- 标题
- 摘要
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- 实验方案
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Fast Visual Odometry Based Sparse Geometric Constraint for RGB-D Camera
摘要： Pose estimation is a basic requirement for the autonomous behavior of robots. In this article we present a robust and fast visual odometry method to obtain camera poses by using RGB-D images. We first propose a motion estimation method based on sparse geometric constraint and derive the analytic Jacobian of the geometric cost function to improve the convergence performance, then we use our motion estimation method to replace the tracking thread in ORB-SLAM for improving its runtime performance. Experimental results show that our method is twice faster than ORB-SLAM while keeping the similar accuracy.
关键词： 3D reconstruction,pose estimation,geometric cost function,fast visual odometry,iterative optimization
更新于2025-09-19 17:15:36
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Spectral Adaptation Transform for Multispectral Constancy
摘要： The spectral reflectance of an object surface provides valuable information of its characteristics. Reflectance reconstruction from multispectral image data is typically based on certain assumptions. One of the common assumptions is that the same illumination is used for system calibration and image acquisition. The authors propose the concept of multispectral constancy which transforms the captured sensor data into an illuminant-independent representation, analogously to the concept of computational color constancy. They propose to transform the multispectral image data to a canonical representation through spectral adaptation transform (SAT). The performance of such a transform is tested on measured reflectance images. The authors also investigate the robustness of the transform to the inaccuracy of illuminant estimation in natural scenes. Results of reflectance reconstruction show that the proposed SAT is efficient and is robust to error in illuminant estimation.
关键词： multispectral imaging,multispectral constancy,spectral reconstruction,spectral adaptation transform,illuminant estimation
更新于2025-09-19 17:15:36
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Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications
摘要： Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and innovative instrument design that, when combined with LAIR, provides the image quality of a conventional CT system. This study describes the construction of different scan acquisition protocols for micro-CT system applications. Fully-sampled Feldkamp (FDK)-reconstructed images were used as references for comparison to assess the image quality produced by these tested protocols. The insufficient portions of a sinogram were inpainted by applying a context encoder (CE), a type of generative adversarial network, to the LAIR process. The context image was passed through an encoder to identify features that were connected to the decoder using a channel-wise fully-connected layer. Our results evidence the excellent performance of this novel approach. Even when we reduce the radiation dose by 1/4, the iterative-based LAIR improved the full-width half-maximum, contrast-to-noise and signal-to-noise ratios by 20% to 40% compared to a fully-sampled FDK-based reconstruction. Our data support that this CE-based sinogram completion method enhances the efficacy and efficiency of LAIR and that would allow feasibility of limited angle reconstruction.
关键词： generative adversarial network (GAN),context encoder (CE),limited-angle iterative reconstruction (LAIR)
更新于2025-09-19 17:15:36
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Generation of differential topographic images for surface inspection of long products
摘要： The current manufacturing industries need efficient quality control systems to ensure their products are free of defects. In most cases, surface inspection is carried out by automatic systems that process 2D images which lack measurable information such as the height or depth of the surface defects. An alternative technology for surface inspection is laser scanning. Using this technique, a 3D representation of a product can be generated and therefore, defects can be easily measured. This paper proposes a real-time algorithm to generate differential topographic images of the surface of a product using laser scanning. The images generated by the proposed method are a flattened representation of the surface of the product which compare it to a perfect-shaped product. In these images, the volumetric defects can be easily segmented and measured using computer vision techniques to fulfill the requirements of the international standards of quality. The proposed algorithm is tested on 500,000 profiles meeting the constraints of real time.
关键词： 3D reconstruction,Real-time imaging,Differential image,Laser scanning
更新于2025-09-19 17:15:36
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Reconstruction of PET Images Using Cross-Entropy and Field of Experts
摘要： The reconstruction of positron emission tomography data is a difficult task, particularly at low count rates because Poisson noise has a significant influence on the statistical uncertainty of positron emission tomography (PET) measurements. Prior information is frequently used to improve image quality. In this paper, we propose the use of a field of experts to model a priori structure and capture anatomical spatial dependencies of the PET images to address the problems of noise and low count data, which make the reconstruction of the image difficult. We reconstruct PET images by using a modified MXE algorithm, which minimizes a objective function with the cross-entropy as a fidelity term, while the field of expert model is incorporated as a regularizing term. Comparisons with the expectation maximization algorithm and a iterative method with a prior penalizing relative differences showed that the proposed method can lead to accurate estimation of the image, especially with acquisitions at low count rate.
关键词： field of experts,reconstruction,positron emission tomography
更新于2025-09-19 17:15:36
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[Lecture Notes in Computer Science] Computer Vision – ECCV 2018 Workshops Volume 11133 (Munich, Germany, September 8-14, 2018, Proceedings, Part V) || Multi-modal Spectral Image Super-Resolution
摘要： Recent advances have shown the great power of deep convolutional neural networks (CNN) to learn the relationship between low and high-resolution image patches. However, these methods only take a single-scale image as input and require large amount of data to train without the risk of overfitting. In this paper, we tackle the problem of multi-modal spectral image super-resolution while constraining ourselves to a small dataset. We propose the use of different modalities to improve the performance of neural networks on the spectral super-resolution problem. First, we use multiple downscaled versions of the same image to infer a better high-resolution image for training, we refer to these inputs as a multi-scale modality. Furthermore, color images are usually taken at a higher resolution than spectral images, so we make use of color images as another modality to improve the super-resolution network. By combining both modalities, we build a pipeline that learns to super-resolve using multi-scale spectral inputs guided by a color image. Finally, we validate our method and show that it is economic in terms of parameters and computation time, while still producing state-of-the-art results (Code at https://github.com/IVRL/Multi-Modal-Spectral-Image-Super-Resolution).
关键词： Image completion,Spectral reconstruction,Spectral image super-resolution,Multi-modality,Residual learning
更新于2025-09-19 17:15:36
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Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance
摘要： Objective Evaluate and compare the image quality and acceptance of a full MBIR algorithm to that of an earlier full IR hybrid algorithm and filtered back projection (FBP). Methods Acquisitions were performed with a 320 detector-row CT scanner with seven different dose levels. Images were reconstructed with three algorithms: FBP, full hybrid iterative reconstruction (HIR), and a full model-based iterative reconstruction algorithm (full MBIR). The sensitometry, spatial resolution, image texture, and low-contrast detectability of these algorithms were compared. Subjective analysis of low-contrast detectability was performed. Ten radiologists answered a questionnaire on image quality and confidence in full MBIR images in clinical practice. Results The contrast-to-noise ratio of full MBIR was significantly higher than in the other algorithms (p < 0.0015). The spatial resolution was also higher with full MBIR at high frequencies (> 0.3 lp/mm). Full MBIR at low dose levels led to better low-contrast detectability and more inserts being identified with a higher confidence (p < 0.0001). Full MBIR was associated with a change in image texture compared to HIR and FBP. Eighty percent of radiologists judged general appearance and texture of full MBIR images worse than HIR. Moreover, compared with HIR, for 50% of radiologists, the diagnostic confidence on full MBIR images was worse. Questionnaire reliability was considered acceptable (Cronbach alpha 0.7). Conclusion Compared to conventional iterative reconstruction algorithms, full MBMIR presented a higher image quality and low-contrast detectability and a worse acceptance among radiologists.
关键词： Computed tomography,Phantom imaging,Image quality,Abdomen,Image reconstruction
更新于2025-09-19 17:15:36
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Unsupervised texture reconstruction method using bidirectional similarity function for 3-D measurements
摘要： The developments of three-dimensional (3-D) imaging and display technology have created important demands for 3-D model with realistic texture. Due to texture misalignment and inconsistent illumination, texture reconstruction is subject to degradation like blurring, ghosting and color discontinuity. A composite weight texture blending approach is proposed to mitigate the artifacts. For inaccurate geometric model, bidirectional similarity function based multi-scale energy minimization method is proposed to correct texture misalignment. The average processing time of the proposed method is only 40% of existing method. The optical experiments demonstrate that photorealistic texture could be achieved for inaccurate geometric model and inconsistent illumination.
关键词： 3-D measurement,Structured light,Bidirectional similarity function,Texture reconstruction,Pinhole camera model
更新于2025-09-19 17:15:36
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FPGA Implementation of ECT Digital System for Imaging Conductive Materials
摘要： This paper presents the hardware implementation of a stand-alone Electrical Capacitance Tomography (ECT) system employing a Field Programmable Gate Array (FPGA). The image reconstruction algorithms of the ECT system demand intensive computation and fast processing of large number of measurements. The inner product of large vectors is the core of the majority of these algorithms. Therefore, a reconfigurable segmented parallel inner product architecture for the parallel matrix multiplication is proposed. In addition, hardware-software codesign targeting FPGA System-On-Chip (SoC) is applied to achieve high performance. The development of the hardware-software codesign is carried out via commercial tools to adjust the software algorithms and parameters of the system. The ECT system is used in this work to monitor the characteristic of the molten metal in the Lost Foam Casting (LFC) process. The hardware system consists of capacitive sensors, wireless nodes and FPGA module. The experimental results reveal high stability and accuracy when building the ECT system based on the FPGA architecture. The proposed system achieves high performance in terms of speed and small design density.
关键词： electrical tomography,image reconstruction algorithm,capacitance measurements,FPGA,LFC
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Spatially Resolved Material Quality Prediction Via Constrained Deep Learning
摘要： Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ～2 mm pixels provided higher detection performance than those with ～4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.
关键词： PET/CT reconstruction,PET/CT,image reconstruction,Image quality assessment
更新于2025-09-19 17:13:59