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Machine Learning Based Solutions Machine learning enables learning the correspondance between the subtle features in the input and the respective 3D equivalent. Deep neural networks have shown to be highly effective for 3D reconstruction from a single color image. [15] This works even for non-photorealistic input images such as sketches.
3D reconstruction/pose estimation 2020 [187] B. Biggs et al. The Oxford-IIIT Pet Dataset 37 categories of pets with roughly 200 images of each. Breed labeled, tight bounding box, foreground-background segmentation. ~ 7,400 Images, text Classification, object detection 2012 [186] [188] O. Parkhi et al. Corel Image Features Data Set
Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Artificial Intelligence Markup Language (AIML) [11] is an XML dialect [12] for use with Artificial Linguistic Internet Computer Entity (A.L.I.C.E.)-type chatterbots. Planner is a hybrid between procedural and logical languages. It gives a ...
In computer vision and computer graphics, the 3D Morphable Model (3DMM) is a generative technique that uses methods of statistical shape analysis to model 3D objects. The model follows an analysis-by-synthesis approach over a dataset of 3D example shapes of a single class of objects (e.g., face, hand).
This method uses X-ray images for 3D Reconstruction and to develop 3D models with low dose radiations in weight bearing positions. In NSCC algorithm, the preliminary step is calculation of an initial solution. Firstly anatomical regions from the generic object are defined. Secondly, manual 2D contours identification on the radiographs is performed.
In learned iterative reconstruction, the updating algorithm is learned from training data using techniques from machine learning such as convolutional neural networks, while still incorporating the image formation model. This typically gives faster and higher quality reconstructions and has been applied to CT [4] and MRI reconstruction. [5]
One group of deep learning reconstruction algorithms apply post-processing neural networks to achieve image-to-image reconstruction, where input images are reconstructed by conventional reconstruction methods. Artifact reduction using the U-Net in limited angle tomography is such an example application. [6]
This is known as motion parallax, and this depth information can be used to generate an accurate 3D representation of the world around them. [2] Finding structure from motion presents a similar problem to finding structure from stereo vision. In both instances, the correspondence between images and the reconstruction of 3D object needs to be found.