National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Radar & Synthetic aperture radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Modified two-dimensional otsu image segmentation algorithm and fast realisation

Abstract: Traditional two-dimensional (2D) Otsu method supposes that the sum of probabilities of diagonal quadrants in 2D histogram is approximately one. This studies experiments and theory prove that the sum of probabilities of off-diagonal quadrants in 2D histogram is not always very small and this could not be neglected. Therefore the assumption mentioned above in 2D Otsu method is inadequately reasonable. In this study, an improved 2D Otsu segmentation method and recursive algorithm are proposed. By calculating probabilities of diagonal quadrants in 2D histogram separately, modified method is acquired. Experimental results show that proposed method can obtain better performance of segmentation than the traditional 2D Otsu method. The computation complexity of improved 2D Otsu method is equal to traditional 2D Otsu method.

PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes

Abstract: We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly. The input to our network is a 3D shape segmented into parts, where each part is first encoded into a feature representation using a part autoencoder. The core component of PQ-NET is a sequence-to-sequence or Seq2Seq autoencoder which encodes a sequence of part features into a latent vector of fixed size, and the decoder reconstructs the 3D shape, one part at a time, resulting in a sequential assembly. The latent space formed by the Seq2Seq encoder encodes both part structure and fine part geometry. The decoder can be adapted to perform several generative tasks including shape autoencoding, interpolation, novel shape generation, and single-view 3D reconstruction, where the generated shapes are all composed of meaningful parts.