3D Matching Research Articles

3D graphene-Ni microspheres with excellent microwave absorption and corrosion resistance properties

In this study, we report a simple and efficient two-step method consisting of water-in-oil (W/O) emulsion technique and subsequent annealing process for synthesizing the hollow reduced graphene oxide microspheres embedded with Ni nanoparticles (Air@rGO€Ni). The microspheres showed good electromagnetic properties because of the coexistence of magnetic loss and dielectric loss to microwaves. The minimum reflection loss (RLmin) value of Air@rGO€Ni microsphere reaches up to − 59.7 dB at 11.6 GHz with a thickness of 2.7 mm and an absorption bandwidth (lower than − 10 dB) is 4.8 GHz (9.4–14.2 GHz). Their resistance to chemical corrosion is amazingly, after treatment in hydrochloric acid for 5 days, the saturation magnetization (Ms) values of Air@rGO€Ni microsphere has fallen only six percentage points. More interestingly, we can easily controll the microwave absorbing properties of the microspheres by changing the ratio of the two components in the composites. The unique 3D structure and excellent electromagnetic match at the corresponding resonance peaks for dielectric and magnetic loss play an important role in improving microwave absorption property. Our study provides a good potential method for preparation of lightweight microwave absorbing materials.

A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system

Background: Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty.Methods: A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration.Results: Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18–26 mm) and different support radii (2–6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm.Conclusion: This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

Improved BM3D denoising method

Block matching 3D denoising (BM3D) is an excellent single-image denoising method. However, it still needs to be improved for solving practical problems. In this study, the authors attempt to improve the method of BM3D. First, one of the problems of BM3D is that some of its references cannot perform self-adaption when the noise intensity of the images is changed. Therefore, they propose a method using total variation (TV) to calculate the image noise intensity and make the references perform self-adaption. Second, finding similar blocks in the BM3D method is a time-consuming procedure. To solve this problem, they analyse the relationship between the numbers of similar blocks and denoising effect, improve the process of searching for similar blocks, and reduce the running time. Third, through the experiment they find that the denoising effect of BM3D method in the domain of complex texture is unsatisfactory. Thus, they proposed a hybrid denoising method for the complex texture area, using the new TV model and BM3D method together to restore the image. Their experimental results show that the improved BM3D method performs better than the original BM3D method.

Dual-eyes Vision-based Docking System for Autonomous Underwater Vehicle: an Approach and Experiments

A critical challenge for autonomous underwater vehicles (AUVs) is the docking operation for applications such as sleeping under the mother ship, recharging batteries, transferring data, and new mission downloading. The final stage of docking at a unidirectional docking station requires the AUV to approach while keeping the pose (position and orientation) of the vehicle within an allowable range. The appropriate pose therefore demands a sensor unit and a control system that have high accuracy and robustness against disturbances existing in a real-world underwater environment. This paper presents a vision-based AUV docking system consisting of a 3D model-based matching method and Real-time Multi-step Genetic Algorithm (GA) for real-time estimation of the robot’s relative pose. Experiments using a remotely operated vehicle (ROV) with dual-eye cameras and a separate 3D marker were conducted in a small indoor pool. The experimental results confirmed that the proposed system is able to provide high homing accuracy and robustness against disturbances that influence not only the captured camera images but also the movement of the vehicle. A successful docking operation using stereo vision that is new and novel to the underwater vehicle environment was achieved and thus proved the effectiveness of the proposed system for AUV.

Cell Population Tracking in a Honeycomb Structure Using an IMM Filter Based 3D Local Graph Matching Model.

Developing algorithms for plant cell population tracking is very critical for the modeling of plant cell growth pattern and gene expression dynamics. The tracking of plant cells in microscopic image stacks is very challenging for several reasons: (1) plant cells are densely packed in a specific honeycomb structure; (2) they are frequently dividing; and (3) they are imaged in different layers within 3D image stacks. Based on an existing 2D local graph matching algorithm, this paper focuses on building a 3D plant cell matching model, by exploiting the cells' 3D spatiotemporal context. Furthermore, the Interacting Multi-Model filter (IMM) is combined with the 3D local graph matching model to track the plant cell population simultaneously. Because our tracking algorithm does not require the identification of "tracking seeds", the tracking stability and efficiency are greatly enhanced. Last, the plant cell lineages are achieved by associating the cell tracklets, using a maximum-a-posteriori (MAP) method. Compared with the 2D matching method, the experimental results on multiple datasets show that our proposed approach does not only greatly improve the tracking accuracy by 18 percent, but also successfully tracks the plant cells located at the high curvature primordial region, which is not addressed in previous work.

Using 3D Matching for Picking and Placing on UR Robot

Due to conventional industrial robot moving has been known that programing is either tedious or simplex and operating platform requires a pure environment without unnecessary distractions from other objects. This paper presents a novel method which using shape based 3Dmatching technology for picking and placing object by UR robot. This method is able to help the robot catch a specified object in any complex environment. To do this, we need only one camera and the targets' 3D CAD model. There are many efforts was indeed carried out in order to improve the control accuracy, first , the 3D coordinates of the interested points should be high-resolution calculated with the images that areprovided by the camera and we use a special method to optimize internal and external camera parameters when calibrating the camera, Then, A 3D-matching-operator is used to search for the specific objects in the real-time images based on the given 3D model, which is realized by projects the edges of the 3D object model that was used to create the 3D shape model into the image coordinate system and return projected edges. Finally, the coordinates of the 3D object model is obtained in the PTU coordinate system through the integration of a PTU and a Laser range finder , and a transformational matrix is obtained for calculate the coordinates of the targets on UR robot’s base coordinate system. Based on this technology, users only simply need to import a 3D CAD model and click on the image of the workplace to define the end point, the robot will exceed the procedure of pick and place automatically.

Bone comparison identification method based on chest computed tomography imaging.

The aim of this study is to examine the usefulness of bone structure extracted data from chest computed tomography (CT) images for personal identification. Eighteen autopsied cases (12 male and 6 female) that had ante- and post-mortem (AM and PM) CT images were used in this study. The two-dimensional (2D) and three-dimensional (3D) bone images were extracted from the chest CT images via thresholding technique. The similarity between two thoracic bone images (consisting of vertebrae, ribs, and sternum) acquired from AMCT and PMCT images was calculated in terms of the normalized cross-correlation value (NCCV) in both 2D and 3D matchings. An AM case with the highest NCCV corresponding to a given PM case among all of the AM cases studied was regarded as same person. The accuracy of identification of the same person using our method was 100% (18/18) in both 2D and 3D matchings. The NCCVs for the same person tended to be significantly higher than the average of NCCVs for different people in both 2D and 3D matchings. The computation times of image similarity between the two images were less than one second and approximately 10min in 2D and 3D matching, respectively. Therefore, 2D matching especially for thoracic bones seems more advantageous than 3D matching with regard to computation time. We conclude that our proposed personal identification method using bone structure would be useful in forensic cases.

Reliability of visual and instrumental color matching.

The aim of this investigation was to evaluate intra-rater and inter-rater reliability of visual and instrumental shade matching. Forty individuals with normal color perception participated in this study. The right maxillary central incisor of a teaching model was prepared and restored with 10 feldspathic all-ceramic crowns of different shades. A shade matching session consisted of the observer (rater) visually selecting the best match by using VITA classical A1-D4 (VC) and VITA Toothguide 3D Master (3D) shade guides and the VITA Easyshade Advance intraoral spectrophotometer (ES) to obtain both VC and 3D matches. Three shade matching sessions were held with 4 to 6 weeks between sessions. Intra-rater reliability was assessed based on the percentage of agreement for the three sessions for the same observer, whereas the inter-rater reliability was calculated as mean percentage of agreement between different observers. The Fleiss' Kappa statistical analysis was used to evaluate visual inter-rater reliability. The mean intra-rater reliability for the visual shade selection was 64(11) for VC and 48(10) for 3D. The corresponding ES values were 96(4) for both VC and 3D. The percentages of observers who matched the same shade with VC and 3D were 55(10) and 43(12), respectively, while corresponding ES values were 88(8) for VC and 92(4) for 3D. The results for visual shade matching exhibited a high to moderate level of inconsistency for both intra-rater and inter-rater comparisons. The VITA Easyshade Advance intraoral spectrophotometer exhibited significantly better reliability compared with visual shade selection. This study evaluates the ability of observers to consistently match the same shade visually and with a dental spectrophotometer in different sessions. The intra-rater and inter-rater reliability (agreement of repeated shade matching) of visual and instrumental tooth color matching strongly suggest the use of color matching instruments as a supplementary tool in everyday dental practice to enhance the esthetic outcome.

Synthesizing Obama

Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track. Our approach produces photorealistic results.

Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images.

Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.

Sparse approximations in complex domain based on BM3D modeling

In this paper the concept of sparsity for complex-valued variables is introduced in the following three types: directly in complex domain and for two real-valued pairs phase/amplitude and real/imaginary parts of complex variables. The nonlocal block-matching technique is used for sparsity implementation and filter design for each type of sparsity. These filters are complex domain generalizations of the Block Matching 3D collaborative (BM3D) filter based on the high-order singular value decomposition (HOSVD) in order to generate group-wise adaptive analysis/synthesis transforms. Complex domain denoising is developed and studied as a test-problem for comparison of the designed filters as well as the different types of sparsity modeling.

Accelerated parabolic Radon domain 2D adaptive multiple subtraction with fast iterative shrinkage thresholding algorithm and its application in parabolic Radon domain hybrid demultiple method

Adaptive multiple subtraction is an important step for successfully conducting surface-related multiple elimination in marine seismic exploration. 2D adaptive multiple subtraction conducted in the parabolic Radon domain has been proposed to better separate primaries and multiples than 2D adaptive multiple subtraction conducted in the time-offset domain. Additionally, the parabolic Radon domain hybrid demultiple method combining parabolic Radon filtering and parabolic Radon domain 2D adaptive multiple subtraction can better remove multiples than the cascaded demultiple method using time-offset domain 2D adaptive multiple subtraction and the parabolic Radon transform method sequentially. To solve the matching filter in the optimization problem with L1 norm minimization constraint of primaries, traditional parabolic Radon domain 2D adaptive multiple subtraction uses the iterative reweighted least squares (IRLS) algorithm, which is computationally expensive for solving a weighted LS inversion in each iteration. In this paper we introduce the fast iterative shrinkage thresholding algorithm (FISTA) as a faster alternative to the IRLS algorithm for parabolic Radon domain 2D adaptive multiple subtraction. FISTA uses the shrinkage-thresholding operator to promote the sparsity of estimated primaries and solves the 2D matching filter with iterative steps. FISTA based parabolic Radon domain 2D adaptive multiple subtraction reduces the computation time effectively while achieving similar accuracy compared with IRLS based parabolic Radon domain 2D adaptive multiple subtraction. Additionally, the provided examples show that FISTA based parabolic Radon domain 2D adaptive multiple subtraction can better separate primaries and multiples than FISTA based time-offset domain 2D adaptive multiple subtraction. Furthermore, we introduce FISTA based parabolic Radon domain 2D adaptive multiple subtraction into the parabolic Radon domain hybrid demultiple method to improve its computation efficiency while maintaining its accuracy. Synthetic and field data examples demonstrate the effectiveness of the proposed FISTA based parabolic Radon domain 2D adaptive multiple subtraction and its application in the parabolic Radon domain hybrid demultiple method.

Verifying tumor position during stereotactic body radiation therapy delivery using (limited-arc) cone beam computed tomography imaging

Background and purposeProof of tumor position during stereotactic body radiotherapy (SBRT) delivery is desirable. We investigated if cone-beam CT (CBCT) scans reconstructed from (collimated) fluoroscopic kV images acquired during irradiation could show the dominant tumor position. Materials and methodsFull-arc CBCT scans were reconstructed using FDK filtered back projection from 38kV fluoroscopy datasets (16 patients) continuously acquired during volumetric modulated spine SBRT. CBCT-CT match values were compared to the average spine offset values found using template matching+triangulation of the individual kV images. Multiple limited-arc CBCTs were reconstructed from fluoroscopic images acquired during lung SBRT of an anthropomorphic thorax phantom using 20–180° arc lengths and for 3 breath-hold lung SBRT patients. ResultsDifferences between 3D CBCT-CT match results and average spine offsets found using template matching+triangulation were 0.1±0.1mm for all directions (range: 0.0–0.5mm). For limited-arc CBCTs of the thorax phantom, the automatic 3D CBCT-CT match results for arc lengths of 80–180° were ≤1mm. 20° CBCT reconstruction still allowed for positional verification in 2D. Conclusions(Limited-arc) CBCT reconstructions of kV images acquired during irradiation can identify the dominant position of the tumor during treatment delivery.

In silico analysis of Nattokinase from Bacillus subtilis sp natto

Nattokinase or subtilisin NAT (EC 3.4.21.62) is one of the most remarkable enzymes produced by Bacillus subtilis sp. Natto, which posses direct fibrinolytic activity. The aim of this study is in silico analysis of Nattokinase structure and function. The three-dimensional structure of serine protease Nattokinase from Bacillus subtilis sp. natto was determined using homology modeling performed by Geno3D2 Web Server and refined by ModRefiner. The obtained models were validated via programs such as RAMPAGE, ERRAT, 3D Match and verify 3D for consistency; moreover, functional analysis performed by PFP from Kihara Bioinformatics laboratory. RAMPAGE analysis showed that 96.7% of the residues are located in the favored region, 3.0% in allowed region and 0.4% in outlier region of the Ramachandran plot. The verify 3D value of 0.73 indicates that the environmental sketch of the model is fine. SOPMA and PSIPRED were exploited for computation of the secondary structural properties of serine protease Nattokinase. Active site determination via AADS suggested that this enzyme can be applied as a potent enzyme for cardiovascular therapy. However, these results should be more confirmed by wet lab researches for designing the more active enzyme for better functions on its fibrinolysis activity.

Principal Curvature Measures Estimation and Application to 3D Face Recognition

This paper presents an effective 3D face keypoint detection, description and matching framework based on three principle curvature measures. These measures give a unified definition of principle curvatures for both smooth and discrete surfaces. They can be reasonably computed based on the normal cycle theory and the geometric measure theory. The strong theoretical basis of these measures provides us a solid discrete estimation method on real 3D face scans represented as triangle meshes. Based on these estimated measures, the proposed method can automatically detect a set of sparse and discriminating 3D facial feature points. The local facial shape around each 3D feature point is comprehensively described by histograms of these principal curvature measures. To guarantee the pose invariance of these descriptors, three principle curvature vectors of these principle curvature measures are employed to assign the canonical directions. Similarity comparison between faces is accomplished by matching all these curvature-based local shape descriptors using the sparse representation-based reconstruction method. The proposed method was evaluated on three public databases, i.e. FRGC v2.0, Bosphorus, and Gavab. Experimental results demonstrated that the three principle curvature measures contain strong complementarity for 3D facial shape description, and their fusion can largely improve the recognition performance. Our approach achieves rank-one recognition rates of 99.6, 95.7, and 97.9% on the neutral subset, expression subset, and the whole FRGC v2.0 databases, respectively. This indicates that our method is robust to moderate facial expression variations. Moreover, it also achieves very competitive performance on the pose subset (over 98.6% except Yaw 90°) and the occlusion subset (98.4%) of the Bosphorus database. Even in the case of extreme pose variations like profiles, it also significantly outperforms the state-of-the-art approaches with a recognition rate of 57.1%. The experiments carried out on the Gavab databases further demonstrate the robustness of our method to varies head pose variations.

SigVox – A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree.

Autofluorescence imaging captures heterogeneous drug response differences between 2D and 3D breast cancer cultures.

Two-photon microscopy of cellular autofluorescence intensity and lifetime (optical metabolic imaging, or OMI) is a promising tool for preclinical drug development. OMI, which exploits the endogenous fluorescence from the metabolic coenzymes NAD(P)H and FAD, is sensitive to changes in cell metabolism produced by drug treatment. Previous studies have shown that drug response, genetic expression, cell-cell communication, and cell signaling in 3D culture match those of the original in vivo tumor, but not those of 2D culture. The goal of this study is to use OMI to quantify dynamic cell-level metabolic differences in drug response in 2D cell lines vs. 3D organoids generated from xenograft tumors of the same cell origin. BT474 cells and Herceptin-resistant BT474 (HR6) cells were tested. Cells were treated with vehicle control, Herceptin, XL147 (PI3K inhibitor), and the combination. The OMI index was used to quantify response, and is a linear combination of the redox ratio (intensity of NAD(P)H divided by FAD), mean NADH lifetime, and mean FAD lifetime. The results confirm that the OMI index resolves significant differences (p<0.05) in drug response for 2D vs. 3D cultures, specifically for BT474 cells 24 hours after Herceptin treatment, for HR6 cells 24 and 72 hours after combination treatment, and for HR6 cells 72 hours after XL147 treatment. Cell-level analysis of the OMI index also reveals differences in the number of cell sub-populations in 2D vs. 3D culture at 24, 48, and 72 hours post-treatment in control and treated groups. Finally, significant increases (p<0.05) in the mean lifetime of NADH and FAD were measured in 2D vs. 3D for both cell lines at 72 hours post-treatment in control and all treatment groups. These whole-population differences in the mean NADH and FAD lifetimes are supported by differences in the number of cell sub-populations in 2D vs. 3D. Overall, these studies confirm that OMI is sensitive to differences in drug response in 2D vs. 3D, and provides further information on dynamic changes in the relative abundance of metabolic cell sub-populations that contribute to this difference.

COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT

The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equalization (CLAHE), Histogram Equalization with 3D Block Matching (HE+BM3D), and basic image enhancement, unsharp masking. We examine and discuss the best method for enhancing chromosome image. Therefore, to evaluate the methods, the original image was manipulated by the addition of some noise and blur. Peak Signal-to-noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to examine method performance. The output of enhancement method will be compared with result of Professional software for karyotyping analysis named Ikaros MetasystemT M . Based on experimental results, HE+BM3D method gets a stable result on both scenario noised and blur image.

Think locally, fit globally: Robust and fast 3D shape matching via adaptive algebraic fitting

In this paper, we propose a novel 3D free form surface matching method based on a novel key-point detector and a novel feature descriptor. The proposed detector is based on algebraic surface fitting. By global smooth fitting, our detector achieved high computational efficiency and robustness against non-rigid deformations. For the feature descriptor, we provide algorithms to compute 3D critical net which generates a meaningful structure on standalone local key-points. The scale invariant and deformation robust Dual Spin Image descriptor is provided based on the 3D critical net. Our method is proved by solid mathematics. Intensive quantitative experiments demonstrate the robustness, efficiency and accuracy of the proposed method.

Determining 3D scapular orientation with scapula models and biplane 2D images

This study evaluated a strategy for identifying 3D scapulothoracic orientation using bilateral X-ray scans and 3D scapula models. Both subject-specific scapula models and a scaled general model were utilized. 3D scapulothoracic orientations obtained from X-rays were compared to motion capture data. “Subjects” consisted of a skeletal model of a human torso and ten real bone scapulae. Retroreflective markers were placed on the scapulae and a three-marker triad was placed on the trunk. Marker positions were recorded using an eight camera motion capture system. A biplane X-ray system from EOS Imaging was used to collect two orthogonal 2D images of the skeleton and markers. Custom software was created for the 3D to 2D matching process. The results indicated that the matched orientations compared favorably to motion capture orientations, with RMSE errors ranging from 3.1° to 5.5° and a mean error of 3.9° The proposed strategy was shown to be accurate for both subject-specific models and a scaled general model.