Improved Registration Algorithm Research Articles

An image fusion algorithm based on iterative wavelet transform for space non-cooperative targets

Due to the interference of outer space environment, the real image data of non-cooperative targets collected by a single sensor often has problems such as low contrast and insufficient color information display. In this paper, we propose an image fusion method with iterative wavelet transform, which utilizes the respective advantages of visible image and near-infrared image to improve the clarity of space non-cooperative target and retain color information. The dataset in this paper comes from the satellite model group taken by ourselves. Due to different shooting angles and resolutions, we need to carry out accurate registration of near-infrared images and visible light. Therefore, we also propose an improved registration algorithm based on ORB feature matching. In order to verify the effectiveness of the method, other image fusion methods are selected for comparative analysis. The method proposed can preserve texture features and color information of space non-cooperative targets.

Solution of Spatial Transformation Relationship of Similar Ruled Surfaces Based on Registration of Divided Regions

Since the geometric transformation relationship of similar surfaces with complex features, such as local deformation and curvature changes, is hard to be solved through global registration, this paper proposes a method for solving the spatial transformation relationship of similar ruled surfaces based on registration of divided regions. First, an adaptive region division algorithm is proposed to divide similar surfaces, and then, an improved registration algorithm is proposed by adding two constraints which are the curvature feature and differential geometric features of point clouds. Through this improved registration algorithm, the geometric transformation relationship of each sub-region can be solved, and then the spatial geometric transformation relationship of the overall similar surface can be established. Moreover, the improved registration algorithm can ensure that the differential geometric properties of corresponding points are similar after registration, which may provide a basis for mapping and reuse of process knowledge between corresponding points on similar surfaces. Finally, two similar ruled surface blades are taken as examples for simulation verification, the results show that the maximum registration error of each sub-region is 0.025 mm, which is within the allowable error range, and the registration speed of the proposed algorithm is better than the S-ICP algorithm. This proves that the method in this paper is feasible and effective.

Improved Registration Algorithm Based on Double Threshold Feature Extraction and Distance Disparity Matrix.

Entire surface point clouds in complex objects cannot be captured in a single direction by using noncontact measurement methods, such as machine vision; therefore, different direction point clouds should be obtained and registered. However, high efficiency and precision are crucial for registration methods when dealing with huge number of point clouds. To solve this problem, an improved registration algorithm based on double threshold feature extraction and distance disparity matrix (DDM) is proposed in this study. Firstly, feature points are extracted with double thresholds using normal vectors and curvature to reduce the number of points. Secondly, a fast point feature histogram is established to describe the feature points and obtain the initial corresponding point pairs. Thirdly, obviously wrong corresponding point pairs are eliminated as much as possible by analysing the Euclidean invariant features of rigid body transformation combined with the DDM algorithm. Finally, the sample consensus initial alignment and the iterative closest point algorithms are used to complete the registration. Experimental results show that the proposed algorithm can quickly process large data point clouds and achieve efficient and precise matching of target objects. It can be used to improve the efficiency and precision of registration in distributed or mobile 3D measurement systems.

Improved registration algorithm for spatial non-cooperative target point cloud clustering

Improved registration algorithm for spatial non-cooperative target point cloud clustering

Accurate three dimensional body scanning system based on structured light.

Three dimensional (3D) body scanning has been of great interest to many fields, yet it is still a challenge to generate accurate human body models in a convenient manner. In this paper, we present an accurate 3D body scanning system based on structured light technology. A four-step phase shifting combined with Gray-code method is applied to match pixels in camera and projector planes. The calculation of 3D point coordinates are also derived. The main contribution of this paper is twofold. First, an improved registration algorithm is proposed to align point clouds reconstructed from different views. Second, we propose a graph optimization algorithm to further minimize registration errors. Experimental results demonstrate that our system can produce accurate 3D body models conveniently.

A Robust Registration Algorithm for Image-Guided Surgical Robot

The aim of this paper is to propose a robust registration algorithm to reduce the impact of the marker-recognition error on image-to-physical registration in the robot-assisted cranio and maxillofacial (CMF) surgery. Since the image-guided technology has been widely used in the CMF surgery, the surgical robot based on remote or cloud plan data has come into focus. As a critical procedure of the image-guided surgical robot, the image-to-physical registration has become a decisive factor of the operation result. The recognition error of the reference points is a main challenge for the registration. Therefore, we propose an improved method to cope with the noise in the image space. The image-to-physical registration is accomplished via a group of implanted reference markers. Firstly, the reference markers in the image space are picked out manually and extended to a fuzzy point set via a directional region growing algorithm. Then, the reference markers in the physical space are localized by the navigation cameras. At last, the transfer matrix is calculated using an improved registration algorithm based on the geometrical features of reference points in the two spaces. The experimental results demonstrate that the proposed method is less sensitive to the number of the reference points, and the influence of recognition noise can be decreased effectively. This paper is a foundational link of the cloud-based surgical robots, and the results of every case will be collected to the cloud for further data mining and analyzing.

An Improved Image Registration Based on Nonsubsampled Contourlet Transform and Zernike Moments

Feature-based registration is an effective and most widely used image registration method currently. It includes three critical steps, feature extraction, feature matching and transformation parameters estimation. This paper mainly explores the first two steps. In one of Chahira Serief’s paper about image registration, feature points extraction based on nonsubsampled contourlet transform (NSCT) was proposed and feature points matching based on Zernike moments was adopted. The registration accuracy and robustness of his algorithm are acceptable, but it can still be improved. In this paper, an improved scheme of this registration algorithm is proposed. The rotation invariance of NSCT-based feature points extraction is improved, which is beneficial to extract homologous feature points. And the reliability and effectiveness of Zernike momentsbased feature points matching are improved, which can improve the matching accuracy. The improved registration algorithm can realize registration of images related by larger scaling, rotation and translation transformation. The simulation results show that the registration robustness is further improved, and the registration accuracy is still high.

A Method for Spatial Data Registration Based on PCA-ICP Algorithm

The spatial data which acquired by 3D laser scanning is huge, aiming at the iteration time is long with classic ICP algorithm, a improved registration algorithm of spatial data ICP algorithm which based on principal component analysis (PCA) is proposed in this paper (PCA-ICP), the basic principle and steps of PCA-ICP algorithm are given. The experiment results show that this method is feasible and the iterative time of PCA-ICP algorithm is shorter than classical ICP algorithm.

Efficient registration of optical and infrared images via modified Sobel edging for plant canopy temperature estimation

Automatic optical and infrared (IR) image registration is a crucial requirement in the estimation of plant canopy temperature. The latter is used to compute the plant water stress status which can potentially be applied to an automated control system for optimal crop irrigation management. Due to the nature of image sources and plant scene, it is difficult to find enough common features between the pair of images to register them using a simple method. In this paper an improved registration algorithm is described, where a modified Sobel edge detector is adopted and a variable resolution scheme is applied. The algorithm can improve the registration efficiency considerably compared to our previous developed registration method. Experiment results show that the modified algorithm can achieve the same registration accuracy and a better success rate compared to that from the original algorithm with a substantial reduction of computational complexity.

Motion detection in moving background using a novel algorithm based on image features guiding self-adaptive Sequential Similarity Detection Algorithm

A novel efficient algorithm for motion detection in dynamic background was proposed. In image registration step, a feature-based and self-adaptive Sequential Similarity Detection Algorithm (SSDA) algorithm was proposed, which searches for matching position under constraints induced by image features with variational threshold. Then perform change detection by calculating and classifying the Mean Absolute Difference (MAD) around detected features in the middle frames of three consecutive images. Moving objects position was determined according to the rule that the feature from moving regions shows a lager MAD. Experiments on data sets of four typical scenes show that the improved registration algorithm is accurate and costs less than 0.4s in computation, much faster compared with other four methods, and the proposed Dual Maximum Mean Absolute Difference Algorithm (DMMADA) can obtain a robust set of moving object features. Our algorithm can be used for fast detection of moving targets in dynamic background as well as change detection.

SU‐DD‐A4‐02: Autosegmentation and Internal Target Volume (ITV) Generation in 4DCT Lung Imaging Using Deformable Image Registration

Purpose: To present a methodology using an improved intensity based deformable image registration algorithm (i.e. Juggler algorithm) for autosegmentation of normal anatomical structures, and algorithm generation of internal target volume (ITV). Method and Materials: The Juggler algorithm separately registers high intensity gradient and low intensity gradient features, and is particularly well suited for maintaining the topological properties of individual structures. Simulated CT imaging consisted of algorithm deformed clinical CT lung imaging. Clinical imaging, acquired from 4DCT, consisted of free breathing (FB) CT and 10 phased CT sets associated with the respiratory cycle. Manual image segmentation of normal structures was carried out on a reference CT (i.e. FB or end of exhalation phase), and the registration map between the reference and target CTs was used to autosegment the structures onto the target CT. This map was also used for automatic generation of ITV based either on “union target volume” (UTV) (i.e. the union of all the separate 3D segmentations), or “probability density function target volume” (PDFTV). Results: Based on known displacement vectors for simulated data, and difference imaging and cross‐correlation values for clinical data, Juggler yielded superior and faster registration compared to current deformable registration algorithms used in radiotherapy: demons, accelerated demons, free‐form deformation. Based on 4DCTs for 5 patients, normal anatomical structures (lung, skin, trachea, esophagus, heart) were automatically autosegmented onto target 3D CT sets within ⩽2mm accuracy based on visual agreement. The UTV computed ITV increased the FB GTV by up to 5mm. Total computation time was <3min (including deformable registration). Conclusions: The proposed methodology using an improved registration algorithm allowed for accurate autosegmentation of normal structures from initial manual segmentations, and the computation of ITV. It provides an effective treatment planning tool for 4DCT. New computer hardware could potentially reduce computation time to <30sec.