Scale Invariant Feature Transform Keypoints Research Articles

A Model for Fingerprint Liveness Detection Enabled by M-SSO Heuristic Algorithm Using Deep Learning Strategies

This paper plans to implement the benefit of deep hybrid learning for fingerprint liveness detection. The fingerprint image is subjected to background removal using Active contour. Further, the feature extraction is performed by the optimized Scale-Invariant Feature Transform (SIFT) and hybrid feature descriptor. New Modified Shark Smell Optimization (M-SSO) is used for developing optimized SIFT keypoints. The hybridization of the Local Directional Pattern (LDP) and Local Binary Pattern (LBP) is used as the feature pattern extraction. The classification phase combines Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The optimized SIFT keypoints are taken as input by RNN, and the patterns resulting from hybrid LDP and LBP are taken as input by CNN. The M-SSO optimizes the number of hidden neurons of both RNN and CNN to maximize classification accuracy. The proposed approach significantly improves the performance of FLD on different benchmark datasets over state-of-the-art models.

Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs.

In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.

Robust approach of video steganography using combined keypoints detection algorithm against geometrical and signal processing attacks

To secure the secret communication, a robust video steganography algorithm is proposed. The major objectives of the proposed method (PM) are: (1) perceiving the region of interest (ROI) keypoints’ locations in video frames for concealing the secret data and (2) determining the appropriate amount of data to be embedded into the perceived ROI keypoints’ region. In the PM, keypoints are initially extracted from each video frame using scale-invariant feature transform (SIFT) and a speeded-up robust features (SURF) descriptor against a set of predefined geometrical and signal processing attacks. Next, ROI keypoints are generated by comparing the SIFT and SURF keypoint descriptors of the original frame and attacked versions of the frame. Then ROI keypoints are divided into four least significant bit (LSB) groups to determine the embedding capacity of each ROI keypoint. Subsequently, the secret data are encrypted using a symmetric key-based shift cipher to provide an additional security layer for secret communication. Finally, the encrypted secret data have been embedded into the ROI keypoints using the LSB substitution method based on the four LSB groups’ values. The PM is tested on 22 standard benchmark videos. The efficiency of the PM is evaluated in terms of the perceptual invisibility, robustness, and concealing capacity. From experimental results, it is observed that the PM outperforms contemporary methods by attaining significant outcomes.

SIFT-Based Visual Tracking using Optical Flow and Belief Propagation Algorithm

Abstract Perceptible visual tracking acts as an important module for distinct perception tasks of autonomous robots. Better features help in easier decision-making process. The evaluation of tracking objects, dynamic positions and their visual information in results are quite difficult tasks. Until now, most real-time visual tracking algorithms suffer from poor robustness and low occurrence as they deal with complex real-world data. In this paper, we have proposed more robust and faster visual tracking framework using scale invariant feature transform (SIFT) and the optical flow in belief propagation (BF) algorithm for efficient processing in real scenarios. Here, a new feature-based optical flow along with BF algorithm is utilized to compute the affine matrix of a regional center on SIFT key points in frames. Experimental results depict that the proposed approach is more efficient and more robust in comparison with the state-of-the-art tracking algorithms with more complex scenarios.

DeepFake video production and SIFT-based analysis

Nowadays advantages in face-based modification using DeepFake algorithms made it possible to replace a face of one person with a face of another person. Thus, it is possible to make not only copy-move modifications, but to implement artificial intelligence and deep learning for replacing face movements from one person to another. Still images can be converted into video sequences. Consequently, the contemporaries, historical figures or even animated characters can be lively presented. Deepfakes are becoming more and more successful and it is difficult to detect them in some cases. In this paper we explain the video sequences we produced (e.g. using X2Face method, and First Order Motion Model for Image Animation) and perform deepfake video analysis using SIFT (Scale Invariant Feature Transform) based approach. The experiments show the simplicity in video forgery production, as well as the possible role of SIFT keypoints detection in differentiation between the deeply forged and original video content.

A fully pipelined FPGA accelerator for scale invariant feature transform keypoint descriptor matching

The scale invariant feature transform (SIFT) algorithm is considered a classical feature extraction algorithm within the field of computer vision. SIFT keypoint descriptor matching is a computationally intensive process due to the amount of data consumed. In this work, we designed a novel fully pipelined hardware accelerator architecture for SIFT keypoint descriptor matching. The accelerator core was implemented and tested on a field programmable gate array (FPGA). The proposed hardware architecture is able to properly handle the memory bandwidth necessary for a fully-pipelined implementation and hits the roofline performance model, achieving the potential maximum throughput. The fully pipelined matching architecture was designed based on the consine angle distance method. Our architecture was optimized for 16-bit fixed-point operations and implemented on hardware using a Xilinx Zynq-based FPGA development board. Our proposed architecture shows a noticeable reduction of area resources compared with its counterparts in literature, while maintaining high throughput by alleviating memory bandwidth restrictions. The results show a reduction in consumed device resources of up to 91% in LUTs and 79% of BRAMs. Our hardware implementation is 15.7 × faster than the comparable software approach.

Detection of copy-move forgery using AKAZE and SIFT keypoint extraction

Digital image manipulation techniques are becoming increasingly sophisticated and widespread. Copy-move forgery is one of the frequently used manipulation techniques. In this paper, we propose a keypoint based copy-move forgery detection (CMFD) technique, which is a combination of accelerated KAZE (AKAZE) and scale invariant feature transform (SIFT) features. By using AKZAE and SIFT, a significant number of keypoints are extracted even in a smooth region to detect the manipulated regions efficiently. After formation of the mixed keypoints, the g2NN is used for matching process to locate the duplicated regions. The experimental results show that the proposed method can detect the duplicated regions even if the image is post-processed with scaling, rotation, noise and JPEG compression operations. To validate the robustness and effectiveness of the proposed method, a statistical analysis is performed using the ANOVA method.

SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

SIFT Detector Boosted by Adaptive Contrast Threshold to Improve Matching Robustness of Remote Sensing Panchromatic Images

Although scale invariant feature transform (SIFT) based algorithms have a wide range of applications in remote sensing panchromatic image matching, but minor changes in the contrast threshold can bring about drastic changes in the image matching quality. In order to effectively improve the matching quality of the SIFT detector, this paper proposes an adaptive contrast threshold-SIFT (ACT-SIFT) procedure. The ACT-SIFT method set forth herein harmoniously calculates two sought after contrast thresholds for the target and the reference images separately an objective achieved through minimizing certain proposed criterion. As an introductory step, the entropy in the keypoints (EiK) is defined as a control or balancing parameter being estimated in terms of the entropy of the image and the number of the keypoints. To that end, the candidate keypoints are first extracted from the scale space of SIFT method by applying the initial contrast threshold for both images. Then, the contrast threshold values are modified iteratively for both images to reach the needed values by minimizing the relative difference between the EiK of the target and the reference images. Next, in agreement with the threshold values obtained, the keypoints are extracted with the help of the SIFT keypoint detector. The correct matching pairs are also created using the matches acquired through the SIFT descriptor. The results obtained by applying our proposed approach promise boosted matching pairs in remote sensing image correspondences, for it extracts the keypoints in a robust manner by simultaneously checking the information content of the reference and the target images.

Harbor Extraction Based on Edge-Preserve and Edge Categories in High Spatial Resolution Remote-Sensing Images

Efficient harbor extraction is essential due to the strategic importance of this target in economic and military construction. However, there are few studies on harbor extraction. In this article, a new harbor extraction algorithm based on edge preservation and edge categories (EC) is proposed for high spatial resolution remote-sensing images. In the preprocessing stage, we propose a local edge preservation algorithm (LEPA) to remove redundant details and reduce useless edges. After acquiring the local edge-preserve images, in order to reduce the redundant matched keypoints and improve the accuracy of the target candidate extraction method, we propose a scale-invariant feature transform (SIFT) keypoints extraction method based on edge categories (EC-SIFT): this method greatly reduces the redundancy of SIFT keypoint and improves the computational complexity of the target extraction system. Finally, the harbor extraction algorithm uses the Support Vector Machine (SVM) classifier to identify the harbor target. The experimental results show that the proposed algorithm effectively removes redundant details and improves the accuracy and efficiency of harbor target extraction.

Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The purpose of this combination is to reduce the number of SIFT keypoints by keeping only those located in the target area (salient region); this speeds up the recognition process. After that, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to constructing the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of the proposed method to predict adequately the aircraft and the ground targets.

Automatic adaptation of SIFT for robust facial recognition in uncontrolled lighting conditions

The scale invariant feature transform (SIFT), which was proposed by David Lowe, is a powerful method that extracts and describes local features called keypoints from images. These keypoints are invariant to scale, translation, and rotation, and partially invariant to image illumination variation. Despite their robustness against these variations, strong lighting variation is a difficult challenge for SIFT‐based facial recognition systems, where significant degradation of performance has been reported. To develop a robust system under these conditions, variation in lighting must be first eliminated. Additionally, SIFT parameter default values that remove unstable keypoints and inadequately matched keypoints are not well‐suited to images with illumination variation. SIFT keypoints can also be incorrectly matched when using the original SIFT matching method. To overcome this issue, the authors propose propose a method for removing the illumination variation in images and correctly setting SIFT's main parameter values (contrast threshold, curvature threshold, and match threshold) to enhance SIFT feature extraction and matching. The proposed method is based on an estimation of comparative image lighting quality, which is evaluated through an automatic estimation of gamma correction value. Through facial recognition experiments, the authors find significant results that clearly illustrate the importance of the proposed robust recognition system.

Registrating oblique images by integrating affine and scale-invariant features

ABSTRACTAutomatic registration of oblique images can be challenging due to the complexly geometric deformations of the images. A feature-based registration method for oblique images is proposed in this study. The proposed method integrates affine and scale-invariant features and mainly includes three steps: initial matching, propagative matching, and final registration. In the initial matching, the maximally stable extremal region (MSER) keypoints are detected and matched based on Scale-Invariant Feature Transform (SIFT) descriptors. The SIFT keypoints in the supporting region (SR) of MSER are matched using affine invariant normalized cross-correlation matching algorithm. Neighbourhood supporting strength – the ratio of SIFT matches to SIFT keypoints in SR, is proposed to eliminate error matches. In propagative matching, the matches in the former step are used for coarse geometric transformation estimation. The keypoints that have not been successfully matched in the initial matching are handled by iterative correspondence identification under geometric constraint. The two matching processes obtain evenly distributed and sufficient number of correspondences to calculate the accurate transformation between inputted images. Final registration is achieved using bilinear interpolation on sensed image. Experimental results carried on close-range, satellite, and aerial images demonstrate that the proposed method can achieve reliable correspondences and performs better than state-of-the-art methods for oblique image registration.

Illumination invariant feature point matching for high-resolution planetary remote sensing images

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°–180°, reveal that the proposed method retrieves about 40%–60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

SIFT Keypoint Removal via Directed Graph Construction for Color Images

As one of the most successful feature extraction algorithms, scale invariant feature transform (SIFT) has been widely employed in many applications. Recently, the security of SIFT against malicious attack has been attracting increasing attention, and several techniques have been devised to remove SIFT keypoints intentionally. However, most of the existing methods still suffer from the following three problems: 1) the keypoint removal rate achieved by many techniques is unsatisfactory when removing keypoints within multiple octaves; 2) noticeable artifacts are introduced in the processed image, especially in those highly textured regions; and 3) the color information is totally neglected, precluding the widespread adoption of those methods. To tackle these challenges, in this paper, we propose a novel SIFT keypoint removal framework. By modeling the difference of Gaussian space as a directed weighted graph, we derive a set of strict inequality constraints to remove a SIFT keypoint along a pre-constructed acyclic path. To minimize the incurred distortion, the path is strategically designed over the directed graph. Furthermore, we propose a simple yet effective optimization framework for recovering the color information of the keypoint-removed image. Extensive experiments are provided to show the superior performance of our proposed scheme over the state-of-the-art techniques, in both the scenarios of removing keypoints in a single octave and in multiple octaves.

MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these problems, in this paper, we present a horizontal or vertical mirror reflection invariant binary descriptor named MBR-SIFT, in addition to a novel image matching approach. First, 16 cells in the local region around the SIFT keypoint are reorganized, and then the 128-dimensional vector of the SIFT descriptor is transformed into a reconstructed vector according to eight directions. Finally, the MBR-SIFT descriptor is obtained after binarization and reverse coding. To improve the matching speed and accuracy, a fast matching algorithm that includes a coarse-to-fine two-step matching strategy in addition to two similarity measures for the MBR-SIFT descriptor are proposed. Experimental results on the UKBench dataset show that the proposed method not only solves the problem of mirror reflection, but also ensures desirable matching accuracy and speed.

Copy‐Paste Detection Based on a SIFT Marked Graph Feature Vector

To detect copy-paste tampering, an improved SIFT (Scale invariant feature transform)-based algorithm was proposed. Maximum angle is defined and a maximum angle-based marked graph is constructed. The marked graph feature vector is provided to each SIFT key point via discrete polar coordinate transformation. Key points are matched to detect the copy-paste tampering regions. The experimental results show that the proposed algorithm can effectively identify and detect the rotated or scaled copy-paste regions, and in comparison with the methods reported previously, it is resistant to post-processing, such as blurring, Gaussian white noise and JPEG recompression. The proposed algorithm performs better than the existing algorithm to dealing with scaling transformation.

SIFT Keypoint Removal and Injection via Convex Relaxation

Scale invariant feature transform (SIFT), as one of the most popular local feature extraction algorithms, has been widely employed in many computer vision and multimedia security applications. Although SIFT has been extensively investigated from various perspectives, its security against malicious attacks has rarely been discussed. In this paper, we show that the SIFT keypoints can be effectively removed with minimized distortion on the processed image. The SIFT keypoint removal is formulated as a constrained optimization problem, where the constraints are carefully designed to suppress the existence of local extrema and prevent generating new keypoints within a local cuboid in the scale space. To hide the traces of performing SIFT keypoint removal, we then propose to inject a large number of fake SIFT keypoints into the previously cleaned image with minimized distortion. As demonstrated experimentally, our proposed SIFT removal and injection algorithms significantly outperform the state-of-the-art techniques. Furthermore, it is shown that the combined SIFT keypoint removal and injection attack strategy is capable of defeating the most powerful forensic detector designed for SIFT keypoint removal. Our results suggest that an authorization mechanism is required for SIFT-based systems to verify the validity of the input data, so as to achieve high reliability.

Unsupervised SAR Image Change Detection Based on SIFT Keypoints and Region Information

This letter presents a new unsupervised distribution-free change detection method for synthetic aperture radar (SAR) images based on scale-invariant feature transform (SIFT) keypoints and region information. Since the SIFT can detect bloblike structures in an image and be insensitive to noise, we first extract noise-robust SIFT keypoints in the log-ratio image to reduce the detection range. Then, in order to obtain accurate changed regions, rather than directly obtaining the change-detection map from the difference image as in some traditional change detection methods, we make segmentation around the extracted keypoints in the two original multitemporal SAR images, where the edges of detection regions are much clearer than those in the difference image, and further compare the two segmentations to generate the change-detection map. This method utilizes the bloblike structure information offered by SIFT keypoints and the region information extracted via image segmentation. Experiments on real SAR images demonstrate the effectiveness of the proposed method.

Iris Recognition by SIFT Feature and Level Set

This paper proposes a new approach to solve iris recognition problem. In the segmentation step, the inner boundary of iris was extracted by applying Euclidean distance transform and Level Set method. Then the eyelid was removed by applying Hough transform. SIFT (Scale Invariant Feature Transform) was utilized to extract the iris features for use as the identified characteristics. Since SIFT keypoints are scale-invariant, the iris normalization process is not required. The recognition performances of the proposed system are 96.67% and 94.37% for HCMUS–IRITECH dataset and CASIA 3.0 Iris–Interval dataset, respectively. Also, our iris segmenting method shows better performance than the Integro-differential method and Hough transform method for CASIA dataset, but the proposed method is a little worse than those approaches for HCMUS-IRITECH dataset. The most important merit of the proposed method is that the calculation amount of the method is small compared with previous approaches with reasonal recognition accuracy.