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In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available. [ABSTRACT FROM AUTHOR]

In this paper, the issues on current optimization methods for identifying modern digital camera photosensors are presented. Methods for improving the reliability of identification of digital cameras are discussed, i.e., determining the digital camera used to take a particular photograph. Homogeneous images were optically recorded to form a noise signature, and sets of amateur images were tested for identification of three types of cameras from the images. Digital image filtering and image identity metric were optimized. An optimal digital filter was selected to evaluate smoothed images to obtain the noise signatures of the identified cameras. An optimal identity criterion was obtained by comparison of camera noise signatures. Using an optimal filter and identity criterion allowed increasing, on average, the reliability of identifying the camera used to take a particular photograph by > 60 times. This work provides reference to solve image problems in image registration and processing, security, forensics, and big data analysis. [ABSTRACT FROM AUTHOR]

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains. [ABSTRACT FROM AUTHOR]

The paper proposes a new initial disparity estimation algorithm based on sparse local feature matching and one-dimensional dynamic programming. The new algorithm solves the problems of long calculation time, high memory consumption and unknown search range of disparity in the classical pyramid SGM dense matching algorithm. Based on the initial disparity, the final accurate disparity can be got by using SGM algorithm within fixed little search range. The new algorithm is more accurate than the traditional SGM algorithm (by testing on a public data set, the accuracy of new algorithm is 1.57% higher than traditional SGM algorithm), it does not need to artificially estimate the search range of disparity, and it is suitable for implementation on FPGA. Based on the new algorithm, the CPU-FPGA collaborative high-speed dense matching for high-resolution aerial images is realized. The average speed of dense matching on CPU-FPGA is 31.81times faster than the original SGM algorithm and 6.46 times faster than pyramid SGM algorithm. The speed of accurate matching by FPGA is 4.75 times faster than running on GPU. [ABSTRACT FROM AUTHOR]

With the gradual advancement of image sensor technology and the gradual complexity of the application environment, single-source imaging sensors have been difficult to meet practical application requirements. Therefore, the registration and fusion techniques of infrared images and visible images are hot topics in recent years. This article aims to study the fusion of two images. Based on the traditional image fusion algorithm, the color space IHS transform and the lifting wavelet transform are combined according to the characteristics of infrared image and visible light image captured by the UAV imaging device. The experimental results show that the algorithm can not only retain the brightness information of the infrared target. Among them, some details of the visible light image are retained. In this paper, the four objective criteria of information entropy, standard deviation, image mean and average gradient are used to evaluate the fusion effect of the proposed algorithm. The obtained values are 7.285, 29.487, 122.2739, and 4.5678, respectively, which are higher than other algorithms. It can be seen that the algorithm proposed in this paper has important practical significance. [ABSTRACT FROM AUTHOR]

In this paper, a new method for image mosaicing (image stitching) is introduced based on Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the redundancy of the extracted keypoints, which leads to lower image mosaicing quality. Recently, a new method called Redundant Keypoint Elimination (RKEM) was presented to remove these redundant features, and enhance image registration performance. Despite the applicability of RKEM, its threshold value is considered the same in all parts of the image. This characteristic leads to inappropriate removal of keypoints due to the fact that distribution of keypoints in the high-detailed region is denser than the low-detailed ones. This paper proposes a new method to improve RKEM called Clustered RKEM (CRKEM) which is based on keypoints distribution. Moreover, in this paper a new blending algorithm is proposed based on a Gaussian-weighted function. In the proposed blending method, the Gaussian function is proposed based on the mean and variance of the pixels in the overlapped region of images to be mosiaced. In comparison with the classical methods, the experimental results confirm the superiority of the proposed method in image mosaicing as well as to image registration and matching. [ABSTRACT FROM AUTHOR]

In this paper, we propose an original algorithm allowing the registration of multimodal images. Indeed, the large and nonlinear intensity changes between different modalities often lead to mismatching of the interest points, which can alter the quality of the registration. Our method builds on the SURF for the identification and the description of the interest points. In order to boost the confidence on the subsequent matching operation of the registration, an applicable classification on both the distance and orientation SURF features is presented despite the low structural similarity of the multimodal acquisition. The proposed algorithm is tested and validated on a large dataset extracted from acquisitions on cultural heritage wall paintings that implement 4 imaging modalities covering UV, IR, visible and fluorescence. The comparison with conventional SIFT/SURF approaches, PIIFD, SURF-PIIFD-PRM,CFOG and HTCPR for registration highlights the effectiveness of the method and its robustness to outliers in SIFT/SURF descriptors. [ABSTRACT FROM AUTHOR]

Despite all distortions and incompleteness present in Sketch maps, they can be used for different purposes such as collecting and searching spatial data and organizing the spatial knowledge of individuals. Since the prerequisite for all of these cases is matching sketches with existing data in spatial databases or metric maps, several studies have been conducted in this field. These studies generally focus on the relationships between routes, landmarks, and regions, while data sources do not necessarily contain all of these data. Also, the subject of the incompleteness of data and evaluation of the results has been less considered in previous studies. Therefore, in this paper, a new solution for matching routes from sketches to metric maps is presented. The proposed solution uses the Genetic Algorithm to measure the similarities between routes in two phases, and in each phase, a set of geometric/topological criteria is considered to improve the matching. The proposed algorithm is implemented to match routes of 25 sketches with the routes in a metric map. The average F Measure of matching is 45.59%. The results show that by considering the specific conditions (not availability of descriptive data of routes and geometric/descriptive information of landmarks), this paper has been able to take an important step toward finding an acceptable solution for the matching problem. The proposed methods can be used to deal with the incompleteness of the thematic information in metric maps. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing images have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this paper, the target recognition algorithm of multi-source remote sensing image based on IoT vision is investigated. The infrared sensor and SAR radars are set in the visual perception layer of the iVIOT. The visual perception layer transmits the collected remote sensing image information to the application layer through the wireless networks. The data processing module in the application layer uses the normalized central moment idea to extract the features of multi-source remote sensing image. Contourlet two-level decomposition is performed on the image after feature extraction to realize multi-scale and multi-directional feature fusion. A two-step method of primary fineness is used to match the fused features and the random sampling consensus algorithm is used to eliminate false matches for obtaining the correct match pairs. After the image feature matching is completed, the BVM target detection operator is used to complete the target recognition of multi-source remote sensing image. Experimental results show that the use of the IoT to visually recognizing the desired remote sensing image target has low communication overhead, and the recognition reaches 99% accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the requirement of obtaining the panorama of the worktable in the automatic cutting system of workpiece groove based on machine vision, an image registration algorithm based on SIFT and improved PROSAC is proposed. First, SIFT algorithm is used for feature detection and feature description. Then, the bidirectional matching and cosine similarity method are used for rough matching of feature points. Finally, an improved PROSAC algorithm is proposed, which purifies the matching points and calculates the image transformation matrix. In image fusion, the weighted average method is used to fuse the overlapping parts of the image to obtain a whole image of the cutting platform. Experimental results show that the algorithm in this paper has been improved in terms of matching accuracy and time-consuming compared with several classical algorithms. [ABSTRACT FROM AUTHOR]

Under the background of telemedicine, a new registration and mosaic algorithm for medical images is proposed in this paper to solve the problems of electronic noise, uneven illumination and ray scattering in the real-time medical process. The improved Retinex algorithm by trilateral filter and homomorphic filtering algorithm can enhance the image effectively in preprocessing. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the SUSAN corner matching points were extracted in overlapping positions, the improved KD tree was used for enhancing matching efficiency. Later, matching points were purified by the improved RANSAC algorithm. Finally, images were processed by Laplacian pyramid decomposition algorithm to make the image joint seemed smooth and natural. The results of experiments and evaluation criteria confirm that the new method has high robustness in the process of medical image registration and stitching in the network. [ABSTRACT FROM AUTHOR]

Scale invariant feature transform (SIFT) is one of the most effective techniques in image matching applications. However, it has a main drawback: existing numerous redundant keypoints located very close to each other in the image. These redundant keypoints increase the computational complexity while they decrease the image matching performance. Redundant keypoint elimination method (RKEM)–SIFT are incorporated to eliminate these points by comparing their distances with a fixed experimental threshold value. However, this value has a great impact on the matching results. In this paper, an adaptive RKEM is presented which considers type of the images and distortion thereof, while adjusting the threshold value. Moreover, this value is found separately for the reference and sensed images. In an image, the adaptive RKEM finds the histogram of the keypoints distances, for which the number and the width of the bins are determined based on the number of keypoints and the distances distribution metrics. Then, a maximum value for searching the optimal threshold value is determined. Finally, for each integer value smaller than the mentioned maximum, a set containing distances smaller than that value is created and the one with the smallest variance is selected. The integer value corresponding to that set is chosen as the adaptive threshold for that image. This approach can improve the efficiency of the RKEM-SIFT in eliminating redundant keypoints. Simulation results validated that the proposed method outperforms the SIFT, A2 SIFT and RKEM-SIFT in terms of the matching performance indices. [ABSTRACT FROM AUTHOR]

Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database. [ABSTRACT FROM AUTHOR]

Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. In the particular case of hyperspectral images, the exploitation not only of the spatial information contained in the image but also of the spectral information helps to improve the registration. An example of registration method exploiting all the information contained in the images is HSI–KAZE, which is based on feature detection and detects keypoints using nonlinear diffusion filtering. The algorithm is oriented toward extreme situations in which the images are very different in terms of scale, rotation and displacement. In this paper, an efficient implementation of the HSI–KAZE algorithm on GPU using CUDA is proposed. A detailed analysis of the implementation as well as a performance comparison to an OpenMP multicore implementation is also presented. The resulting algorithm is suitable for on-board processing of high-resolution images. [ABSTRACT FROM AUTHOR]

Three-dimensional object reconstruction from multi-view images is an important topic in computer vision, which has attracted enormous attention during the past decades. With the further study in deep learning, remarkable progress of three-dimensional object reconstruct has been obtained in recent years. In this paper, we proposed three-dimensional rapid registration and reconstruction of multi-view rigid objects based on end-to-end deep surface model in the field of three-dimensional object reconstruction. Firstly, we introduce a matching algorithm called local stereo matching algorithm based on improved census transform and multi-scale spatial, aiming to improve the matching results for those regions. In cost aggregation step, guided map filtering algorithm with excellent gradient preserving property is introduced into Gaussian pyramid structure and regularization is added to strengthen cost volume consistency. Secondly, the improved inception RESNET module is added to improve the feature extraction ability of the network, and multiple features are extracted by using multiple network structures, and finally multiple features are sequentially input into the VRNN module to enhance the reconstruction effect of multi-view images. The experimental results show that our proposed method can not only achieve better reconstruction results, but also reconstruct more details and spend less time in training. [ABSTRACT FROM AUTHOR]

We propose a fast elemental image generation algorithm for integral imaging display based on depth information and a two-dimensional scene image. Existing elemental image generation algorithms establish many-to-one mappings from the scene to the elemental image, thereby including redundant calculations. The improved algorithm entails establishing a single mapping from elemental image to the scene. Each pixel in the elemental image matches a unique scene pixel by inverse iteration along the optical path. This establishes a high precision matching and eliminates hole caused by depth discontinuities. In this paper, we obtain a unique mapping solution by inverse iteration along the optical path using a one-pixel step. The time complexity of this algorithm is mainly driven by the pixel sum of the elemental image array. We generate the elemental image seven times faster than the original algorithm. The larger total quantity of the scene pixels further demonstrates the advantages of our algorithm. The experiments show that the update rate can reach more than 30 fps according to the VCD standard (352×240 resolution) using four parallel calculation threads with a four-core Intel® Core(TM) i7-7500U 2.70 GHz CPU despite various delay factors. The results verify the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

The dimensional and geometric inspection of non-rigid mechanical parts still presents a real challenge to the automotive and aerospace industry. Much research has been devoted to automating the inspection of these parts in order to eliminate the cost of specialized fixtures that cause productivity problems for manufacturers. A registration step between the part'31s nominal CAD model and its corresponding scan is required for an automatic inspection. The coherent point drift (CPD) is one of the existing algorithms for registration and is widely used in imaging applications. It has been recently adapted with regard to the mechanical field for the fixtureless dimensional inspection problems of deformable bodies, notably the IDB-ACPD algorithm. In this paper, an improvement of the optimization phase by reformulating the objective function of the algorithm is put forward. A correction matrix associated with the importance of each measurement point, and therefore with the local rigidity of the part during the registration operation, was introduced and incorporated. In addition, the measurement points representing the same rigidity have been clustered based on mechanical metrics using a fuzzy c-mean algorithm. The effectiveness of the proposed approach is demonstrated on different case studies from the transport industry and the results provide an improved registration of non-rigid parts leading to better dimensional and geometric errors detection. [ABSTRACT FROM AUTHOR]

The traditional iterative closest point (ICP) algorithm could register two point sets well, but it is easily affected by local dissimilarity. To deal with this problem, this paper proposes an isotropic scaling ICP algorithm with corner point constraint. First of all, because the corner points can preserve the similarity of the whole shapes, an objective function based on least square error is proposed under the guidance of the corner points. Second, a new ICP algorithm is proposed to complete the isotropic scaling registration. At each iterative step of this new algorithm, the correspondence is built based on the closest point searching, and then a closed-form solution of the transformation is computed. This new algorithm converges monotonically to a local minimum from any given initial scaling transformation. To obtain the expected minimum, the traditional scaling ICP algorithm is applied to compute the initial transformation. The experimental results demonstrate that our algorithm can prevent the influence of the local dissimilarity and improve the registration precision compared with the traditional ICP algorithm. [ABSTRACT FROM AUTHOR]

The scale invariant feature transform (SIFT) algorithm has been applied to many fields, it has been found that the algorithm has some problems, such as high complexity, it is easily led to the dimensional disaster and it is not completely affine invariant. Some scholars have proposed improved algorithms to solve these problems, but these algorithms were specific to certain solutions and not applicable for comprehensively solving the above problems. To solve these problems, an adaptive image feature extraction algorithm based on bi-dimensional local mean decomposition (BLMD) and SIFT is proposed in this paper. First, adaptive BLMD is used to decompose the image and obtain a number of bi-dimensional production functions (BPFs). Second, the SIFT algorithm based on parameter optimization is used to extract the features of the decomposed BPFs. Finally, we synthesize and process the feature information extracted by the BPFs to obtain all the feature information of the original image. Traditional feature extraction methods and the proposed method are compared and analyzed in three different scenarios involving face database images with different scales and levels of blur. The proposed method yields rich and complete feature information and is beneficial to image matching and registration. Moreover, the proposed is more efficient than other methods. The proposed approach provides a technical method for image adaptive feature extraction and a directional framework for the development and improvement of adaptive image feature extraction schemes. [ABSTRACT FROM AUTHOR]

Abstract: Image registration problem often arises in microscopy when analyzing cell images. The most popular registration methods are rigid methods that use affine transformations. These methods are good enough for different types of images and image modalities, but they are very slow. This makes speed optimization techniques for these methods of particular importance. In this paper, we propose an algorithm for finding the optimal image downsampling coefficient to speedup image registration methods. The algorithm is tested for different rigid registration methods on HeLa cell images. [ABSTRACT FROM AUTHOR]

Abstract: Personalizing the three-dimensional atlas model (template) of an organ under analysis based on the data of patient’s three-dimensional examination is an important task for modern digital medicine. The template should be represented by a set of points Y and marked with the key points for the model (landmarks). The template is personalized by the non-rigid registration of the set Y with the set of points X that represents patient’s tomogram. Presently, the coherent point drift (CPD) is the most popular method for solving the problem of non-rigid alignment. In this paper, we propose and explore an approach that substantially improves the CPD result in the problem of automatic registration of cephalometric points (CPs). The proposed algorithm remains robust in the presence of significant skull deformations. Traditionally, 3D cephalometry uses the geometric descriptor of a CP, which refines the position of the point on the bone surface. However, the result of applying the descriptor depends significantly on the accuracy of its anatomical basis reconstruction. The proposed approach solves this problem by clarifying the basis of geometric descriptors, the supporting elements of which are orbital planes and the Frankfurt (orbital-ear) horizontal. For this purpose, additionally marked points of the orbitals (YO) are included into the template Y. Once Y and X are aligned by the CPD method, the plane positions of the orbitals are refined by solving a linear regression problem on the subsets of YO for the left and right orbitals. Cases of refinement with and without use of Tikhonov regularization (ridge-regression) are analyzed. The effect of the increase in the cardinality of the set X relative to Y on the registration accuracy is investigated. It is found that the condition |X| < |Y| has a negative effect on the accuracy, which increases when the cardinality of X relative to Y decreases. The refinement of CPs by the geometric descriptor is carried out on tomogram data in the region around CPs that is found by the CPD method. The dimensions of this region along three coordinates are determined by the anatomical domain of a particular CP descriptor. The quality of the algorithm is measured by the Euclidean distance between hand-marked and automatically found points. The template Y for the algorithm is built on a trauma-deformed skull. The algorithm is quantitatively verified by registering the CPs of orbitals and cheekbones from the data of four tomograms for the deformed skull. A key feature and source of high accuracy of the approach is the use of linear regression with Tikhonov regularization to refine it. As a result, 81.25% of the points found fall within a radius of 2 mm from the hand-marked points and 100% of the points fall within a radius of 4 mm. [ABSTRACT FROM AUTHOR]

This paper presents a robust point-pattern matching (PPM) algorithm, in which the invariant feature and probabilistic relaxation labeling are combined to improve the assignment accuracy and efficiency. A local feature descriptor, namely, point pair local topology (PPLT), is proposed first. The feature descriptor is defined by histogram which is constructed using the weighting of distance measures and angle measures based on local point pair. We use the matching scores of point pair local topology descriptor's statistic test to define new compatibility coefficients. Then, the robust support functions are constructed based on the obtained compatibility coefficients. Finally, according to the relaxed iterations of matching probability matrix and the mapping constraints required by the bijective correspondence, the correct matching results are obtained. A number of comparison and evaluation experiments on both synthetic point sets and real-world data demonstrate that the proposed algorithm performs better in the presence of outliers and positional jitter. In addition, it achieves the superior performance under similarity and even nonrigid transformation among point sets in the meantime compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Visual saliency is a type of visual feature which simulates visual attention selection mechanism in biological system and has better robustness and invariance. A rapid infrared image and visible light image registration method based on visual saliency and SIFT (scale invariant feature transform) is proposed in this paper. The method adopts amplitude modulation Fourier transform to construct saliency map, and the image salient points are achieved preliminarily by salient threshold. Subsequently, this method calculates the local entropy for these salient points because of entropy’s character for information measurement, then these points are reordered and screened based on the strategy of entropy priority. The screened results are thought as centers for salient regions. Morphological operation is used for growing and merging for neighbor salient scene region in image. Aiming at the abstracted salient region for image, PCA (principal component analysis)-SIFT algorithm is proposed, which can produce the compressed SIFT registration features and largely reduce the computational cost of image registration. The proposed algorithm adopts random sampling conformance method to remove the mistaken point pairs before calculating the model parameter of affine transformation for registration between infrared image and visible lights. The experimental results indicate that the method has good invariance under image scale, rotation, translation, and illumination variation and can realize effective registration between infrared image and visible lights. Compared with some classical algorithms, the proposed method has advantage in registration accuracy and registration speed obviously. [ABSTRACT FROM AUTHOR]

In this paper we systematically evaluate the performance of several state-of-the-art local feature detectors and descriptors in the context of longitudinal registration of retinal images. Longitudinal (temporal) registration facilitates to track the changes in the retina that has happened over time. A wide number of local feature detectors and descriptors exist and many of them have already applied for retinal image registration, however, no comparative evaluation has been made so far to analyse their respective performance. In this manuscript we evaluate the performance of the widely known and commonly used detectors such as Harris, SIFT, SURF, BRISK, and bifurcation and cross-over points. As of descriptors SIFT, SURF, ALOHA, BRIEF, BRISK and PIIFD are used. Longitudinal retinal image datasets containing a total of 244 images are used for the experiment. The evaluation reveals some potential findings including more robustness of SURF and SIFT keypoints than the commonly used bifurcation and cross-over points, when detected on the vessels. SIFT keypoints can be detected with a reliability of 59% for without pathology images and 45% for with pathology images. For SURF keypoints these values are respectively 58% and 47%. ALOHA descriptor is best suited to describe SURF keypoints, which ensures an overall matching accuracy, distinguishability of 83%, 93% and 78%, 83% for without pathology and with pathology images respectively. [ABSTRACT FROM AUTHOR]