Search results
Results from the WOW.Com Content Network
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 [192] 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 [191] [193] O. Parkhi et al. Corel Image Features Data Set
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.
Learning 3D shapes has been a challenging task in computer vision. Recent advances in deep learning have enabled researchers to build models that are able to generate and reconstruct 3D shapes from single or multi-view depth maps or silhouettes seamlessly and efficiently. [24] Automatic inspection, e.g., in manufacturing applications;
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]
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]
Point set registration is the process of aligning two point sets. Here, the blue fish is being registered to the red fish. In computer vision, pattern recognition, and robotics, point-set registration, also known as point-cloud registration or scan matching, is the process of finding a spatial transformation (e.g., scaling, rotation and translation) that aligns two point clouds.
Fashion landmark detection is particularly difficult due to the extreme deformation that can occur in clothing. [9] Some classical methods of feature detection such as scale-invariant feature transform have been used in the past. However, it is now more common to use deep learning methods.