorithms.

Shallow water bathymetry from WorldView-2 stereo imagery using two-media photogrammetry

ABSTRACT Two-media photogrammetry appears to be a useful technology for mapping shallow water depth. The objective of this paper is to explore the feasibility of shallow water bathymetry using satellite two-media photogrammetry. This paper presents a two-media photogrammetry technique for WorldView-2 stereo multispectral imagery. In this method, near-infrared band is used for sun glint elimination. A combination of Scale Invariant Feature Transform and Semi-Global Matching algorithm is adopted for dense stereo matching. Rational Function Model is applied for raw Digital Elevation Model generation and the water surface elevation is determined by interpolating water edge elevation. Finally, the approximate refraction correction model is adopted in the water region to correct the vertical coordinate offsets. Our experimental results of Ganquan Island and Zhaoshu Island show that a reasonable depth estimate with relative error less than 17% and 14% can be obtained for the water depth range between 5 and 20 m, and the root-mean-square error are 2.09 m and 1.76 m, respectively. However, less accurate estimate is found for the very shallow water depth range between 0 and 5 m. The overall accuracy of satellite two-media photogrammetry is at the same level with the traditional multispectral-based model for bottom depth retrieval.

A hybrid approach to find cloned objects in copy move forged images

In this paper, we have proposed a new hybrid approach to detect the cloned object in images. In copy-move forgery, images are forged by making the clone of objects of the same image within the image. In our proposed work, we have integrated two states of art techniques named adhoc method and principle component analysis (PCA) based scale invariant feature transform (SIFT) method. In two step process, we have preprocessed the object image by the proposed dynamic block size technique based local contrast modification and contrast limited adaptive histogram equalisation (DBST-LCM CLAHE) up to desired PSNR and grey level and then after integrated adhoc, and PCA based SIFT algorithm to design a hybrid approach. Proposed method tested over CoMoFoD image database. Proposed method shows the better performance in comparison of the state of art techniques regarding false positive rate (FPR), true positive rate(TPR), and PSNR.

改进的尺度不变特征变换算法并行加速双目测距系统及其实现

改进的尺度不变特征变换算法并行加速双目测距系统及其实现