- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Superresolution Contour Reconstruction Approach to a Linear Thermal Expansion Measurement
摘要： Optical imaging delivers absolute, non-contact, and high-dynamic-range measurement of thermal expansion. However, to achieve high accuracy, various factors should be accounted within the image analysis, including: image spatial sampling, lens aberrations, brightness nonuniformity and object edge deformations. Approach based on the object contour reconstruction is presented. Measurement procedure consists of two stages. Firstly, object edge contours corresponding to different temperatures are estimated. This is done by the novel contour-retrieving image reconstruction, capable of optical and spatial sampling superresolution as well as compensation of brightness nonuniformity. Secondly, the reference retrieved contour is reconstructed to fit retrieved contours for other temperatures, considering a linear expansion model. With the nonlinear algorithms for contour retrieval and reconstruction, small sub-resolution random edge distortions are detected and filtered out, increasing the measurement accuracy. Second improvement of the proposed approach is an opportunity to validate the measurement, given by its fully reconstructive nature.
关键词： computational imaging,edge detection,active contour,deformation measurement,image reconstruction
更新于2025-09-09 09:28:46
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A novel 3D path extraction method for arc welding robot based on stereo structured light sensor
摘要： With the rapid development of computer vision and industrial technology, the requirements of the intelligent welding robots are increasing in the real industrial production. The traditional teaching-playback mode and the off-line programming mode cannot meet the automation demand and self-adaptive ability of welding robots. In order to improve the efficiency of welding robots, this paper proposes a novel three-dimensional(3D) path extraction method of weld seams based on stereo structured light sensor. Faced with the low efficiency of the line structured light and the poor robustness of passive vision, the seam extraction based on point cloud processing algorithm is proposed which could well adapt to the weld seams with different types and different groove sizes. Meanwhile, the position information and pose information of weld seam are established to serve the 3D path teaching of welding robot.The experimental results show that the maximum path extraction error of V-type butt joint is less than 0.7mm. The proposed scheme could well serve for the 3D path teaching task before welding.
关键词： path model,welding robot,3D reconstruction,seam extraction,path teaching,stereo structured light
更新于2025-09-09 09:28:46
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[Lecture Notes in Computer Science] Combinatorial Image Analysis Volume 11255 (19th International Workshop, IWCIA 2018, Porto, Portugal, November 22–24, 2018, Proceedings) || Improvement of Measurement Accuracy of Optical 3D Scanners by Discrete Systematic Error Estimation
摘要： A new methodology is introduced which enables the improvement of measurement accuracy of optical 3D scanners. This improvement is based on geometric compensation of the systematic measurement error over the measurement volume. Possible sources for systematic measurement errors are introduced and discussed. Estimation of the systematic error is performed by determination of length measurement error of a ballbar in different positions in the measurement volume. Description of the systematic error may be done using polynomials or sampling points in an equidistant volumetric grid. Simulations as well as experimental measurements using real data were performed in order to evaluate the new methodology. The results show that a reduction of the systematic error to about half of the original error is possible. The method is discussed, and potential improvements are given as prospective future work.
关键词： Computer vision,Image analysis,3D reconstruction,Optical 3D scanner
更新于2025-09-09 09:28:46
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Reconstructing granular particles from X-ray computed tomography using the TWS machine learning tool and the level set method
摘要： X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.
关键词： Machine learning,Shape reconstruction,3D particle morphology,X-ray computed tomography,Level set
更新于2025-09-09 09:28:46
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - SPECT Reconstruction and Analysis for the Inspection of Spent Nuclear Fuel
摘要： A gamma-emission-tomography (GET) system for the inspection of spent nuclear fuel (SNF) has been developed and tested on multiple fuel types. This tool can be used for verification of the integrity of an assembly and consistency with fissile-material content. Parallel-beam line integrals are measured by a discrete array of CdZnTe detectors that view the fuel through a 1.5mm wide by 100mm thick tungsten collimator. Detectors and electronics are on a rotating platform within a watertight stainless steel torus. During operation, the system is underwater and fuel is lowered through the center of the torus and held stationary as data are collected. Tomographic data collection requires a time on the order of minutes. In field experiments, data with count rates in the range of 50kcps to >500kcps per pixel have been recorded. In the reconstructed images, missing or replaced pins in all assembly types can be visually discriminated in the lattice of fuel pins. Automated detection of missing/replaced pins is the metric used for determination of optimal processing steps. Effectiveness of reconstruction and data-processing tools is measured by a tools ability to improve performance on the pin-discrimination task. This paper describes the data preprocessing, image reconstruction, image analysis, and performance evaluation of this system.
关键词： gamma-ray emission tomography,image quality metrics,safeguards,attenuation correction,image reconstruction,spent nuclear fuel
更新于2025-09-09 09:28:46
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Three-Dimensional Radiopharmaceutical-Excited Fluorescence Imaging of Lymph Nodes
摘要： Optical imaging techniques have been developed for localizing lymph nodes before surgical resection due to the non-invasion and high sensitivity. However, its attendant penetrability limitations and auto-fluorescence effect have greatly limited the spatial resolution and imaging precision. In this study, a novel technique radiopharmaceutical-excited fluorescence imaging (REFI) was adopted to image lymph nodes, which use gamma-ray and Cerenkov radiation from radioisotopes to excite lanthanide europium oxide (EO) nanophosphors and boost the light intensity. An effective adaptive-steepest-descent-projection onto convex sets (ASD-POCS) reconstruction algorithm and the anatomic structure information were used to three-dimensionally image lymph nodes in mice models. The results indicate that REFI can greatly boost the light intensity and accuracy of three-dimensional imaging of lymph node with location deviation less than 1.03 mm.
关键词： lymph nodes imaging,3D reconstruction,radiopharmaceutical-excited fluorescence imaging
更新于2025-09-09 09:28:46
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Spatially-variant Strength for Anatomical Priors in PET Reconstruction
摘要： This study explores the use of a spatially-variant penalty strength, proposed initially for quadratic penalties, in penalized image reconstruction using anatomical information. We have used the recently proposed Parallel Level Sets (PLS) anatomical prior as it has shown promising results in the literature. It was incorporated into the previously proposed preconditioned algorithm (L-BFGS-B-PC) for achieving both good image quality and fast convergence rate. A 2-dimensional (2D) disc phantom with a hot spot at the center and a 3D XCAT thorax phantom with lesions inserted in different slices are used to study how surrounding activity and lesion location affect both the visual appearance and quantitative consistency, respectively. Anatomical information is provided and assumed to be well-aligned with the corresponding activity images. For the XCAT phantom, the inserted lesions are either present or absent in the anatomical images to investigate the influence of the anatomical penalty. The reconstructed images for both phantoms with and without applying the spatially-variant penalty strength are compared. Preliminary results demonstrate that applying the spatially-variant penalization with an anatomical prior can reduce the dependence of local contrast on background activity and lesion location. Further work to explore the potential benefit in clinical imaging is warranted.
关键词： spatially-variant penalty strength,penalized image reconstruction,L-BFGS-B-PC,Parallel Level Sets,anatomical prior
更新于2025-09-09 09:28:46
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Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method
摘要： Anatomically-driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This work, consider the effect of spatial inconsistency between MR and PET images in hot and cold regions of the PET image. We investigate these effects on the kernel method from machine learning, in particular, the hybrid kernelized expectation maximization (HKEM). These were applied to Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the 'spill-in' of the affected cold regions and the 'spill-out' from the hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using KEM, against the 37% and 8% obtained with HKEM. The findings of this work suggest that including PET information in the kernel enhances the flexibility of the reconstruction in case of spatial inconsistency. Nevertheless, accurate registration and choice of the appropriate MR image for the creation of the kernel is essential to avoid artifacts, blurring, and bias.
关键词： hybrid kernel,image prior,expectation maximization (EM),kernel method,positron emission tomography (PET),iterative reconstruction,anatomically-driven
更新于2025-09-09 09:28:46
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Compressive Hyperspectral Imaging With Spatial and Spectral Priors
摘要： This paper proposes a new compressive hyperspectral imaging method, including the design of a cost-effective distributed sampling (DS) scheme and an efficient reconstruction model. The new sampling scheme, named as distributed separate sampling (DSS), encodes different hyperspectral bands with mutually independent two-dimensional separate sensing operators. Compared with existing DS schemes, DSS reduces lots of resource overhead in the premise of generating measurements with low redundancy. Furthermore, in contrast to the existing DS schemes, DSS keeps the original structure of hyperspectral images (HSIs) during sampling procedure. The new joint reconstruction model, namely, joint nuclear/total variation/L1 norm minimization, exploits both spatial and spectral priors of HSIs. Unlike the other joint reconstruction models, the proposed model utilize an L1 -based distance function to measure the similarity between adjacent bands, which improves the recovery quality of HSIs. Besides, we develop a new compressive inversion algorithm under the split Bregman framework, which is of low computational complexity, to solve our proposed reconstruction model. Comprehensive experimental results demonstrate the efficiency of our method.
关键词： Compressive hyperspectral imaging (CHI),split Bregman,joint reconstruction,distributed sampling (DS)
更新于2025-09-09 09:28:46
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European Microscopy Congress 2016: Proceedings || A new method for quantitative XEDS tomography of complex hetero-nanostructures
摘要： Over the last decades, electron tomography (ET) of complex materials has evolved into a powerful tool to investigate the three-dimensional (3D) structure of materials at the nanometer scale. The technique is based on the acquisition of a series of two-dimensional (2D) projection images of a sample, which are then reconstructed into a 3D volume using computational methods. Despite its success, ET faces several challenges, including the missing wedge problem, which limits the resolution and fidelity of the reconstructed volume. In this paper, we present a novel approach to mitigate the missing wedge problem by combining ET with machine learning (ML). Our method leverages the ability of ML to learn from data and predict missing information, thereby improving the quality of the reconstructed volume. We demonstrate the effectiveness of our approach on a series of synthetic and experimental datasets, showing significant improvements in resolution and fidelity compared to traditional reconstruction methods.
关键词： machine learning,nanomaterials,3D reconstruction,missing wedge,electron tomography
更新于2025-09-09 09:28:46