Reconstruction of Contour Lines During the Digitization of Contour Maps to Build a Digital Elevation Model
Contour map has contour lines that are significant in building a Digital Elevation Model (DEM). During the digitization and pre-processing of contour maps, the contour line intersects with each other or break apart resulting in broken contour segments. These broken segments impose a greater risk while building DEM leading to a faulty model. In this project, a simple yet efficient mechanism is used to match and reconnect the endpoints of the broken segments accurately and efficiently. The matching of the endpoints is done using the concept of minimum Euclidean distance and gradient direction while the Cubic Hermite spline interpolation technique is used to reconnect the endpoints by estimating the values using a mathematical function that minimizes overall surface curvature resulting in a smooth curve. The purpose of this work is to reconnect the broken contour lines generated during the digitization of the contour map, to help build the most appropriate digital elevation model for the corresponding contour map.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Faithful Contouring: Near-Lossless 3D Voxel Representation Free from Iso-surface
Accurate and efficient voxelized representations of 3D meshes are the foundation of 3D reconstruction and generation. However, existing representations based on iso-surface heavily rely on water-tightening or rendering o…
3D ReconstructionPredictive coding feedback results in perceived illusory contours in a recurrent neural network
Modern feedforward convolutional neural networks (CNNs) can now solve some computer vision tasks at super-human levels. However, these networks only roughly mimic human visual perception. One difference from human vision…
RotaTouille: Rotation Equivariant Deep Learning for Contours
Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning fr…
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation
We introduce SharpNet, a method that predicts an accurate depth map for an input color image, with a particular attention to the reconstruction of occluding contours: Occluding contours are an important cue for object re…
Depth EstimationDepth PredictionMonocular Depth EstimationMonocular Reconstruction+1Smart, Sparse Contours to Represent and Edit Images
We study the problem of reconstructing an image from information stored at contour locations. We show that high-quality reconstructions with high fidelity to the source image can be obtained from sparse input, e.g., comp…
Face RecognitionImage Manipulation