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[ACM Press SIGGRAPH Asia 2017 Posters - Bangkok, Thailand (2017.11.27-2017.11.30)] SIGGRAPH Asia 2017 Posters on - SA '17 - 4D computed tomography measurement for growing plant animation
摘要： Detailed observation of plant growth is essential for botanical analysis and realistic animation design. This study introduces spatial-temporal measurement techniques for a growing plant using X-ray Computed Tomography (CT). We scanned a target plant using CT over the course of couple of days with fixed time intervals to obtain four-dimensional (4D) volumetric images. We present a technique to segment the obtained 4D-CT images semi-automatically. We provide a 4D-CT measurement of budding bean sprouts to illustrate the feasibility of it.
关键词： Plant Animation,4D Measurement,Segmentation,X-ray CT
更新于2025-09-23 15:22:29
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Performance of the sinogram-based iterative reconstruction in sparse view X-ray computed tomography
摘要： Performing X-ray computed tomography (CT) examinations with less radiation has recently received increasing interest: in medical imaging this means less (potentially harmful) radiation for the patient; in non-destructive testing of materials/objects such as testing jet engines, the reduction of the number of projection angles (which for large objects is in general high) leads to a substantial decreasing of the experiment time. In the experiment, less radiation is usually achieved by either (1) reducing the radiation dose used at each projection angle or (2) using sparse view X-ray CT, which means significantly less projection angles are used during the examination. In this work, we study the performance of the recently proposed sinogram-based iterative reconstruction algorithm in sparse view X-ray CT and show that it provides, in some cases, reconstruction accuracy better than that obtained by some of the Total Variation regularization techniques. The provided accuracy is obtained with computation times comparable to other techniques. An important feature of the sinogram-based iterative reconstruction algorithm is that it is simpler and without the many parameters specific to other techniques.
关键词： iterative reconstruction,Dose reduction,sparse view X-ray CT
更新于2025-09-10 09:29:36
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Recognition of incorrect assembly of internal components by X-ray CT and deep learning
摘要： It is important to make sure that all components of a complex product are assembled correctly. Because in many cases, some components are enclosed in an opaque shell, x-ray imaging is currently used to extract their characteristics and match prior-known ones. However, x-ray imaging is not very robust in recognition of incorrect assembly of internal components, because some of them may overlap. To solve this problem, we propose a new method to detect internal component assembly fault, by x-ray computed tomography (CT) and convolutional neural network (CNN). Multi-view imaging is implemented by mechanical rotation of a product in respect with an x-ray CT machine to capture multiple projection information on each internal component, and then the component can be recognized by making use of deep learning. A CNN model is trained to classify the internal components and give the coordinates of each component. Based on the CNN recognition results and the CT projection sinogram, a projection corresponding to a reference in a projection data set of a standard product can be found. By comparing and matching the locations of each component, transposition or dislocation can be recognized. Both simulation and experiment show that this new method can effectively identify incorrect assembly, missing assembly, transposition, and other problems, improving the product quality.
关键词： Projection sinogram,Assembly recognition,Convolution neural network (CNN),x-ray CT
更新于2025-09-04 15:30:14