Scale Invariant Feature Transform Feature Extraction Research Articles

An Efficient Search on Cloud Images with Blockchain Technology

A key task in computer vision is image retrieval, which has wide-ranging applications across multiple domains. Using query picture features, this abstract proposes an image retrieval approach that focuses on the widely used Scale-Invariant Feature Transform (SIFT) algorithm for feature extraction and distance calculation. The suggested method starts by extracting SIFT features from a set of photos, building a keypoints and descriptor database. By capturing the unique qualities of nearby image regions, these features enable reliable matching and retrieval. The security of private cloud data, which includes query queries, the search tree, and outsourced photographs, is another major issue. Use a feature extraction method first for integrated picture features, which are composed of fundamental components like colour and shape. In particular, because the proposed method uses a balancing index tree, it can achieve logarithmic search time. Second, the picture and query feature are encrypted using the secure inner product. Include a system for determining duplicate picture material as well. When given a query image, SIFT feature extraction is applied to it, producing keypoints and descriptors that correspond to its visual characteristics. Next, a distance computation method like Euclidean distance is used to compare the features of the database photos with the query image features. The similarity between the query image and the database images is measured through this comparison. The retrieved photos are ranked in order of similarity to the query image based on the calculated distances. Search results with lesser distances between images are deemed more similar and are displayed first. To locate visually related images in huge databases, the image retrieval system that uses SIFT feature extraction and distance calculation provides a reliable and effective solution. It contributes to developments in multimedia retrieval, visual analytics, and picture understanding by enabling applications like content-based image search, image recommendation systems, and image clustering.

Plane Fitting in 3D Reconstruction to Preserve Smooth Homogeneous Surfaces

Photogrammetric techniques for weakly-textured surfaces without sufficient information about the R (red), G (green) and B (blue) primary colors of light are challenging. Considering that most urban or indoor object surfaces follow simple geometric shapes, a novel method for reconstructing smooth homogeneous planar surfaces based on MVS (Multi-View Stereo) is proposed. The idea behind it is to extract enough features for the image description, and to refine the dense points generated by the depth values of pixels with plane fitting, to favor the alignment of the surface to the detected planes. The SIFT (Scale Invariant Feature Transform) and AKAZE (Accelerated-KAZE) feature extraction algorithms are combined to ensure robustness and help retrieve connections in small samples. The smoothness of the enclosed watertight Poisson surface can be enhanced by enforcing the 3D points to be projected onto the absolute planes detected by a RANSAC (Random Sample Consensus)-based approach. Experimental evaluations of both cloud-to-mesh comparisons in the per-vertex distances with the ground truth models and visual comparisons with a popular mesh filtering based post-processing method indicate that the proposed method can considerably retain the integrity and smoothness of the reconstruction results. Combined with other primitive fittings, the reconstruction extent of homogeneous surfaces can be further extended, serving as primitive models for 3D building reconstruction, and providing guidance for future works in photogrammetry and 3D surface reconstruction.

Face Recognition and Gender Detection Using SIFT Feature Extraction, LBPH, and SVM

Face recognition and name and gender identification are challenging processes, especially when identifying perpetrators and suspects or when used in authentication systems. Machine learning and computer vision technologies are used in many fields, including security, and play an important role in face recognition and gender detection, offering valuable information to officials to rectify a situation in less time. This study used a few machine learning methods in the Labelled Faces in the Wild (LFW) database to examine their facial recognition and gender detection capacities. The LFW dataset was used to train and evaluate the Scale Invariant Feature Transform (SIFT) feature extraction method along with the Support Vector Machine (SVM) classifier and the Local Binary Pattern Histogram (LBPH) method. The result comparison from the current and other studies showed that the proposed LBPH method had higher accuracy in face recognition, while its accuracy in gender detection was very close to the ones of other, relevant studies.

Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.

Tourist Behavior Recognition Through Scenic Spot Image Retrieval Based on Image Processing

The monitoring of tourist behaviors, coupled with the recognition of scenic spots, greatly improves the quality and safety of travel. The visual information is the underlying features of scenic spot images, but the semantics of the information have not been satisfactorily classified or described. Based on image processing technologies, this paper presents a novel method for scenic spot retrieval and tourist behavior recognition. Firstly, the framework of scenic spot image retrieval was constructed, followed by a detailed introduction to the extraction of scale invariant feature transform (SIFT) features. The SIFT feature extraction includes five steps: scale space construction, local space extreme point detection, precise positioning of key points, determination of key point size and direction, and generation of SIFT descriptor. Next, multiple correlated images were mined for the target scenic spot image, and the feature matching method between the target image and the set of scenic spot images was introduced in details. On this basis, a tourist behavior recognition method was designed based on temporal and spatial consistency. The proposed method was proved effective through experiments. The research results provide theoretical reference for image retrieval and behavior recognition in many other fields.

Image Stitching for Chest Digital Radiography Using the SIFT and SURF Feature Extraction by RANSAC Algorithm

Image stitching is one of the branches of computer vision. It combines two or more images for a scene to acquire a high-resolution panoramic image. An invariant local function often uses to stitch two images together. Since the flat plate of a digital radiography (DR) system does not cover all parts of the body, the whole bone structure image cannot seize in a single scan. To solved this problem, image stitching is broadly utilized by medical systems to stitch DR images, which can be helpful for scoliosis or lower extremity deformities in the diagnosis, and pre-operative planning are of great importance. In this paper, the stitching and retrieval of medical images planned. To conquer the background noise in medical images, and improve the recovery of quality and stitching rapidity of medical images, a random sample consensus (RANSAC) algorithm is useful to stitching the images of Chest digital radiography by scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) feature extraction. Down-sampling utilizes to lessen the size of the images and reduction the measure of calculation. In the interim, the phase correlation is engaged to discover the overlapping region. After feature matching and perspective transformation, the stitched image is gotten dependent on the homography. At last, experimentation has finished showing the presentation.

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.

An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

Recovering depth information of objects from two-dimensional images is one of the very important and basic problems in the field of computer vision. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object’s depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the most appropriate pair of straight line segments are chosen, and also reduce computation, convex hull theory and knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical.

Guest Editorial: Design and Implementation of DSP Systems

In recent years, there has been an increasing need to develop new implementation techniques and design methodologies for DSP systems. Algorithmic and architectural optimizations are key to developing high-performance signal and information processing systems under strict constraints on implementation complexity and power consumption. This special issue is composed of a selection of papers reporting on advances in the design and implementation of signal processing systems. The topics range from domainspecific hardware implementation to design methodologies for signal processing algorithm implementations. In BAn energy-efficient Reconfigurable ASIP supporting multi-mode MIMO detection,^ (10.1007/s11265-015-0972-x) Ahmad, Li, Amin, Li, Van der Perre, Lauwereins, and Pollin present a programmable ASIP MIMO baseband processor. They first present an efficient modification of the Multi-Tree Selective Spanning Detector algorithm. Then they introduce a soft-output algorithm for generating log-likelihood ratios, called counter-ML bit-flipping. A C-programmable ASIP is designed for 40 nm CMOS, operating at 3.6 Gbps for hard MIMO detection, and 2.05 Gbps for soft detection. Tripakis, Limaye, Ravindran, Wang, Andrade, and Ghosal consider models of dataflow computation in their paper BTokens vs. Signals: On Conformance between Formal Models of Dataflow and Hardware^ (10.1007/s11265-0150971-y). They define a formal conformance relation between finite state machines with synchronous semantics and a formal model for dataflow: asynchronous processes communicating via queues. The conformance can provide information in determining the accuracy of hardware models, can be used to highlight timing and synchronization errors, and derive performance properties. In BA dynamic modulo scheduling with binary translation: Loop optimization with software compatibility,^ (10.1007 /s11265-015-0974-8) Ferreira, Denver, Pereira, Wong, Lisboa, and Carro propose a binary translation technique for run-time modulo scheduling of loops onto course-grained reconfigurable arrays. The technique eliminates the need to generate an intermediate dataflow graph (DFG) and uses a greedy placement step. The experimental results show that the run-time technique can achieve higher instruction-level parallelism compared to a 16-issue VLIW processor. Akin, Franchetti, and Hoe present restructured Fast Fourier Transform (FFT) algorithms with efficient memory access patterns in their paper BFFTs with Near-Optimal Memory Access Through Block Data Layouts: Algorithm, Architecture and Design Automation^ (10.1007/s11265-015-1018-0). They use a formal representation of the FFT using the Kronecker product to automatically generate hardware implementations of DRAM-optimized FFT algorithms. Results for 1D, 2D, and 3D FFTs show that their designs can achieve close to the theoretical peak performance on several different platforms. In BAnalyzing the Performance-Hardware Trade-off of an ASIP-based SIFT Feature Extraction,^ (10.1007/s11265-0150986-4) Mentzer, Paya-Vaya, and Blume consider the implementation of the Scale-Invariant Feature Transform (SIFT) used in computer vision. This complexity of the SIFT algorithm is too high for real-time implementation on * Warren J. Gross warren.gross@mcgill.ca

BEMD–SIFT feature extraction algorithm for image processing application

Scale-invariant feature transform (SIFT) algorithm has been successfully applied to object recognition and to image feature extraction, which is a major application in the field of image processing. Nonetheless, the SIFT algorithm has not been solved effectively in practical applications that requires real-time performance, much calculation, and high storage capacity given the framework level and the iterative calculation process in the SIFT Gaussian blur operation. The extraction of image feature information is accelerated using the speeded-up robust features algorithm. However, this algorithm remains sensitive to complicated deformation. To address these problems, in this paper, we proposes a novel algorithmic framework based on bidimensional empirical mode decomposition (BEMD) and SIFT to extract self-adaptive features from images. First, the BEMD algorithm is used to decompose the self-adaptive features of the original image and to obtain multiple BIMF components. Second, the SIFT algorithm optimizes the extraction of parameters that reflect characteristic information on BIMF components. Related parameters are obtained through genetic algorithm optimization. Third, the method for extracting the characteristic information of the BIMF components involves synthesizing all of the accumulated characteristic information in the original image. Comparison results show that the method of calculating image feature extraction speed, accuracy, and reliability has a stronger effect than other methods.

The Mobile Visual Search Guiding System Based on SIFT

In order to provide personal guiding service to visitors in the Picture Gallery, a mobile visual search guiding system based on SIFT(Scale Invariant Feature Transform) is proposed in this paper. The visitors can achieve the paintings’ information using the camera function of the mobile phones when they enjoy the famous paintings in the Picture Gallery. The mobile search visual system proposed in this paper can identify the paintings in the server side so there are no extra performance requirements for the mobile phones. Firstly, the mobile visual search system was introduced. Then the system was designed and realized. The classification training was based on SVM (Support Vector Machine). The SIFT feature extraction algorithm was used to enhance the recognition rate. After the image retrieval, the geometric consistency check was employed to choose the best matching image. Finally, the experimental results verified the feasibility and practicability of this system. Compared with the present guiding systems, this system can communicate with the visitors conveniently and offer abundant and all-around information to the visitors. In addition, using this guiding system, any auxiliary devices are not needed to be installed in the Picture Gallery.

Performance Analysis of SIFT Feature Extraction Algorithm in Application to Registration of SAR Image

Exactly extracting the stable feature of high resolution SAR image as well as matching it are two critical steps for the Antomatic regiestation systems. It is suggested that the Scale Invariant Feature Transform (SIFT) algorithm can be applied in the optical image registration systems and four representative experiments were performed to test its validity. It is found that SIFT can accurately register the high resolution SAR images than the traditional Harris in applicability and precision.

Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

Abstract Batik is a traditional fabric of Indonesian cultural heritage. Automatic batik image classification is required to preserve the wealth of traditional art of Indonesia. In such classification, a method to extract unique characteristics of batik image is important. Combination of Bag of Features (BOF) extracted using Scale-Invariant Feature Transform (SIFT) and Support Vector Machine (SVM) classifier which had been successfully implemented in various classification tasks such as hand gesture, natural images, vehicle images, is applied to batik image classification in this study. The experimental results show that average accuracy of this method reaches 97.67%, 95.47% and 79% in normal image, rotated image and scaled image, respectively.

A comparative study of dimensionality reduction methods for large-scale image retrieval

“Curse of Dimensionality” is one of the important problems that Content-Based Image Retrieval (CBIR) confronts. Dimensionality reduction is an effective method to overcome it. In this paper, six commonly-used dimensionality reduction methods are compared and analyzed to examine their respective performance in image retrieval. The six methods include Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA), Local Fisher Discriminant Analysis (LFDA), Isometric Mapping (ISOMAP), Locally Linear Embedding (LLE), and Locality Preserving Projections (LPP). For comparison, Scale Invariant Feature Transform (SIFT) and color histogram in Hue, Saturation, Value (HSV) color space are firstly extracted as image features, meanwhile SIFT feature extraction procedure is optimized to reduce the number of SIFT features. Then, PCA, FLDA, LFDA, ISOMAP, LLE, and LPP are respectively applied to reduce the dimensions of feature vectors, which can be used to generate vocabulary trees. Finally, we can process large-scale image retrieval based on the inverted index built by vocabulary trees. In the experiments, the performance of various dimensionality reduction methods are analyzed comprehensively by comparing the retrieval performance, advantages and disadvantages, computational complexity and time-consuming of image retrieval. Through a series of experiments, we can conclude that dimensionality reduction method of LLE and LPP can effectively reduce computational complexity of image retrieval, while maintaining high retrieval performance.

AN IMPROVED ALGORITHM USED IN AUTOMATIC MATCHING FOR LOW-ALTITUDE AERIAL IMAGE

Abstract. In this paper, we present an improved algorithm used for low altitude aerial image automatic matching based on SIFT operator. Compared to traditional photogrammetry based on platforms such as satellites and aerospace aircrafts, the platforms of low-altitude remote sensing system have relatively lighter weight, therefore existing rotation angle and scale differences in the stereo-images. In addition, there appears fracture lines and the discontinuities of parallax in the elevation undulating area. The characteristics above make it unsuitable for the traditional photogrammetry matching method based on grey scale correlation and the matching search strategy based on continuous parallax. In this paper an improved SIFT(Scale-invariant feature transform) operator is applied to the automatic matching of low-altitude aerial images. Several improvements were made to enhance the feature recurrence rate, matching correct rate and speed of matching. Firstly, we applied the theory of zero-crossing in SIFT feature extraction introducing the image geometry feature in scale space detection. Secondly, correlation coefficient is used as similarity measure instead of Euclidean distance in SIFT algorithm. Thirdly, we proposed a new matching strategy based on principal orientation constrain to shorten the search distance compared to the global search in SIFT algorithm. To demonstrate the feasibility of the approach, experiments were carried with four groups low-altitude remote sensing stereo-images from different sensors, and presented different distortions. Results showed that the improved algorithm has higher feature recurrence rate, matching correct rate and speed of matching dealing with different scale, large rotation angle, affine distortion and nonlinear distortion of low-altitude remote sensing stereo-images.

Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT

Privacy has received considerable attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario where the server is resource-abundant, and is capable of finishing the designated tasks. It is envisioned that secure media applications with privacy preservation will be treated seriously. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to target the importance of privacy-preserving SIFT (PPSIFT) and to address the problem of secure SIFT feature extraction and representation in the encrypted domain. As all of the operations in SIFT must be moved to the encrypted domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic encryption. We show through the security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure against ciphertext only attack and known plaintext attack. Experimental results obtained from different case studies demonstrate that the proposed homomorphic encryption-based privacy-preserving SIFT performs comparably to the original SIFT and that our method is useful in SIFT-based privacy-preserving applications.

The Application of SIFT Algorithm in Blind Road Environmental Image Matching

Image Matching is a key technology in the intelligent navigation system for the blind, which is based on the computer video. The images of moving blind people, collected at real time, have variety of changes in light, rotation, scaling, etc. Against this feature, we propose a practical matching algorithm, which is based on the SIFT (Scale Invariant, Feature Transform). That is the image matching algorithm. We focus on the algorithm of the SIFT feature extraction and matching, and obtain the feature points of the image through feature extraction algorithm. We verify the effect of the algorithm by selecting practical images with rotation, scaling and different light. The result is that this method can get better matches for the blind road environmental image.

Automatic registration of remote sensing images based on SIFT and fuzzy block matching for change detection

This paper presents an automated image registration approach to detecting changes in multi-temporal remote sensing images. The proposed algorithm is based on the scale invariant feature transform (SIFT) and has two phases. The first phase focuses on SIFT feature extraction and on estimation of image transformation. In the second phase, Structured Local Binary Haar Pattern (SLBHP) combined with a fuzzy similarity measure is then used to build a new and effective block similarity measure for change detection. Experimental results obtained on multi-temporal data sets show that compared with three mainstream block matching algorithms, the proposed algorithm is more effective in dealing with scale, rotation and illumination changes.

Image registration method for multimodal images

A new image registration method for multimodal images is proposed in this paper. This method is a combination of the modified scale invariant feature transform (SIFT) feature extraction algorithm and the shape-context feature descriptor. Salient points of multimodal images are extracted by using the modified SIFT feature extraction algorithm. Then each salient point is described by using the shape-context descriptor that formed a feature vector from the orientation histograms of the subregion around each salient point. After salient points matching by using Euclidean distance, random sample consensus algorithm is used to eliminate wrong corresponding pairs. At last, multimodal images registration is achieved by affine transformation and bilinear interpolation. Experimental results for registration of IR images and electro-optical images show that this method has a good registration result.

Automatic registration of remote sensing images based on SIFT and fuzzy block matching for change detection

This paper presents an automated image registration approach to detecting changes in multi-temporal remote sensing images. The proposed algorithm is based on the scale invariant feature transform (SIFT) and has two phases. The first phase focuses on SIFT feature extraction and on estimation of image transformation. In the second phase, Structured Local Binary Haar Pattern (SLBHP) combined with a fuzzy similarity measure is then used to build a new and effective block similarity measure for change detection. Experimental results obtained on multi-temporal data sets show that compared with three mainstream block matching algorithms, the proposed algorithm is more effective in dealing with scale, rotation and illumination changes.