The generic combinatorial algorithm for image matching with classes of projective transformations

The algorithms based on image feature detection and matching are critical in the field of computer vision. Feature extraction and matching algorithms are used in many computer vision problems, including object recognition and structure from motion. Each feature detector and descriptor algorithm’s computational efficiency and robust performance have a major impact on image matching precision and time utilization. The performance of image matching algorithms that use expensive descriptors for detection and matching is addressed using existing approaches. The algorithm’s efficiency is measured by the number of matches found and the number of faults discovered when evaluated against a given pair of images. It also depends on the algorithm that detects the features and matches them in less amount of time. This paper examines and compares the different algorithm (SURF, ORB, BRISK, FAST, KAZE, MINEIGEN, MSER) performances using distinct parameters such as affine transformation, blur, scale, illumination, and rotation. The Oxford dataset is used to assess their robustness and efficiency against the parameters of interest. The time taken to detect features, the time taken to match images, the number of identified feature points, and the total running time is recorded in this study. The quantitative results show that the ORB and SURF algorithms detect and match more features than other algorithms. Furthermore, they are computationally less expensive and robust compared to other algorithms. In addition, the robustness of ORB and SURF is quite high in terms of outliers, and the amount of time taken to match with the reference is also significantly less. However, the efficiency of SURF reduces against blur transformation. FAST is good in detecting corners but lacks efficiency under different transformations. Experiments show that when each algorithm is subjected to numerous alterations, it has its own set of advantages and limitations.

Image matching is a process to establish correspondence between primitives of two or more images that capturing a scene from different viewing position. Various image matching techniques using image features have been known in literature. Feature-based matching algorithm cannot tackle the problem of matching ambiguities easily. This study presents an image matching technique using the structural descriptions of image. Structural descriptions are consist of lines and interline relationship in the line-extracted image. Three conditions of inter-line relationship, namely ordering, intersection and co-linearity, were defined and derived in this study. The method involves the representation of the structural descriptions of image in relational graph and the matching between relational graphs to perform image matching. The methodology is consists of six steps: (1) input image, (2) line segment extraction from the image, (3) the interpretation and derivation of structural descriptions from the line-extracted image, (4) the construction of relational graph to represent the structural descriptions, (5) the derivation of association graph from relational graphs to perform relational graph matching, and (6) the searching of the largest maximal clique in the association graph to determine the best matching. Hence, image matching is transformed as a relational graph matching problem in this study. Experiments are carried out to evaluate the applicability of incorporating structural information into the image matching algorithm. The data is consisting of 14 pairs of stereo images. From the result obtained, it was found that the usage of structural information of image is only plausible for the matching of images of simple scene. The matching accuracy of images of complicated scene remains low even after the incorporation of inter-line descriptions into the image matching algorithm.

Image matching as a data source for forest inventory – Comparison of Semi-Global Matching and Next-Generation Automatic Terrain Extraction algorithms in a typical managed boreal forest environment

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.

GAT (Global Affine Transformation) and GPT (Global Projection Transformation) matchings proposed by Wakahara and Yamashita calculate the optimal AT (affine transformation) and PT (2D projection transformation), respectively. These image matching criteria realize deformation-tolerant matchings by maximizing the normalized cross-correlation between a template and a GAT/GPT-superimposed image. In order to shorten the calculation time, Wakahara and Yamashita also proposed the acceleration algorithms for GAT/GPT matchings. Later on, Wakahara et al. proposed the enhanced GPT matching to calculate the optimal PT parameters simultaneously which overcomes the incompatibility during the matching process. Zhang et al. figured out that these matching techniques do not take account of the conservation of the L2 norm, and introduced norm normalization factors that realize accurate and stable matchings. All these correlation-based matching techniques are well suited for “whole-to-whole” image matching, but are weak in “whole-to-part” image matching being cursed by complex backgrounds and noise. This research firstly proposes simplified HOG patterns for the enhanced GPT matching with norm normalization to obtain the robustness against noise and background. Secondly, this research also proposes the acceleration algorithm for the proposed matching criterion by creating several reference tables. Experiments using the Graffiti dataset show that the proposed method exhibits an outstanding matching ability compared with the original GPT correlation matching and the well-known combination of SURF feature descriptor and RANSAC algorithm. Furthermore, the computational complexity of the proposed method is significantly reduced below double figures via the acceleration algorithm.

In a vision-based system for mobile robot/human localization in the environment, extracting feature from images and image matching are important for the final results. Although there are many algorithms for image matching, they are not fit for this system. A new method is designed to improve image matching effect.

Based on analyzing the traditional image matching algorithms, a fast image matching method, which integrates improved sequential similarity detection algorithm (SSDA) and wavelet transformation, is presented in this paper. Firstly, some basic principles of SSDA image matching and wavelet transformation are introduced. Secondly, according to the shortcoming of traditional SSDA algorithm, a new SSDA algorithm is studied for improving the determining velocity of the threshold in SSDA searching match. By integrating wavelet transformation, an integrated matching algorithm is given out. Finally, the new method is compared with the traditional matching algorithms in experiments. The experimental results show that the new method has higher efficiency and accuracy in image matching and is suitable for the realization of real-time underwater navigation.

BI&T Tell me about the Medical Imaging and Technology Alliance. David Fisher The Medical Imaging & Technology Alliance (MITA), a division of the National Electrical Manufacturers Association (NEMA), is the leading organization and collective voice of medical imaging equipment manufacturers, innovators, and product developers. It represents companies whose sales comprise more than 90 percent of the global market for medical imaging technology. The goals of MITA are to: • Increase the awareness and understanding of the value of medical imaging • Achieve efficient and reasonable regulation of medical imaging technologies • Interact with appropriate government agencies on reimbursement and technology assessment policies • Expand the global acceptance of the digital communications standard (DICOM) that allows digital imaging technologies to interact seamlessly • Improve regulatory harmonization of the global market for medical imaging products • Develop and represent industry positions in technical, trade and other issues • Provide market data unique to this industry

A real-time image matching algorithm for integrated navigation system

Binocular stereo measurement system can obtain accurate three-dimensional information from two-dimensional images. It has been widely applied in many fields such as vehicle tracking, robot navigating, automatic crane lifting, as well as other fields. The crucial step of binocular stereo measurement is image matching. For the image matching, it is a great challenge to ensure both real-time and matching accuracy simultaneously. The image matching algorithm has a great influence on the image matching time and accuracy. In this paper, a real-time image matching algorithm for binocular stereo measurement system is proposed based on Speedup Robust Features (SURF) algorithm. In the proposed algorithm, firstly, the key feature points are identified by the original SURF algorithm method. Secondly, the main direction of the key feature point is determined by intensity centroid method. Then, the feature descriptor is calculated by the BRIEF binary method so that the time of feature description can be shortened. Finally, RANSAC (Random Sample Consensus) method is adopted to remove mismatching points. The experiments results show that the proposed algorithm can shorten image matching time obviously and improve the accuracy of matching points.

Today, computer vision has shown a variety of roles in our lives, making people’s life more convenient. Also, a variety of artificial intelligence models and algorithms have emerged for computer vision. Image matching is an important technique in the field of computer vision to find the similarities between two images or multiple images with the help of matching algorithms to achieve scientific and accurate processing of images. Therefore, summarizing the effective approximate solution to this problem as well as the future research is the main part for current research. The paper firstly describes the basic elements of the image matching technique. Then, some representative traditional image matching algorithms proposed in the field of computer vision research in recent years are summarized and reviewed. Finally, the future research directions and research ideas of image matching are discussed, providing reliable guidance and reference for subsequent research.

In today's big data era, with the development of the Internet of Things (IoT) technology and the trend of autonomous driving prevailing, visual information has shown a blowout increase, but most image matching algorithms have problems such as low accuracy and low inlier rates, resulting in insufficient information. In order to solve the problem of low image matching accuracy and low inlier rate in the field of autonomous driving, this research innovatively applies spectral clustering (SC) in the field of data analysis to image matching in the field of autonomous driving, and a new image matching algorithm "SC-RANSAC" based on SC and Random Sample Consensus (RANSAC) is proposed. The datasets in this research are collected based on the monocular cameras of autonomous driving cars. We use RANSAC to obtain the initial inlier set and the SC algorithm to filter RANSAC's outliers and then use the filtered inliers as the final inlier set. In order to verify the effectiveness of the algorithm, it shows the matching effect from three angles: camera translation, rotation, and rotation and translation. SC-RANSAC is also compared with RANSAC, graph-cut RANSAC, and marginalizing sample consensus by using two different types of datasets. Finally, we select three representative pictures to test the robustness of the SC-RANSAC algorithm. The experimental results show that SC-RANSAC can effectively and reliably eliminate mismatches in the initial matching results; has a high inlier rate, real-time performance, and robustness; and can be effectively applied in the environment of autonomous driving.

Aiming at the problems of large calculating scale and high complexity in Scale Invariant Feature Transform (SIFT) feature matching algorithm, this paper presents an improved SIFT feature matching algorithm based on quasi Euclidean distance.The traditional Euclidean distance can only calculate out the variance of the two images to the corresponding pixel, so when a slight shift or distortion occurs in the image, it may produce a large deviation.The quasi Euclidean distance instead of Euclidean distance is as the similarity measure of feature descriptors to improve the SIFT feature matching.It reduces the dimensions of SIFT feature vector to improve the efficiency of feature matching.Experimental results show that under the condition of keeping the image matching rate and algorithm robust, the method can not only improve the matching accuracy but also shorten the matching time.The new algorithm has better performance than traditional algorithm, it is possible and valid, which are useful for the fields of image recognition, image reconstruction, etc.

Algorithm for rotated image matching was slow, inefficient and standard genetic algorithm (SGA) in image matching was not high in accuracy, this article introduce a fast anti-rotation image matching algorithm based on hybrid genetic algorithm. The algorithm combines GA and hill-climbing algorithm in an efficient way and performs fast both in global search and local search. The algorithm use circular and ring template instead of rectangle template, and use the histogram of the template to calculate the fitness of the individual in the hybrid genetic algorithm. The speed and accuracy of the Algorithm was proved by the experiment.

A novel dissimilarity measure is proposed to perform correspondence image matching for object recognition, image registration and content-based image retrieval. This is a feature-based matching, which supposes image representation (object description) in the form of a set of multi-location descriptor vectors. The proposed measure called intersection matching distance eliminates outlies (false or missing feature points) while transformation-invariantly matching two sets of descriptor vectors. A block-subdivision algorithm for time-efficient image matching is also described.