Efficient content adaptive mesh representation of an image using binary space partitions and singular value decomposition
Content adaptive mesh generation is an important research area with many applications in image processing and computer vision. The main issue is to represent an image with the pixels that preserve most of the amount of its information. The obtained pixels are then used to generate a mesh that approximates the original image. This work presents a novel iterative method that simultaneously reduces the number of the pixels and generates the mesh approximation of an image. The main idea is to incorporate binary space partitions along with singular value decomposition to cluster the pixels into planes and thus the nodes of the mesh are nothing but the pixels that define each plane. Compared to previous techniques, the proposed method leads to a 30% reduction in the size of the approximating mesh. In addition, the method minimizes the artifacts obtained from the reconstruction of the original image from the approximating mesh.