The Experts below are selected from a list of 92013 Experts worldwide ranked by ideXlab platform
Chulhee Lee - One of the best experts on this subject based on the ideXlab platform.
-
ICIAR (1) - Fast Video Registration Method for Video Quality Assessment
Lecture Notes in Computer Science, 2004Co-Authors: Jihwan Choe, Chulhee LeeAbstract:In this paper, we propose a block-based video Registration Method that can be used for various applications such as objective video quality assessment. Instead of using the entire frames of a reference video sequence and a processed video sequence, we use a limited number of frames which are selected under a certain criterion and the selected frames are divided into a number of sub-blocks. Then, we select a small number of sub-blocks which have large spatial gradients. In order to find such sub-blocks, a spatial filtering is applied to the sub-blocks of the selected frames and we use energy of filtered sub-blocks to measure spatial gradient. The proposed Registration Method is fast and experimental results show that the Method provides accurate Registrations for a wide range of videos.
Cheng Wang - One of the best experts on this subject based on the ideXlab platform.
-
A line segment based Registration Method for Terrestrial Laser Scanning point cloud data
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 2016Co-Authors: Jun Cheng, Ming Cheng, Yangbin Lin, Cheng WangAbstract:This paper proposed a 3d line segment based Registration Method for terrestrial laser scanning (TLS) data. The 3D line segment is adopted to describe the point cloud data and reduce geometric complexity. After that, we introduce a framework for Registration. We demonstrate the accuracy of our Method for rigid transformations in the presence of terrestrial laser scanning point cloud.
-
A Structure-Based Registration Method for Terrestrial Laser Scanning Data
Applied Mechanics and Materials, 2015Co-Authors: Jun Cheng, Ming Cheng, Yan Bin Lin, Cheng WangAbstract:This paper presents a novel structure-based Registration Method for terrestrial laser scanning (TLS) data. The line support region (LSR), which fits the 3D line segment, is adopted to describe the scene structure and reduce geometric complexity. Then we employ an evolution computation Method to solve the optimization problem of global Registration. Our Method can be further enhanced by iterative closest points (ICP) or other local Registration Methods. We demonstrate the robustness of our algorithm on several point cloud sets with varying extent of overlap and degree of noise.
Xufeng Yao - One of the best experts on this subject based on the ideXlab platform.
-
A robust automated surface-matching Registration Method for neuronavigation.
Medical physics, 2020Co-Authors: Yifeng Fan, Xufeng YaoAbstract:PURPOSE The surface-matching Registration Method in the current neuronavigation completes the coarse Registration mainly by manually selecting anatomical landmarks, which increases the Registration time, makes the automatic Registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial Registration Method for the patient-to-image Registration. MethodS A coarse-to-fine spatial Registration Method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three-dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4-points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine-registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed Method. RESULTS Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target Registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 ± 0.04 mm, and the average Registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient's head surface were 1.70 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average Registration time was 21.4 s. Compared with the Method only based on the 4PCS and ICP algorithm and the current clinical Method, the proposed Method has obvious speed advantage while ensuring the Registration accuracy. CONCLUSIONS The proposed Method greatly improves the Registration speed while guaranteeing the equivalent or higher Registration accuracy, and avoids a tedious manual process for the coarse Registration.
-
An Automatic Spatial Registration Method for Image-Guided Neurosurgery System.
The Journal of craniofacial surgery, 2019Co-Authors: Yifeng Fan, Xufeng YaoAbstract:OBJECTIVE This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial Registration Method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS). MethodS A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment. A variant of the ICP algorithm was then used to finish the fine Registration. Two phantoms and 3 patients' experiments were performed to demonstrate the effectiveness of the proposed Method. RESULTS In the phantom experiments, the mean target Registration error of 15 targets on the surface of the rigid and the elastic phantoms were 1.02 ± 0.18 mm and 1.27 ± 0.36 mm, respectively. In the clinical experiments, the mean target Registration error of 7 targets on the first, second and third patient's head were 1.88 ± 0.19 mm, 1.84 ± 0.19 mm, and 1.89 ± 0.18 mm, respectively, which was sufficient to meet clinical requirements. The Registration accuracy and Registration time using the proposed Method are better than that using the Method based on manually coarse Registration and automatic fine Registration. CONCLUSIONS It is feasible to use the automatic spatial Registration Method based on the 4PCS and ICP algorithm for the IGNS. Moreover, it can replace the spatial Registration Method based on manually selected anatomical landmarks combined with the automatic fine Registration in the currently used IGNS.
Yang Yong-qin - One of the best experts on this subject based on the ideXlab platform.
-
Subpixel Registration Method based on wavelet analysis
Journal of Computer Applications, 2009Co-Authors: Yang Yong-qinAbstract:Image Registration is an important task in the field of computer vision and pattern recognition,and is applied in remote sensing,medical imaging and object indentifying of multi-sensor fusion.In this paper,a new subpixel Registration Method based on wavelet analysis was proposed by improving polynomial subdivision algorithm and pixel level Registration.Furthermore,the precision of the Method was also analyzed.The simulation results show that the Methods can reach the precision of sub-pixel.
Jihwan Choe - One of the best experts on this subject based on the ideXlab platform.
-
ICIAR (1) - Fast Video Registration Method for Video Quality Assessment
Lecture Notes in Computer Science, 2004Co-Authors: Jihwan Choe, Chulhee LeeAbstract:In this paper, we propose a block-based video Registration Method that can be used for various applications such as objective video quality assessment. Instead of using the entire frames of a reference video sequence and a processed video sequence, we use a limited number of frames which are selected under a certain criterion and the selected frames are divided into a number of sub-blocks. Then, we select a small number of sub-blocks which have large spatial gradients. In order to find such sub-blocks, a spatial filtering is applied to the sub-blocks of the selected frames and we use energy of filtered sub-blocks to measure spatial gradient. The proposed Registration Method is fast and experimental results show that the Method provides accurate Registrations for a wide range of videos.
