Image Matching Method Research Articles

Photogrammetric Monitoring of an Artificially Generated Shallow Landslide

AbstractAn artificial rainfall event was applied to a forested slope in Ruedlingen, northern Switzerland. The experiment triggered a landslide which resulted in mobilising about 130 m3 of debris. The event was monitored by a photogrammetric network of four cameras, operating at 5 to 8 frames per second, in order to quantify spatial and temporal changes by tracking tennis balls pegged into the ground. Image measurements were performed using automated image matching methods, implemented through a software package developed in‐house. Three‐dimensional coordinates of the target points were estimated by running a customised type of bundle adjustment, achieving a positioning precision of ± 1·8 cm.

Cartridge case image matching using effective correlation area based method

A firearm leaves a unique impression on fired cartridge cases. The cross-correlation function plays an important role in matching the characteristic features on the cartridge case found at the crime scene with a specific firearm, for accurate firearm identification. This paper proposes that the computational forensic techniques of alignment and effective correlation area-based approaches to image matching are essential to firearm identification. Specifically, the reference and the corresponding cartridge cases are aligned according to the phase-correlation criterion on the transform domain. The informative segments of the breech face marks are identified by a cross-covariance coefficient using the coefficient value in a window located locally in the image space. The segments are then passed to the measurement of edge density for computing effective correlation areas. Experimental results on a new dataset show that the correlation system can make use of the best properties of alignment and effective correlation area-based approaches, and can attain significant improvement of image-correlation results, compared with the traditional image-matching methods for firearm identification, which employ cartridge-case samples. An analysis of image-alignment score matrices suggests that all translation and scaling parameters are estimated correctly, and contribute to the successful extraction of effective correlation areas. It was found that the proposed method has a high discriminant power, compared with the conventional correlator. This paper advocates that this method will enable forensic science to compile a large-scale image database to perform correlation of cartridge case bases, in order to identify firearms that involve pairwise alignments and comparisons.

IMAGE ACQUISITION AND MODEL SELECTION FOR MULTI-VIEW STEREO

Abstract. Dense image matching methods enable efficient 3D data acquisition. Digital cameras are available at high resolution, high geometric and radiometric quality and high image repetition rate. They can be used to acquire imagery for photogrammetric purposes in short time. Photogrammetric image processing methods deliver 3D information. For example, Structure from Motion reconstruction methods can be used to derive orientations and sparse surface information. In order to retrieve complete surfaces with high precision, dense image matching methods can be applied. However, a key challenge is the selection of images, since the image network geometry directly impacts the accuracy, as well as the completeness of the point cloud. Thus, the image stations and the image scale have to be selected according carefully to the accuracy requirements. Furthermore, most dense image matching solutions are based on multi-view stereo algorithms, where the matching is performed between selected pairs of images. Thus, stereo models have to be selected from the available dataset in respect to geometric conditions, which influence completeness, precision and processing time. Within the paper, the selection of images and the selection of optimal stereo models are discussed according to to photogrammetric surface acquisition using dense image matching. For this purpose, impacts of the acquisition geometry are evaluated for several datasets. Based on the results, a guideline for the acquisition of imagery for photogrammetric surface acquisition is presented. The simple and efficient capturing approach with "One panorama each step" ensures complete coverage and sufficiently redundant observations for a surface reconstruction with high precision and reliability.

Multi-modal image matching based on local frequency information

This paper challenges the issue of matching between multi-modal images with similar physical structures but different appearances. To emphasize the common structural information while suppressing the illumination and sensor-dependent information between multi-modal images, two image representations namely Mean Local Phase Angle (MLPA) and Frequency Spread Phase Congruency (FSPC) are proposed by using local frequency information in Log-Gabor wavelet transformation space. A confidence-aided similarity (CAS) that consists of a confidence component and a similarity component is designed to establish the correspondence between multi-modal images. The two representations are both invariant to contrast reversal and non-homogeneous illumination variation, and without any derivative or thresholding operation. The CAS that integrates MLPA with FSPC tightly instead of treating them separately can more weight the common structures emphasized by FSPC, and therefore further eliminate the influence of different sensor properties. We demonstrate the accuracy and robustness of our method by comparing it with those popular methods of multi-modal image matching. Experimental results show that our method improves the traditional multi-modal image matching, and can work robustly even in quite challenging situations (e.g. SAR & optical image).

Precise Remote Measuring System for Building 3D Spatial Information on Rocks that Cover and Uncover

ABSTRACT Hwang, J.S, Yun, H.S., Kang, I.G., Choi, Y.S., Lee, D.H. and Hong, S. For this study, a system capable of measuring the absolute position and shape of nearby rocks that cover and uncover along the shore precisely and remotely was devised. The system, consisting of a GPS/INS and a stereo digital camera, can be attached to the side of a ship. A surveying ship equipped with the system may sail around the rocks, obtaining GPS and INS data on the rocks, and digital images captured with a stereo cameras. The data can then be post-processed to obtain precise 3D spatial information on the rocks. The key methods used to realize the system was GPS/INS integration method, real-time direct geo-referencing method for a digital camera, and 3D scanning technology that automatically matches stereo images. Among these methods, the stereo image matching method was developed for this study. The method was realized by combining the conventional SURF descriptor generation algorithm, the matching methods using the sca...

尺度、旋转以及亮度稳健的高分辨率影像直线特征匹配

A line feature matching method for high-resolution images was proposed to improve the low significant level of a point feature and to overcome the matching shortage between weak texture images.Firstly,the edges of images were extracted and tracked to fit straight-lines.All straight-lines were classified into two groups: long-lines and short-lines.The long-lines were matched based on the direction relationship primarily.Then,the relationship-descriptors of short-lines were constructed using the angle and the Euclidean distance between the short-line and long correspondences.Finally,short-lines were matched according to the similarity of their relationship-descriptors.The experimental results demonstrate that the proposed line matching algorithm is robust for the scale,rotation and illumination.As all the lines have corresponding linear equations in the same image,the image feature has higher significant level and can avoid the mismatching.The probability of correct matches of the algorithm exceeds 90% and its root mean square error has achieved sub-pixel level.The performance of the proposed algorithm is better than that of the point-based method,especially in weak texture areas.

1003 簡易イメージマッチング法による3次元膝関節モデルを用いた膝屈曲運動中の脛骨大腿関節位置の同定(GS-2 CT・イメージング)

1003 簡易イメージマッチング法による3次元膝関節モデルを用いた膝屈曲運動中の脛骨大腿関節位置の同定(GS-2 CT・イメージング)

고해상도 위성영상의 토지피복 특성에 따른 혼합정합

This study proposes fusion image matching method according to land cover property to generate a detailed DEM using the high resolution IKONOS-2 stereo pair. A classified image, consists of building, crop-land, forest, road and shadow-water, is produced by color image with four bands. Edges and points are also extracted from panchromatic image. Matching is performed by the cross-correlation computing after five classes are automatically selected in a reference image. In each of building class, crop-land class, forest class and road class, matching was performed by the grid and edge, only grid, only grid, grid and point, respectively. Shadow-water class was excepted in the matching because this area causes excessive error of the DEM. As the results, edge line, building and residential area could be expressed more dense than DEM by the conventional method.

Invariant matching method for different viewpoint angle images

In recent years, many methods have been put forward to improve the image matching for different viewpoint images. However, these methods are still not able to achieve stable results, especially when large variation in view occurs. In this paper, an image matching method based on affine transformation of local image areas is proposed. First, local stable regions are extracted from the reference image and the test image, and transformed to circular areas according to the second-order moment. Then, scale invariant features are detected and matched in the transformed regions. Finally, we use epipolar constraint based on the fundamental matrix to eliminate wrong corresponding pairs. The goal of our method is not to increase the invariance of the detector but to improve the final performance of the matching results. The experimental results demonstrate that compared with the traditional detectors the proposed method provides significant improvement in robustness for different viewpoint images matching in the 2D scene and 3D scene. Moreover, the efficiency is greatly improved compared with affine scale invariant feature transform (Affine-SIFT).

Detection of Video Shot Changes Based on Image Matching

In order to improve the supervision of TV commercials and reduce the illegal ones, it is necessary to develop a system to detect TV commercials in real time. One of the key points is to detect video shot changes. We put forward a novel method of image matching for shot change detection: firstly, a feature coding is introduced for describing image local gray level distribution; secondly, image matching is performed according to the feature coding; thirdly, shot changes can be detected. A preliminary system of video shot change detection is developed. Experiments show that the algorithm is better than traditional algorithms and can well meet the need of TV Video shot cut detection.

2-D Image Matching Based on Global Harmony Search Optimization

A two-dimensional image matching method based on the improved Hausdorff distance and global harmony search optimization is presented in this paper. First, edges are extracted form the original images by the Canny edge detector. Then, a fitness function based on the improved Hausdorff distance is constructed. Finally the global harmony search optimization is adopted to optimize the fitness function. Experiments show that the proposed method is able to locate the object of interest globally and efficiently.

Research of Image Matching Based on Evolutionary Algorithm

In image matching research, how to ensure that best match’s accuracy of the premise and a significant reduction in the amount of computing is the focus of concern by researchers. Search strategy to find the best match location of the image matching process to determine the amount of computing of image matching, in the existing image matching method are used to traverse search strategy, it is difficult to reduce the amount of computing. This is a common defect of the existing image matching algorithm. Traditional evolutionary algorithm trapped into the local minimum easily. Therefore, based on a simple evolutionary algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, to prevent local convergence to form a new evolutionary algorithm. Compared the traditional evolutionary algorithm, the new algorithm enlarges the searching space and the complexity is not high. We use this new algorithm in image matching; from the results we reach the conclusion: in the optimization precision and the optimization speed, the new algorithm is efficiency for the image match problem.

Kinematic study of root elongation in Arabidopsis thaliana with a novel image-analysis program

The measurement of the spatial profile of root elongation needs to determine matching points between time-lapse images and calculate their displacement. These data have been obtained by laborious manual methods in the past. Some computer-based programs have been developed to improve the measurement, but they require many time-series digital images or sprinkling graphite particles on the root prior to image capture. Here, we have developed GrowthTracer, a new image-analysis program for the kinematic study of root elongation. GrowthTracer employs a multiresolution image matching method with a nonlinear filter called the critical point filter (CPF), which extracts critical points from images at each resolution and can determine precise matching points by analysis of only two intact images, without pre-marking by graphite particles. This program calculates the displacement of each matching point and determines the displacement velocity profile along the medial axis of the root. In addition, a manual input of distinct matching points increases the matching accuracy. We show a successful application of this novel program for the kinematic analysis of root growth in Arabidopsis thaliana.

Real-Time Image-Identification-Based Anti-Manmade Misoperation System for Substations

A novel antimisoperation system is proposed to prevent manmade misoperations using real-time signboard image identification technology. A portable video capture device is employed to capture real-time images of the current working bay's substation signboard. These images, with the aid of image identification, are used to determine whether the operations staff has accessed a wrong bay. The framework and workflow of the system are provided, and the implementation of the signboard image border detection, character partition, and image-matching methods are also introduced in detail. The effectiveness and engineering applicability of the proposed system are demonstrated through lab results and in-field tests. The proposed system can be used in conjunction with an active five-prevention system to further improve the validity and safety of substation operations.

A survey of image processing techniques and statistics for ballistic specimens in forensic science

This paper provides a review of recent investigations on the image processing techniques used to match spent bullets and cartridge cases. It is also, to a lesser extent, a review of the statistical methods that are used to judge the uniqueness of fired bullets and spent cartridge cases. We review 2D and 3D imaging techniques as well as many of the algorithms used to match these images. We also provide a discussion of the strengths and weaknesses of these methods for both image matching and statistical uniqueness. The goal of this paper is to be a reference for investigators and scientists working in this field.

A SEMIAUTOMATIC APPROACH FOR GENERATION OF SITE MODELS FROM CARTOSAT-2 MULTIVIEW IMAGES

Abstract. In the last decade there has been a paradigm shift in creating, viewing and utilizing geospatial data for planning, navigation and traffic management of urban areas. Realistic, three-dimensional information is preferred over conventional two dimensional maps. The paper describes objectives, methodology and results of an operational system being developed for generation of site model from Cartosat-2 multiview images. The system is designed to work in operational mode with varying level of manual interactivity. A rigorous physical sensor model based on collinearity condition models the "step n stare" mode of image acquisition of the satellite. The relative orientation of the overlapping images is achieved using coplanarity condition and conjugate points. A procedure is developed to perform digitization in mono and stereo modes. A technique for refining manually digitized boundaries is developed. The conjugate points are generated by establishing a correspondence between the points obtained on refined edges to analogous points on the images obtained with view angles ±26 deg. It is achieved through geometrically constrained image matching method. The results are shown for a portion of multi-view images of Washington City obtained from Cartosat-2. The scheme is generic to accept very high resolution stereo images from other satellites as input.

A MULTI-VIEW IMAGE MATCHING METHOD FOR FEATURE POINTS BASED ON THE MOVING Z-PLANE CONSTRAINT

Abstract. Focusing on the serious occlusion problem in city images, this paper makes full use of the advantage of multi-view image matching, and proposes a reliable multi-view image matching method based on the moving Z-Plane constraint. It supposes a fictitious plane in the object space, and the plane is divided to regular grid cell (small plane element) by a certain interval (≥ image resolution). By moving the plane to different elevation positions, this algorithm makes feature point projection ray in overall images intersect with the plane, and constrains the candidate points by grid cells in the plane. Feature points which come from different images projection ray in the same grid cell on the plane may be regarded as the matching candidates. It selects the images which matching candidate points by gray similarity constraint to avoid the effect from occlusion image. According to the number of projection ray in the grid cell, this algorithm adopts hierarchy matching strategy of "the best candidate will be matched in the first instant", and uses initial matching results as constraint condition in the latter matching process. Finally, the validity of the algorithm proposed in this paper is verified by the experiments using four UltraCamX (UCX) digital aerial images and the algorithm is shown to have reliable matching results.

OBJECT-SPACE MULTI-IMAGE MATCHING OF MOBILE-MAPPING-SYSTEM IMAGE SEQUENCES

Abstract. This paper proposes an object-space multi-image matching procedure of terrestrial MMS (Mobile Mapping System) image sequences to determine the coordinates of an object point automatically and reliably. This image matching procedure can be applied to find conjugate points of MMS image sequences efficiently. Conventional area-based image matching methods are not reliable to deliver accurate matching results for this application due to image scale variations, viewing angle variations, and object occlusions. In order to deal with these three matching problems, an object space multi-image matching is proposed. A modified NCC (Normalized Cross Correlation) coefficient is proposed to measure the similarity of image patches. A modified multi-window matching procedure will also be introduced to solve the problem of object occlusion. A coarse-to-fine procedure with a combination of object-space multi-image matching and multi-window matching is adopted. The proposed procedure has been implemented for the purpose of matching terrestrial MMS image sequences. The ratio of correct matches of this experiment was about 80 %. By providing an approximate conjugate point in an overlapping image manually, most of the incorrect matches could be fixed properly and the ratio of correct matches was improved up to 98 %.

COMBINED GEOMETRIC/RADIOMETRIC POINT CLOUD MATCHING FOR SHEAR ANALYSIS

Abstract. In the recent past, dense image matching methods such as Semi-Global Matching (SGM) became popular for many applications. The SGM approach has been adapted to and implemented for Leica ADS line-scanner data by North West Geomatics (North West) in co-operation with Leica Geosystems; it is used in North West’s production workflow. One of the advantages of ADS imagery is the calibrated color information (RGB and near infrared), extending SGM-derived point clouds to dense “image point clouds” or, more general, information clouds (info clouds). With the goal of automating the quality control of ADS data, info clouds are utilized for Shear Analysis: Three-dimensional offsets of adjacent ADS image strips are determined from a pattern of info cloud pairs in strip overlaps by point cloud matching. The presented approach integrates geometry (height) and radiometry (intensity) information; matching is based on local point-to-plane distances for all points in a given cloud. The offset is derived in a least squares adjustment by applying it to each individual distance computation equation. Using intensities in addition to heights greatly benefits the offset computation, because intensity gradients tend to occur more frequently than height gradients. They can provide or complement the required information for the derivation of planimetric offset components. The paper details the combined geometric/radiometric point cloud matching approach and verifies the results against manual measurements.

Topological visual mapping in robotics

A key problem in robotics is the construction of a map from its environment. This map could be used in different tasks, like localization, recognition, obstacle avoidance, etc. Besides, the simultaneous location and mapping (SLAM) problem has had a lot of interest in the robotics community. This paper presents a new method for visual mapping, using topological instead of metric information. For that purpose, we propose prior image segmentation into regions in order to group the extracted invariant features in a graph so that each graph defines a single region of the image. Although others methods have been proposed for visual SLAM, our method is complete, in the sense that it makes all the process: it presents a new method for image matching; it defines a way to build the topological map; and it also defines a matching criterion for loop-closing. The matching process will take into account visual features and their structure using the graph transformation matching (GTM) algorithm, which allows us to process the matching and to remove out the outliers. Then, using this image comparison method, we propose an algorithm for constructing topological maps. During the experimentation phase, we will test the robustness of the method and its ability constructing topological maps. We have also introduced new hysteresis behavior in order to solve some problems found building the graph.