Abstract: An example system includes a global location engine to identify a first plurality of locations of interest in an image. The global location engine is to identify the first plurality of locations based on a first neural network. The system also includes a segmentation engine to generate a regional image based on the image. The system includes a local location engine to identify a second plurality of locations of interest based on the regional image and at least a portion of the first plurality of locations of interest. The local location engine is to identify the second plurality of locations of interest based on a second neural network.

Abstract: According to an example, an apparatus may include a processor and a memory. The memory may have stored thereon machine readable instructions that may cause the processor to access an initial halftone image of a three-dimensional (3D) object, the initial halftone image being generated from a continuous tone image of the 3D object. The instructions may also cause the processor to iteratively modify voxel data for the initial halftone image to identify updated voxel data that more accurately corresponds to the continuous tone image than the initial halftone image and to generate halftone image printing data for the 3D object containing the updated voxel data.

Abstract: According to an example, an apparatus may include a processor and a memory. The memory may have stored thereon machine readable instructions that may cause the processor to access an initial halftone image of a three-dimensional (3D) object, the initial halftone image being generated from a continuous tone image of the 3D object. The instructions may also cause the processor to iteratively modify voxel data for the initial halftone image to identify updated voxel data that more accurately corresponds to the continuous tone image than the initial halftone image and to generate halftone image printing data for the 3D object containing the updated voxel data.