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: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Learning Multi-View Representation With LSTM for 3-D Shape Recognition and Retrieval

Abstract: Shape representation for 3-D models is an important topic in computer vision, multimedia analysis, and computer graphics. Recent multiview-based methods demonstrate promising performance for 3-D shape recognition and retrieval. However, most multiview-based methods ignore the correlations of multiple views or suffer from high computional cost. In this paper, we propose a novel multiview-based network architecture for 3-D shape recognition and retrieval. Our network combines convolutional neural networks (CNNs) with long short-term memory (LSTM) to exploit the correlative information from multiple views. Well-pretrained CNNs with residual connections are first used to extract a low-level feature of each view image rendered from a 3-D shape. Then, a LSTM and a sequence voting layer are employed to aggregate these features into a shape descriptor. The highway network and a three-step training strategy are also adopted to boost the optimization of the deep network. Experimental results on two public datasets demonstrate that the proposed method achieves promising performance for 3-D shape recognition and the state-of-the-art performance for the 3-D shape retrieval.

Classification on the Monogenic Scale Space: Application to Target Recognition in SAR Image

Abstract: This paper introduces a novel classification strategy based on the monogenic scale space for target recognition in Synthetic Aperture Radar (SAR) image. The proposed method exploits monogenic signal theory, a multidimensional generalization of the analytic signal, to capture the characteristics of SAR image, e.g., broad spectral information and simultaneous spatial localization. The components derived from the monogenic signal at different scales are then applied into a recently developed framework, sparse representation-based classification (SRC). Moreover, to deal with the data set, whose target classes are not linearly separable, the classification via kernel combination is proposed, where the multiple components of the monogenic signal are jointly considered into a unifying framework for target recognition. The novelty of this paper comes from: 1) the development of monogenic feature via uniformly downsampling, normalization, and concatenation of the components at various scales; 2) the development of score-level fusion for SRCs; and 3) the development of composite kernel learning for classification. In particular, the comparative experimental studies under nonliteral operating conditions, e.g., structural modifications, random noise corruption, and variations in depression angle, are performed. The comparative experimental studies of various algorithms, including the linear support vector machine and the kernel version, the SRC and the variants, kernel SRC, kernel linear representation, and sparse representation of monogenic signal, are performed too. The feasibility of the proposed method has been successfully verified using Moving and Stationary Target Acquiration and Recognition database. The experimental results demonstrate that significant improvement for recognition accuracy can be achieved by the proposed method in comparison with the baseline algorithms.

An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features

Abstract: This paper presents an effective algorithm for calibrating the extrinsic parameters between a camera and a laser range finder whose trace is invisible. On the basis of an analysis of three possible features, we propose to design a right-angled triangular checkerboard and to employ the invisible intersection points of the laser range finder's slice plane with the edges of the checkerboard to set up the constraints equations. The extrinsic parameters are then calibrated by minimizing the algebraic errors between the measured intersections points and their corresponding projections on the image plane of the camera. We compared our algorithm with the existing methods by both simulations and the real data of a stereo measurement system. The simulation and experimental results confirmed that the proposed algorithm can yield more accurate results.

INS/GPS Integration: Global Observability Analysis

Abstract: Observability is an important aspect of the state-estimation problem in the integration of the inertial navigation system (INS) and the Global Positioning System (GPS) as it determines the existence and nature of solutions. In most previous research, conservative observability concepts, e.g., local observability and linear observability, have extensively been used to locally characterize the estimability properties. In this paper, a novel approach that directly starts from the basic observability definition is used to investigate the global observability of the nonlinear INS/GPS system with consideration of the lever arm uncertainty. A sufficient condition for the global observability of the system is presented. Covariance simulations with an extended Kalman filter (EKF) and a field test are performed to confirm the theoretical results. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights that were unreachable by conventional methods.